Satellite-derived surface upward longwave radiation (SULR) is essential for monitoring the global surface radiation budget, ecological processes and climate change. However, the widely-used SULR products derived from thermal infrared (TIR) remote sensing exhibit spatial discontinuities because TIR signals cannot penetrate cloud cover. Conventional cloud-sky SULR estimation approaches often utilize post-processed reanalysis data as inputs, which could not meet the real-time requirement of operational system. This study proposes a lightweight cloud-sky SULR real-time estimation method for the Fengyun-4A (FY-4A) geostationary satellite using a Light Gradient Boosting Machine (LightGBM) model. The daytime cloud-sky SULR is estimated by applying the established relationship between auxiliary variables and clear-sky SULR to cloudy conditions, while the nighttime cloud-sky SULR values are estimated by applying the determined relationship between input variables and a publicly accessible, gap-filled SULR product. The model inputs include (1) spatial-temporal location record data, (2) multiple surface characteristic parameters generated from previous-year data, and (3) two categories of operational FY-4A radiation products, with both components being available in real-time. Validation against six Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites demonstrates that the reconstructed cloud-sky SULR achieves acceptable RMSE (MBE) values of 33.4 W/m2(1.5 W/m2) for daytime and 25.2 W/m2(4.7 W/m2) for nighttime conditions. Therefore, the proposed lightweight method could improve the spatial coverage of current FY-4A SULR product and further promote real-time SULR-related applications.

Daily mean albedo is a critical variable in surface energy budget and climate change studies. Currently, satellite-based daily mean albedo is typically estimated from the diurnal variation of albedo, derived from multi-angle reflectance observations using a Bidirectional Reflectance Distribution Function (BRDF) kernel-driven model. However, this model assumes flat terrain and neglects topographic effects. This study evaluates the estimation errors of daily mean albedo derived from the BRDF kernel-driven model over rugged terrain. Experiments were conducted for rugged terrains with different mean slopes (10°, 20°, and 30°) and aspects (north and west) at spatial scales of 500 m and 1 km, using large-scale remote sensing data and the image simulation framework (LESS) model. The results demonstrate that topography significantly influences the daily mean albedo derived from the BRDF kernel-driven model, with the largest relative error exceeding 50%. The estimation error increases as the slope of the terrain becomes steeper and is also strongly influenced by the aspect of the terrain. When the solar azimuth angle aligns with the aspect of the rugged terrain, the estimation error becomes particularly pronounced. These findings highlight the necessity of accounting for topographic effects when estimating daily mean albedo.

Thermal radiation directionality (TRD) describes the anisotropic signature in the thermal infrared domain, leading to significant uncertainties in current land surface temperature (LST) and surface upward longwave radiation (SULR) products. The kernel-driven model (KDM) is considered as the most potential tool to correct TRD effects due to its good tradeoff between physical accuracy and computational efficiency. However, the application of existing 4-parameter KDMs is limited due to the requirement of simultaneously ≥4 multi-angle observations. By combining a diurnal temperature cycle model, the time-evolving kernel driven model (TEKDM) had achieved significant TRD elimination effect over the overlapping region of two Geostationary Operational Environmental Satellite (i.e., GOES-16 and GOES-17) LST products. However, the performance of TEKDM in SULR TRD elimination is still not clear. In this letter, the TEKDM was extended to the overlapping region of FengYun-4A (FY-4A) and Himawari-8 satellite observations, in order to correct the directional SULR (SULRD) to hemispherical integrated SULR (SULRH). Then, a step-by-step SULR downscaling method based on multiple linear regression was conducted forSULRDandSULRH(from 4 km to 40 m). The downscaled SULRD and SULRH values were validated by the pyrgeometer-observed hemispherical SULR of 14 in-situ sites within a heterogeneous region. The validation result shows that the TEKDM could eliminate the TRD effect of FY-4A and Himawari-8 SULR with an RMSE decrease of 4.47 W/m2(16.5%) and 4.03 W/m2(15.1%), respectively. Therefore, the TEKDM also performs well for the TRD correction of SULR products of two geostationary satellites.

Radiative transfer models (RTMs) designed to reproduce the anisotropy of surface brightness temperature (BT) are particularly useful for applications on Earth’s energy budget when using remote sensing (RS) datasets. Despite the fact that several thermal infrared (TIR) RTMs have been developed, a quantitative analysis comparing the benefits and limits of these models remains necessary. Herein, three modeling frameworks (physical hybrid, analytical parameterization, and kernel driven) have been evaluated comparatively for homogeneous vegetation, a row-planted crop, and a sparse forest. Airborne measurements and the discrete anisotropy radiative transfer (DART) model simulations were retained as the benchmark. Forward modeling and inverse fitting schemes were proposed for the sake of comparison. Results reveal that: 1) in the forward modeling scheme, from airborne measurements, the hybrid model performs better with root-mean-squared errors (RMSEs) of$0.17~^{\circ }$C,$1.57~^{\circ }$C, and$0.38~^{\circ }$C for homogenous, row-planted vineyard, and sparse forest scenes, respectively; the analytical model appears similar performant ($0.17~^{\circ }$C,$0.40~^{\circ }$C) for the homogeneous and sparse forest scenes, but less performant ($2.39~^{\circ }$C) for the row-planted scene and 2) in the inverse fitting scheme, the uncertainties (95% of probability) of model coefficients and predicted directional anisotropies were considered. The kernel-driven model has fewer modeling constraints and statistically performs better for the homogeneous and sparse forest scenes with RMSEs of$0.07~^{\circ }$C and$0.19~^{\circ }$C, respectively, whereas it is less efficient for the row-planted scene with RMSE of$0.80~^{\circ }$C. This study highlights the differences in accuracy between models of different complexity and provides reference information for researchers to improve existing models and for users to choose their best modeling solution.

The diurnal variation of high spatial resolution albedo is crucial for understanding the energy budget over mountainous areas. Topography significantly affects the diurnal variation of albedo, making its accurate estimation challenging. In this study, we propose a novel algorithm for estimating the diurnal variation of albedo over sloping terrain using high-resolution satellite data. The diurnal variation of albedo is represented as the product of instantaneous albedo at the time of satellite overpass and a diurnal variation factor. Instantaneous albedo is derived from Landsat data and prior BRDF information from the Polarization and Directionality of the Earth’s Reflectances (POLDER) database. The diurnal variation factor is calculated using a fine-scale digital elevation model (DEM) and prior BRDF information, capturing the shape of diurnal variation. Validation against in situ measurements demonstrates the algorithm’s high accuracy ($R^{2} = 0.902$and root-mean-square error (RMSE) = 0.029). In addition, this study examines the differences in the diurnal variation patterns between horizontal/horizontal sloped albedo (HHSA) and inclined/inclined sloping surface albedo (IISA). The results reveal a notable difference between the two: diurnal variation of HHSA is more sensitive to topography, showing a J-shaped pattern, whereas that of IISA consistently follows a U-shaped pattern, better reflecting the sloping surface properties.

Semantic segmentation is a critical process in remote sensing image analysis, supporting various applications. The recent development of the segment anything model (SAM), a visual foundation model designed to segment-anything, highlights the potential of foundational models in computer vision. However, SAM generates segmentation results without category labels, and predictions from semantic segmentation models for remote sensing often exhibit excessive fragmentation and imprecise boundaries. To address these limitations, we propose a strategy that integrates SAM with semantic segmentation models, replacing the imprecise boundaries of remote sensing segmentation masks with the more boundary-accurate SAM masks while retaining the original semantic information. Subsequently, a framework is designed and realized to enhance the prediction results of semantic segmentation models for remote sensing imagery by leveraging the raw outputs generated by SAM. This approach requires no additional training, modification to the semantic segmentation model, or changes to the visual foundation model, making it efficient and straightforward compared with other methods. Specifically, experimental results on two well-known datasets, LoveDA Urban and ISPRS Potsdam, demonstrate the effectiveness and broad applicability of our approach. In addition, incorporating recent visual foundation models, such as SAM-HQ and semantic SAM, further improves segmentation accuracy. As these models advance, the potential of our framework to enhance the performance of semantic segmentation for remote sensing imagery will grow. The source code for this work will be accessible athttps://github.com/qycools/SESSRS.

Characterizing the anisotropic features at the Top of Atmosphere (TOA) is crucial for vegetation monitoring and retrieval of biophysical parameters. The core challenge lies in modeling the mutual interactions between land surface and atmosphere, particularly in the context of rugged terrains and cloudy conditions. The GOSAILTA is proposed to extend the top-of-canopy (TOC) anisotropic reflectance Geometric Optical and mutual shadowing and Scattering-from Arbitrarily-Inclined- Leaves model coupled with Topography (GOSAILT) model to TOA reflectance/radiance by integrating Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model. The interactions between atmosphere and land surface are characterized for the effects of the sloped surface and its surrounding terrains under both clear and cloudy conditions. The model was validated against Discrete Anisotropic Radiative Transfer (DART) simulations, airborne observations from Wideangle Infrared Dual-model line/area Array Scanner (WIDAS), and satellite observations from HJ-1A/B constellation Charge- Coupled Device (CCD). Results demonstrate high overall accuracy in the red band (coefficient of determination (R2) = 0.993; root-mean-square error (RMSE) = 0.008; mean absolute percentage error (MAPE) = 5.481%) and near-infrared (NIR) band (R2 = 0.933, RMSE = 0.025; MAPE = 6.227%) compared to DART simulations. The simulations show strong agreement with WIDAS and HJ, achieving an R² of 0.9. However, the accuracy is slightly lower for top-of-cloud reflectance, with an R² and MAPE of 0.311 and 14.972%, respectively, primarily due to limitations in cloud parameterization.

Land Surface Temperature (LST) estimation relies on precise measurements of surface thermal infrared (TIR) radiance. LST, classified as an essential climate variable (ECV), exhibits rapid temporal variations within specific spatial scales, closely tied to illumination and scan angles. To enhance the utility of LST products, this study proposes a novel approach for concurrently reconstructing the temporal profile and angular dependence. The suggested method, known as the visible-thermal envelope method, integrates kernel-driven (KD) and diurnal temperature cycle (DTC) models, addressing surface structure and thermal factors, respectively. Additionally, this method facilitates LST downscaling by leveraging the higher spatial resolution of visible and near-infrared (VNIR) data, assuming temperature differences are homogeneous within coarse pixels. To validate the reliability of the proposed approach, TIR data from the geostationary satellite Himawari 8 are amalgamated with VNIR data from the polar-orbit satellite Sentinel-3A/3B. When compared to field measurements, the reconstructed results exhibit improvements with a total bias of 0.55 K and Root Mean Square Error (RMSE) of 2.14 K. Notably, in contrast to the original uncorrected results, this correction results in an approximate 50% reduction in bias and a 10% decrease in RMSE.

High-resolution land surface temperature (LST) product holds significant importance in quantifying surface heat, monitoring climate change, assessing environmental health, and water resource management. Therefore, accurate LST retrieval improves our understanding of detailed thermal characteristics of the Earth’s surface. In this research, we developed an operational split-window algorithm for generationg high-resolution LST products from Gaofen5-02 (GF5-02) Visible and Infrared Multispectral Imager (VIMI) data. The coefficients of the split-window algorithm are simulated utilizing the MODTRAN 5.2 atmospheric radiative transfer model, with the global atmospheric profile library of SeeBor V5.0. The land surface emissivities in VIMI bands 11 and 12 are estimated using the ASTER global emissivity dataset (GED) based on the vegetation cover method. The GF5-02 VIMI LSTs are validated using in-situ data collected from the Huailai experiment site in China. Preliminary results show the accuracy of the GF5-02 LST products is satisfactory, exhibiting a bias of -0.03 K and a Root Mean Square Error (RMSE) of 2.42 K.

This study investigated the success rate of the Billion Tree Tsunami Project (BTTP), which was initialized in 2013 by the KPK Government and completed in 2017. Our investigation mainly involves analyzing (i) fluctuations of the green fraction by applying a Land change modeler (LCM), gain-loss, and exchange analysis using a high-resolution GLC30 data set. (ii) Forest cover changes in response to the BTTP Project. (iii)Impacts of Green fraction changes towards Land surface temperature (LST) by utilizing a less explored technique Curve fit linear regression model (CFLR). Our Findings indicate that despite the government's claim of increasing the forest cover by 2%, a significant gain was observed in grassland (3904.87 km2) with an effective transition of bare land. The forest cover increase was only 0.39% in response to BTTP. On the other hand, the CFLRM-based contributions of LCC transition to LST indicate a significant decline in LST. Despite some positive impact on LST because of the green fraction increase, this project cannot be called a complete success due to its inability to hit the prime objective. The government authorities must revisit their strategies for BTTP so that the ongoing "Ten Billion Tsunami Project" will show better results.

Thermal infrared (TIR) observation is the most widely used and accurate method for generating global land surface temperatures (LST) products. However, TIR signals are susceptible to cloud obscuration, and the affected region accounts for more than 50% of the global LST product. We referred to both analytical solution and optimization methods and proposed an all-weather LST reconstruction framework under the cloudy conditions for Chinese FengYun satellites (FY-3D MERSI-II and FY-4A AGRI) based on the principles of radiative transfer and surface energy balance. For FY-3D, the optimization method outperforms the analytical solution method, the overall bias (RMSE) of the estimated all-weather LST is 0.69 K (3.42 K) for the optimization method, while the overall bias (RMSE) of the analytical solution method is 1.33 K (3.52 K). For FY-4D, the validation results are similar for both methods, with a bias (RMSE) of 0.03 K (3.04 K) for the analytical solution method and a bias (RMSE) of -0.24 K (3.03 K) for the optimization method.

Daily mean, maximum, and minimum land surface temperatures (Tmean, Tmax, and Tmin) are fundamental parameters for geophysical studies. Machine learning methods provide powerful tools to estimate these three parameters from instantaneous land surface temperature (LST) observations. This paper evaluated eight machine learning methods for two Moderate Resolution Imaging Spectroradiometer (MODIS) LST products (MxD11A1 and MxD21A1) and their joint application. The preliminary results indicate the support vector regression and linear regression methods exhibit satisfactory performance and fusion of two MODIS LST products is beneficial for estimating Tmeanand Tmax. Notably, the MxD11A1 nighttime LST product performs best for estimating Tmin.

The diurnal variation of land surface albedo (DVLSA) is crucial for understanding energy budgets and climate change. As topography complicates the radiative transfer processes, the estimation of DVLSA over rugged terrain becomes challenging. In this study, the topography-coupled DVLSA model (DVLSA_T) is developed to estimate DVLSA over rugged terrain. DVLSA_T represents DVLSA as a multiplication between the basic albedo and a diurnal variation factor. The basic albedo is the albedo at local noon with topographic effects removed, while the diurnal variation factor extends the albedo from local noon to different times of the day, accounting for topographic effects. Specifically, the diurnal variation factor of black-sky albedo (BSA) changes with the illumination geometry, integrating the topographic effects and U-shaped pattern of DVLSA. In contrast, the diurnal variation factor of white-sky albedo (WSA) is independent of illumination geometry and is solely influenced by topography. DVLSA_T shows good performance when compared with the 3-D radiative transfer simulations by the large-scale remote sensing data and image simulation framework (LESS) (BSA: coefficient of determination (${R}^{2}$) = 0.977; root-mean-square (RMSE) = 0.013; WSA:${R}^{2} =0.982$; and RMSE = 0.012) and sandbox measurements (blue-sky albedo:${R}^{2} = 0.904$and RMSE = 0.012). DVLSA_T also has a good agreement with in situ measurements, with an RMSE of 0.024 and an${R}^{2}$of 0.738. Our results demonstrate that DVLSA_T can effectively characterize DVLSA over rugged terrain.

In this study, a novel algorithm for high spatial resolution leaf area index (LAI) retrieval, specifically tailored for mountain forests, has been developed. As an essential climate variable, LAI has been incorporated into many ecohydrological process simulation models; however, the majority of the algorithms are developed on the assumption of flat terrain. Previous studies have proved that neglecting the influence of topography may introduce significant biases and uncertainties into LAI estimates particularly in rugged areas. As an important species in the mountain area, forests occupy a large land area worldwide; nevertheless, it is still challenging to obtain high-quality LAIs from satellite images due to their complex canopy structures. In spite of numerous attempts having been made to address such issues with topographic correction (TC) or mountain canopy reflectance models, few algorithms were actually available for LAI estimation of mountain forests. Here, we try to employ the geometric optical and mutual shadowing and scattering from the arbitrarily inclined-leaves model coupled with the topography (GOSAILT) model to retrieve forest LAI over complex terrain. GOSAILT is a combined model that incorporates the radiative transfer model (RTM) into the geometrical optical model (GOM) on the slope surface. It is capable of characterizing the bidirectional reflectance of both discrete and continuous canopies. The validations against computer-simulated LAIs reveal root-mean square errors (RMSEs) being 1.7160 and 0.6260, corresponding to terrain-ignored scenario and terrain-considered scenario, respectively. Besides, the validation against in situ LAIs demonstrated that the RMSE is 0.9262 over flat terrain and 0.6402 over sloped terrain. This evidence underscores the robust performance of the newly developed algorithm.

Land surface temperature (LST) is an essential parameter for geoscience studies, and the 1-km polar-orbiting satellite LST products are widely used due to their global coverage on a daily basis. Aimed to jointly utilize all 1-km LST products to thoroughly quantify the LST temporal tendency within one day, comprehensive evaluation of existing 1-km LST products over the same sites is meaningful. In this study, taking sparsely vegetated sites as an example, ten 1-km polar-orbiting LST products were evaluated against the in situ measurements of ten USCRN sites with high spatial representativeness (from December 2019 to November 2020 covering four seasons). The evaluated LST products included three Moderate Resolution Imaging Spectroradiometer (MODIS) LST products (i.e., MOD11, MYD11, and MYD21), two Sea and LST Radiometer (SLSTR) LST products (i.e., Sentinel-3A and Sentinel-3B), one LST product from Advanced Very High Resolution Radiometer (AVHRR) onboard Metop-B, three Visible Infrared Imaging Radiometer Suite (VIIRS) LST products (i.e., VNP21, S-NPP EDR, and NOAA-20 EDR), and one LST product from Visible and Infra-Red Radiometer (VIRR) onboard FY-3B. The results indicate that: 1) the RMSE is much higher in the daytime (2.5–4.0 K) than in the nighttime (1.7–2.5 K); 2) the daytime MBE is negative (from −0.3 to −2.8 K) and this underestimate trend is significantly slighter for nighttime MBE (from −1.7 to 0.2 K); and 3) the daytime RMSE varies from 2.7 to 4.6 K in summer, 2.7–4.5 K in spring, 2.5–3.7 K in winter, and 2.0–3.4 K in autumn. Furthermore, we found that the severe daytime RMSE is related to the thermal anisotropy amplitude, which is around 4 K in summer, 2 K in spring and autumn, and 1 K in winter. Correcting the thermal anisotropy of daytime LST is an essential step that needs to be taken seriously before conducting high-quality joint applications, such as diurnal temperature cycle (DTC) modeling.

The human living environment of cities area has been rapidly improved from the end of the 20th century to the 21st century. The urban thermal environment and local microclimate have been changed and will affect human health and well-being for a long time, particularly in metropolitan areas. Land surface temperature (LST) for urban areas can be obtained from thermal infrared remote sensing observations, enabling the analysis of spatial and temporal variations in urban heat. However, there is very little published research on the modeling and analyzing land surface emissivity (LSE) for urban surfaces. LSE is a prerequisite for some inversion algorithms of LST such as the split-window algorithm, and it is also an important parameter in urban energy balance. Therefore, we proposed an analytical model for the urban LSE by using geometric optical (GO) and spectral invariant (SI) theories. The proposed model was evaluated based on both the synthetic and measured datasets. Results indicated that the simulation performance of proposed model was satisfactory with root mean squared error (RMSE) of approximately 0.004 and 0.009 when compared with datasets from 3D raytracing model and satellite-based emissivity product, respectively.

Land surface temperature (LST) is a vital parameter for studying global ecological, climatic, and environmental changes. A variety of regional and global scale LST products have been produced based on satellite remote sensing. Therefore, reliable retrieval accuracy is crucial for the application of LST products. A ground measurement processing method for medium and high spatial resolution LST products is proposed in this paper, to solve the spatial scale issues between the ground radiometer's field-of-view and the satellite pixel in T-based validation. The method is divided into two steps: LST quality control and spatial scale transformation. The Thermal Airborne Spectrographic Imager (TASI) data was used to evaluate the accuracy of the method. The results showed that this method can improve the reliability of the ground LST measurements.

Optical canopy models that connect land surface properties and satellite-observed radiance must be validated before being used. These models include the bidirectional reflectance distribution function (BRDF) models in the visible and near-infrared domains, and directional brightness temperature (DBT) models in the thermal infrared domain. Near-surface experiments have been extensively conducted to evaluate the modeling accuracy, including ground-, tower-, and aircraft-based measurements. Indeed, it should be noted that in situ measured BRDF/DBT results are sensitive to the experiment protocol, such as sensor moving orientation, flight height, and sampling frequency. A practical tool for optimizing the in situ measurement protocols is needed in the community of remote sensing modeling. For that, we devised a virtual experiment framework based on the discrete anisotropic radiative transfer (DART) 3-D radiative transfer model that is capable of simultaneously simulating both the BRDF/DBT pattern and the images acquired by in situ cameras. Here, as an optimization case, we use it to determine the optimal sensor flight orientation over heterogeneous vegetated canopies (a row-planted scene with three solar angles and a discrete scene with three solar angles) for measuring their DBT distribution. Results showed considerable errors (i.e., image-extracted DBT minus DART-simulated DBT) exist for sensor flight orientation along the canopy rows ($R^{2}$= 0.24 and root mean square error (RMSE) = 4.32 K), and they become much smaller ($R^{2}$= 0.94 ~ 0.98 and RMSE = 0.82 ~ 1.03 K) in other typical orientations (e.g., cross row plane, solar principal plane, and cross solar principal plane). The critical azimuth offset relative to the row direction that can ensure an acceptable RMSE < 1 K is quantified as atan(3*Unitwidth/Scenesize) based on a series of intensive simulations by this new tool. However, the RMSE of the discrete scene is not sensitive to the flight orientation. Such accuracy differences in various protocols were experimentally verified over row-planted maize using a 4-D tower in Huailai, Hebei, China. The result highlights the great potential of this newly designed DART-based virtual experiment to optimize near-surface experiment protocols.

The leaf area index (LAI) is a key structural parameter of vegetation canopies. Accordingly, several moderate-resolution global LAI products have been produced and widely used in the field of remote sensing. However, the accuracy of the current moderate-resolution global LAI products cannot satisfy the requirements recommended by the LAI application communities, especially in heterogeneous areas composed of mixed land cover types. In this study, we propose a mixed-pixel correction (MPC) method to improve the accuracy of LAI retrievals over heterogeneous areas by considering the influence of heterogeneity caused by the mixture of different biome types with the help of high-resolution land cover maps. The DART-simulated LAI, the aggregated Landsat LAI, and the site-based high-resolution LAI reference maps are used to evaluate the performance of the MPC method. The results indicate that the MPC method can reduce the influences of spatial heterogeneity and biome misclassification to obtain the LAI with much better accuracy than the Moderate Resolution Imaging Spectroradiometer (MODIS) main algorithm, given that the high-resolution land cover map is accurate. The root mean square error (RMSE) (bias) decreases from 0.749 (0.486) to 0.414 (0.087), while the R2 increases from 0.084 to 0.524, and the proportion of pixels that fulfill the uncertainty requirement of the GCOS increases from 38.2% to 84.6% for the results of site-based high-resolution LAI reference maps. Spatially explicit information about vegetation fractional cover can further reduce uncertainties induced by variations in canopy density for the results of DART simulated data. The proposed method shows potential for improving global moderate-resolution LAI products.

Land surface temperature (LST) is a vital parameter for studying global ecological, climatic, and environmental changes. Although various LST retrieval algorithms have been proposed, including split-window (SW), dual-window (DW), three-channel (TC), and dual-angle (DA) algorithms, few studies have compared these algorithms using the same satellite observations. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3A provides a unique opportunity to conduct this comparison owing to its dual-angle viewing capability and multiple thermal infrared (TIR) and mid-infrared (MIR) channels. Here, we implemented two SW algorithms, one DW algorithm, two TC algorithms and one DA algorithm for the SLSTR data. The LST retrievals from these six algorithms were validated, along with the SLSTR operational LST product based on an emissivity-implicit SW algorithm. Temperature-based and radiance-based validation methods were used to evaluate different LST retrievals across different land cover types. The results indicated that the proposed SW algorithm had the highest accuracy, followed by the Pérez-Planells SW and the official algorithms. The overall root-mean-square errors (RMSEs) of these three SW algorithms were 1.42 K, 1.79 K and 2.05 K, respectively. The three algorithms involving the MIR channel (one DW and two TC algorithms) were more suitable for nighttime LST retrieval and had similar performances to the three SW algorithms, with a nighttime RMSE of approximately 1.36 K. The LST retrieval accuracy of the DA algorithm had the highest uncertainty and was closely related to the angular variation in surface emissivity and brightness temperature. The findings of this study contribute to a better understanding of the different LST retrieval algorithms and facilitate potential improvements in the official LST retrieval algorithm for SLSTR.

Land surface temperature (LST) is an important parameter that characterizes the energy balance of the land surface, and it is widely used in various research fields. This paper proposes an operational split-window (SW) algorithm for use with the Chinese Fengyun-3 (FY-3) series satellite data, with the purpose of generating long-term global LST products. The algorithm primarily involves three steps. First, the brightness temperatures of the FY-3 Visible and Infra-Red Radiometer (VIRR) were recalibrated using historical recalibration coefficients to improve the accuracy of the absolute radiometric calibration. Second, daily dynamic emissivity maps were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global emissivity dataset (GED) and vegetation/snow cover products based on the vegetation cover method. Finally, the coefficients of the SW algorithm were simulated using MODTRAN 5 combined with the SeeBor V5.0 atmospheric profile library and ASTER spectral library, and then the coefficients were stratified by the view zenith angle and atmospheric water vapor content to improve the fitting accuracy. The proposed SW algorithm was integrated into the MUlti-source data SYnergized Quantitative (MUSYQ) remote sensing production system to then generate FY-3 VIRR LST products. Ten land surface sites from the HiWATER and SURFRAD networks and nine water surface sites from the National Data Buoy Center (NDBC) were used to evaluate the accuracy of the FY-3 VIRR LST products. The results demonstrated that the accuracy of the historical recalibration coefficients of the FY-3A/B VIRR is higher than that of the operational calibration coefficients for LST retrieval. The evaluation results revealed that the FY-3A VIRR LST products (2009-2013) had a bias of 0.13 K and an RMSE of 2.77 K, and the FY-3B VIRR LST products (2011-2020) had a bias of -0.07 K and an RMSE of 2.83 K. These results demonstrate that the proposed operational SW algorithm has reasonable accuracy and can be used to produce global LST products from the FY-3 VIRR data.

Three-dimensional (3D) radiative transfer (RT) models are frequently recognized as a prerequisite when using high spatial resolution remote sensing data in heterogeneous surfaces. However, most studies of 3D RT models have been restricted to limited applications due to the low computational efficiency. Therefore, this study proposed a graphic processing unit (GPU)-based solution for the ray tracing 3D RT model. A state-of-the-art graphics and compute application programming interface, Vulkan, was introduced to implement the RT process. A bounding box method was adopted for the computation acceleration. By comparison with a central processing unit (CPU)-based solution, the performance efficiency of the proposed solution is significantly better: the simulation time of a GPU model is significantly reduced by more than 99% when facing a large-scale simulation mission. The simulation accuracy of the two solutions is similar, with root mean squared errors (RMSEs) lower than 0.005, 0.032 and 0.31 K for the red, near-infrared (NIR) and brightness temperature images, respectively. An evaluation based on airborne multiangle measurements also indicated that the accuracy of the proposed solution was satisfactory for simulating the red and NIR bidirectional reflectance factor and brightness temperature directional anisotropies, with RMSEs lower than 0.003, 0.020 and 0.20 K, respectively, when treating the whole scene as a pixel. Considering the simulation accuracy and efficiency, a GPU-based model will be an important supplement to the CPU model.

Cloud detection is a crucial procedure in remote sensing preprocessing. However, cloud detection is challenging in cloud–snow coexisting areas because cloud and snow have a similar spectral characteristic in visible spectrum. To overcome this challenge, we presented an automatic cloud detection neural network (ACD net) integrated remote sensing imagery with geospatial data and aimed to improve the accuracy of cloud detection from high-resolution imagery under cloud–snow coexistence. The proposed ACD net consisted of two parts: 1) feature extraction networks and 2) cloud boundary refinement module. The feature extraction networks module was designed to extract the spectral–spatial and geographic semantic information of cloud from remote sensing imagery and geospatial data. The cloud boundary refinement module is used to further improve the accuracy of cloud detection. The results showed that the proposed ACD net can provide a reliably cloud detection result in cloud–snow coexistence scene. Compared with the state-of-the-art deep learning algorithms, the proposed ACD net yielded substantially higher overall accuracy of 97.92%. This letter provides a new approach to how remote sensing imagery and geospatial big data can be integrated to obtain high accuracy of cloud detection in the circumstance of cloud–snow coexistence.

Semi-empirical bidirectional reflectance distribution function (BRDF) models are developed based on various spatial-resolution pixels. Because of its simplicity and physical significance, it is widely used in medium- and low-spatial-resolution quantitative remote sensing. With the emergence of high-spatial-resolution remote sensing data and the lack of high-spatial-resolution BRDF models, semi-empirical BRDF models have also been directly applied to high-spatial-resolution qualitative and quantitative remote sensing research. However, whether semi-empirical BRDF models can be directly applied to pixels with high resolution remains unclear. To answer this question, this letter quantitatively evaluates the applicability of semi-empirical BRDF models for remote sensing data with 0.5–30 m spatial resolution based on the WIDAS multi-angular observation dataset obtained during the HiWATER experiment in 2012. The results demonstrate that the semi-empirical BRDF models are not applicable at the 0.5 m pixel scale but are applicable at the 10 m pixel scale. There is a transitional pixel scale from not applicable to applicable between 0.5 and 10 m. We define this scale as the optimal minimum pixel scale (OMS) of semi-empirical BRDF models. The OMS is related to the spatial structure of the vegetation scene, and it is highly consistent with the canopy characteristic scale calculated based on the semivariogram method ($R^{2}=0.901$). Therefore, the range of the semivariogram can be used to estimate the OMS to answer the question of which scale semi-empirical BRDF models are applicable to high-spatial-resolution images.

The estimation of satellite-based albedo highly depends on the surface reflectance (SR). In mountainous areas, three types of SRs [i.e., the virtual SR (VSR) that is retrieved from the atmospheric correction model, the topographically corrected SR (TCSR) that is retrieved from the atmospheric and topographic correction model, and the sloping SR (SSR) that is retrieved from the physically bidirectional reflectance distribution function (BRDF)-based mountain-radiative-transfer (MRT) model] are commonly used to retrieve land surface albedo (SA). However, which type of SR is the best option for SA retrieval has not yet been quantitatively addressed. This letter assessed the performance of these three types of SRs on driving SA by comparison within situalbedo measurements over field sites in the Heihe River Basin, China. Our results show that these three types of albedos have consistent accuracy over flat sites with a root mean squared error (RMSE) smaller than 0.0320. Moreover, the sloping SA (SSA) retrieved from SSR shows the best agreement within situalbedo measurements over rugged sites with a bias of 0.0008, RMSE of 0.0338, relative RMSE (RMSER) of 12.92%, and correlation coefficient ($r$) of 0.89, followed by the topographically corrected SA (TCSA) from TCSR with a lager bias of 0.0208, RMSE of 0.0470, RMSERof 20.24%, and$r$of 0.69. The virtual SA (VSA) retrieved from VSR shows the largest uncertainty than the other two types of albedos, with an RMSE of 0.0516. These results illustrate that SSR is the best option of reflectance for satellite-based albedo retrieval over mountainous areas.

There are five widely used kernel-driven models in the thermal infrared domain designed for the angular correction of land surface temperature (LST), including three-parameter Roujean Lagouarde (RL), Vinnikov, RossThick-LiSparseR (Ross–Li), LiStrahlerFriedl-LiDenseR (LSF-Li), and four-parameter Vinnikov-RoujeanLagouarde (Vinnikov-RL). Their fitting accuracies with hundreds of observation angles (i.e., sufficient angle) were studied; however, the fitting ability of these five models with limited observation angles is unknown, which makes it difficult to choose the appropriate one in applications. To solve this problem, 30 600 groups of multiangle directional brightness temperature (DBT) datasets were simulated by the unified optical-thermal 4-stream model considering scattering by arbitrary inclined leaves (4SAIL) model considering ten different leaf area index values, three leaf inclination distribution functions, two hotspot factors, 17 different component temperatures, five solar zenith angles, and six solar azimuth angles. Each group contains DBT values in 21 960 viewing directions [i.e., 61 viewing zenith angle (VZA)$\times $360 viewing azimuth angle (VAA)]. We assume that all limited observations are in the plane with VAA = 180°/0° and VZA changing from −60° to 60° with a step of 10°. There are 13 candidate angles to be selected. Five, seven, nine, and 11 angle sampling schemes include 225, 400, 225, and 36 limited multiangle combinations, respectively. Each combination was used to drive these five kernel-driven models to fit 21 960 DBTs for 30 600 groups of 4SAIL simulations. The root-mean-square error (RMSE) of each combination and mean RMSE of all 886 combinations were used to assess the overall fitting ability of five kernel-driven models. In addition, 1 k errors were added to the driven DBTs to evaluate the models’ robustness. Four groups of airborne measured DBTs were adopted to validate the assessment conclusions. Results show that the recommended order of these five models driven by 5–11 multiangle DBTs is Vinnikov-RL, LSF-Li, Vinnikov, Ross–Li, and RL when the driven DBTs do not contain errors; Vinnikov-RL, Vinnikov, LSF-Li, Ross–Li, and RL when the driven DBTs contain 1k errors; and Vinnikov-RL, LSF-Li, Ross–Li, RL, and Vinnikov for four groups of airborne measured datasets.

Land surface albedo is a crucial variable of earth energy budget and global climate change. Rugged terrain significantly impacts surface bidirectional reflectance distribution function (BRDF) and the subsequent albedo retrieval using satellite remote sensing. Existing studies of estimating surface BRDF/albedo from satellite observations are limited to neglecting topographic impacts, resulting in large uncertainty in satellite albedo product, especially for low spatial resolution satellite sensors that are primarily regulated by subpixel-scale topographic effects. To fill this knowledge gap, we proposed an extended multisensor combined BRDF inversion (EMCBI) model to characterize subpixel-scale topographic effects, and applied this model to estimate BRDF/albedo from the Himawari-8 Advanced Himawari Imager (AHI) and Terra/Aqua moderate resolution imaging spectroradiometer (MODIS) data and finally validated the satellite-derived albedo with ground measurements of two stations located in Tibet plateau. Our results show that: 1) EMCBI can generate a daily BRDF/albedo dataset with more than 90% spatial coverage and 2) EMCBI-derived albedo agrees well with the referenced albedo corrected from ground measurement, with a root-mean-square-error (RMSE) of 0.0537 and 0.0608 for black-sky albedo (BSA) and white-sky albedo (WSA), and a mean absolute percentage error (MAPE) of 21.93% and 25.13% for BSA and WSA, respectively. These results demonstrate EMCBI has great potential for mapping large-scale high temporal resolution BRDF/albedo product over rugged terrain.

Vegetation index (VI) derived from remotely sensed images is a proxy of terrestrial vegetation information and widely used in land monitoring and global change studies. Recently, the prediction of vegetation properties has been an interest in related communities. With the accumulation of satellite records over the past few decades, the spatial–temporal prediction of VI becomes feasible. In this letter, we developed deep recurrent neural networks (RNNs) with long short-term memory (LSTM) and gated recurrent units (GRUs) to predict the short-term VI based on historical observations. The pixel-based fully connected networks GRU and LSTM (FCGRU and FCLSTM) and patch-based convolutional networks (ConvGRU and ConvLSTM) are established and compared with the traditional multilayer perceptron (MLP) model. Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 normalized difference VI (NDVI) data sets were used in the experiments. The prediction performance is evaluated globally in different regions, different vegetation types, and different growing seasons. Results demonstrate that the RNN models can predict VI with high accuracy (average root mean square error (RMSE) around 0.03), which is superior to the MLP model. In general, the pixel-based RNN models performed better than the patch-based models especially in regions with a larger proportion of outliers. And the prediction accuracy is stable over different vegetation types and growing seasons.

High-accuracy estimates of fractional vegetation cover (FVC) are vital for regional-scale vegetation growth monitoring. In this context, a question worth exploring is whether FVC estimation methods developed at 300-m to 1-km spatial resolution are suitable for finer spatial resolution satellite images. This study compared the performances of three types of algorithms [i.e., the pixel dichotomy model (PDM) based on either the normalized difference vegetation index (NDVI) or an index of near-infrared reflectance of vegetation (NIRv), the gap probability theory (GPT), and linear spectral mixture analysis (LSMA)] based on FVC ground measurements and fine resolution reference maps from the Validation of Land European Remote sensing Instrument (VALERI) project and the ImagineS field campaigns. For all vegetation types, the FVC estimates from the GPT method showed the best consistency with ground measurements of FVC [root mean square error (RMSE) = 0.17 and bias (BIAS) = 0.05]. For forest types, the PDM method based on NDVI also showed satisfactory results with ground measurements (RMSE = 0.17 and BIAS = 0.11). For sparse grasses, the PDM method based on NIRv showed better agreement with ground measurements (RMSE = 0.15 and BIAS = 0.01). This study provides a reference for selecting the method of FVC estimation with fine spatial resolution images.

Aerosol satellite remote sensing retrieval is of great importance in the study of climatic and environmental effects. However, due to varied factors affecting the signals reaching satellite sensors, accurate aerosol retrieval remains challenging. Focusing on limitations in the availability of aerosol data from real scenes, we simulated real scenes to obtain sample data to achieve aerosol retrieval using a deep belief network (DBN). The 6S atmospheric radiative transfer model was used to simulate various possible parameters of earth–atmosphere, sun, and sensor in real scenes. A large amount of simulated data was generated and used as sample datasets of DBN training to obtain the aerosol inversion model. Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to perform aerosol optical thickness (AOT) retrieval experiments. Global-scale aerosol retrieval experiments were conducted based on the following three representative regions: 1) Beijing–Tianjin–Hebei region in China; 2) Midwestern and Southern United States; and 3) Central and Western Europe. The retrieval results were verified using Aerosol Robotic Network (AERONET) datasets in comparison with MCD19A2 aerosol products. Five indicators, including mean absolute error (MAE) and within expected error (${f} _{=\text {EE}}$), were used for the evaluation. The evaluation indicators of the proposed method, in which MAEs were 0.0626, 0.0366, and 0.0487, and${f} _{=\text {EE}}$’s were 86.28%, 85.21%, and 80.71%, performed better than MCD19A2 in three typical regions. The most significant advantage of the proposed method is that high-precision retrieval of spatially continuous AOT can be achieved using single-temporal satellite imagery data, which is not possible realizing in current aerosol retrieval methods.

A comprehensive assessment of satellite-derived albedo products is undeniably essential for better use consideration and the further refinement of the retrieval algorithm. Although satellite albedo products have been extensively validated over spatially homogeneous areas, it remains a challenge to validate them over rugged terrain. Consequently, the accuracy of satellite albedo products over rugged terrain is still unknown. This study for the first time systematically evaluated two widely used satellite albedo products (i.e., MCD43A3 V006 and GLASS V04 albedo) over mountainous areas with a Mountain Radiation Transfer (MRT) coupled multi-scale validation strategy. Fine-scale albedo was first generated with a root mean square error (RMSE) smaller than 0.0317. Then they were upscaled to the coarse pixel and as the reference data for validation. The validation results indicated that the accuracy of the two products tends to decrease with the increase of means slopes. The RMSE and relative RMSE (RMSER) of full retrieval MCD43A3 C6 black-sky albedo (BSA) and white-sky albedo (WSA) over abrupt slopes (mean slope >10°) increase to 0.0432 and 31.87% and to 0.0436 and 32.21%, respectively. The RMSE and RMSERof high quality GLASS V04 were 0.0452 and 33.71% of BSA and 0.0458 and 33.92 % of WSA respectively over abrupt slopes. Particularly, if the backup retrievals were included over the abrupt slopes, the RMSE and RMSER of MCD43A3 C6 can reach to 0.0600 and 36.92% for BSA and 0.0613 and 37.67% for WSA, and those of GLASS V04 can reach to 0.0567 and 36.28% for BSA and 0.0540 and 35.72% respectively.

A comprehensive assessment of satellite-derived albedo products is undeniably essential for better use consideration and the further refinement of the retrieval algorithm. Although satellite albedo products have been extensively validated over spatially homogeneous areas, it remains a challenge to validate them over rugged terrain. Consequently, the accuracy of satellite albedo products over rugged terrain is still unknown. This study for the first time systematically evaluated two widely used satellite albedo products (i.e., MCD43A3 C6 and Global Land Surface Satellite (GLASS) V04 albedo) over mountainous areas with a mountain radiation transfer (MRT) coupled multiscale validation strategy. Fine-scale albedo was first generated with a root-mean-square error (RMSE) smaller than 0.0317. Then, they were upscaled to the coarse pixel and as the reference data for validation. The validation results indicated that the accuracy of the two products tends to decrease with the increase of means slopes. The RMSE and relative RMSE (RMSER) of full retrieval MCD43A3 C6 black-sky albedo (BSA) and white-sky albedo (WSA) over abrupt slopes (mean slope >10°) increase to 0.0432 and 31.87% and to 0.0436 and 32.21%, respectively. The RMSE and RMSERof high-quality GLASS V04 were 0.0452 and 33.71% of BSA and 0.0458 and 33.92% of WSA, respectively, over abrupt slopes. In particular, if the backup retrievals were included over the abrupt slopes, the RMSE and RMSERof MCD43A3 C6 can reach 0.0600 and 36.92% for BSA and 0.0613 and 37.67% for WSA, respectively, and those of GLASS V04 can reach 0.0567 and 36.28% for BSA and 0.0540 and 35.72%, respectively.

Spatiotemporally continuous long-term leaf area index (LAI) products are urgently needed to monitor environmental changes. The current filter- or curve-fitting-based time series reconstructive algorithms fail to reconstruct the LAI time series with many continuous missing values or missing values in key phenological periods, which are common issues in high-spatial-resolution LAI time series. In this article, a meteorological data-driven backpropagation neural network (MBPNN) was proposed to reconstruct discontinuous LAI profiles with a two-step process using vegetation phenological information. As the basis of the strong dependence of vegetation growth on meteorological conditions, a reasonable growth trajectory of reconstructed LAI can be guaranteed by the algorithm even though if many observed values are missing. Validations for reconstructed LAI were conducted both spatially and temporally based on reference maps and field-measured long-term observations. The results showed that the LAI predicted by the MBPNN had a similar accuracy (RMSE = 0.4076) as the Landsat LAI inversions (RMSE = 0.4083) and a similar reconstructed trajectory as the field-measured LAI series even though over 100 days of continuous data were missing (RMSE = 0.1620). A comparison with the Harmonic ANalysis of Time Series (HANTS) algorithm showed that the accuracy of MBPNN was more stable regardless of the size/position of the missing data, and the proposed method performed much better when the data were continuously missing for 50 days or more.

In the thermal infrared domain, there are four widely used 3-parameter kernel-driven models (RL, Vinnikov, Ross-Li and LSF-Li), and four newly proposed 4-parameter kernel-driven models (Vinnikov-RL, Vinnikov-Chen, LSF-RL and LSF-Chen). The fitting abilities of these eight models with limited observations are unknown which makes it difficult to choose the best one in practical applications. In this study, aimed to comprehensively evaluate all eight kernel-driven models, 1700 groups of multi-angle directional brightness temperature (DBT) datasets were simulated by the 4SAIL model considering 10 different leaf area indexes, 1 7 different component temperatures, 2 hotspot factors and 5 solar zenith angles. There are 13 angles from -60° to 60° with a step of 10° in the solar principal plane. Five, seven, nine and eleven angle groups produce 225, 400, 225, and 36 limited-angle combinations, respectively. The total 886 combinations were used to drive these eight kernel-driven models. Then, the fitted DBTs were compared with the 4SAIL simulated DBTs. Results show that LSF - Li always has the least fitting mean RMSE (0.24), followed by Ross-Li (0.33), Vinnikov (0.35), and RL (0.42) for 3-parameter models. The 4-parameter kernel-driven models have the same level accuracy (mean RMSE ≈ 0.10) and are much better than these 3-parameter models.

In this study, we have developed an operational split-window algorithm for retrieving the land surface temperature (LST) from Chinese FengYun-3 (FY-3) series satellite data, with the purpose of generating long-term FY-3 LST products from 2009 to 2020. The refined generalized split-window (GSW) algorithm was selected and the coefficients of the algorithm were simulated using radiative transfer model MODTRAN 5.2 and Seebore v5.0 atmospheric profile database. The land surface emissivities (LSE) in the two SW channels were calculated using the ASTER Global Emissivity Database (GED), vegetation cover product and snow cover product based on the vegetation cover method. The developed FY-3 GSW algorithm was implemented in a MUlti-source data SYnergized Quantitative (MuSyQ) remote sensing product production system. The FY-3A and FY-3B LST products in northwest China were produced for 2013 and 2014, respectively, and the results were evaluated using ground measurements collected in four barren surface sites in the Heihe river basin. Both level 1 and recalibrated VIRR data were used for retrieving the LSTs. The results showed that the historical recalibration coefficients of the VIRR data can improve the accuracy of the LST retrievals.

As a surface component, the tree trunk affects the top-of-canopy (TOC) emissivity and thermal infrared (TIR) radiance over a forest with fewer leaves, which is important for the inversion of land surface temperatures (LSTs) and further applications such as predicting forest fires and monitoring drought conditions. Therefore, the tree trunk effect was analyzed in this article using a thermal radiation directionality model, in which the forest structure was considered by the geometric optical (GO) theory and the spectral invariance theory was introduced into the GO framework for the single-scattering effect between components. The model used was evaluated using unmanned aerial vehicle (UAV)-based measurements with root-mean-square errors (RMSEs) lower than 0.25 °C for directional anisotropies (DAs) of brightness temperatures (BTs). Comparison with a 3-D radiative transfer model, discrete anisotropic radiative transfer (DART), also indicated an acceptable tool of the proposed model for the trunk effect with RMSEs lower than 0.003 °C and 1.2 °C for DAs of emissivity and BTs, respectively. In this study, the root-mean-squared difference (RMSD) levels between the vegetation-soil and vegetation-trunk-soil canopies, which were viewed as an equivalent indicator of the trunk effect, were provided for the TOC emissivity and BTs as well as their DAs, by combination with the changes in the leaf area index (LAI), stand density, trunk shape, and component temperatures, which can help identify the cases in which the trunk effect should be considered. According to a comprehensive analysis, for cases with sparse stand density ( α0.04), the tree trunk should be considered for a BT RMSD level lower than 0.5 °C when the LAI value was lower than 0.6. The corresponding LAI value was 0.8 for an RMSD level of BT DA lower than 0.3 °C. Moreover, for the cases with low soil emissivity, the difference in the TOC emissivity with and without trunk can reach up to 0.035, and the RMSD was still larger than 0.01 when the stand density and LAI were 0.05 and 0.6, respectively.

In this study, two collection 6 (C6) Moderate Resolution Imaging Spectroradiometer (MODIS) level-2 land surface temperature (LST) products (MYD11_L2 and MYD21_L2) from the Aqua satellite were evaluated using temperature-based (T-based) and radiance-based (R-based) validation methods over barren surfaces in Northwestern China. The ground measurements collected at four barren surface sites from June 2012 to September 2018 during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment were used to perform the T-based evaluation. Ten sand dune sites were selected in six large deserts in Northwestern China to carry out an R-based validation from 2012 to 2018. The T-based validation results indicate that the C6 MYD21 LST product has a better accuracy than the C6 MYD11 product during both daytime and nighttime. The LST is underestimated by the C6 MYD11 products at the four T-based sites during the daytime, with a mean bias of -2.82 K and a mean RMSE of 3.82 K, whereas the MYD21 LST product has a mean bias and RMSE of -0.51 and 2.53 K, respectively. The LST is also underestimated at night by the C6 MYD11 products at the four T-based sites, with a mean bias of -1.40 K and a mean RMSE of 1.72 K, whereas the MYD21 LST product has a mean bias and RMSE of 0.23 and 1.01 K, respectively. For the R-based validation, the MYD11 results are associated with large negative biases during both daytime and nighttime at three sand dune sites and biases within 1 K at the other seven sites, whereas the MYD21 results are more consistent at all ten sand dune sites, with a mean bias of 0.45 and 0.70 K for daytime and nighttime, respectively. The emissivities for these two products in MODIS bands 31 and 32 were compared with each other and then compared with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) emissivity and laboratory emissivity. The results indicate that the emissivities in MODIS bands 31 and 32 of MYD11 at the four T-based and three of the R-based validation sites are overestimated and result in LST underestimation, whereas the emissivities of MYD21 are more consistent with the laboratory emissivity. Besides, an experiment was carried out to demonstrate that the physically retrieved dynamic emissivity of the MYD21 product can be utilized to improve the accuracy of the split-window (SW) algorithm for barren surfaces, making it a valuable data source for retrieving LST from different remote sensing data.

The Leaf Area Index (LAI) is a key structure parameter of plant canopy and is a basic input variable in various terrestrial ecological models. Previous studies indicate that the spatial heterogeneity and the mixture of different land cover types in the moderate resolution pixels will cause large errors in the retrieval of moderate resolution LAI product. Therefore, the influence of spatial heterogeneity should be corrected to retrieve a more reasonable LAI. In this study, we propose a method to improve the accuracy of moderate resolution LAI retrievals based on the mixed-pixel clumping index. The data simulated by the LESS model are used to validate the proposed method. Results show that the LAI estimated by the MODIS operational algorithm become smaller with the increase in spatial heterogeneity of pixel. The proposed method can correct the influence of mixed land cover types and spatial heterogeneity to obtain a more reasonable LAI, and thus shows the potential in generating the global moderate resolution LAI product with improved accuracy.

Normalized difference vegetation index (NDVI) is a key parameter in land use/cover change and terrestrial modelling studies. With the accumulation of satellite records in the past few decades, the spatial-temporal prediction of vegetation index becomes feasible. In this paper, we established a convolutional GRU network (ConvGRU) to predict the short-term vegetation index considering the spatial and temporal patterns in the NDVI records. The predictive performance of the proposed method is evaluated for several vegetation types in different regions globally. The results demonstrate that the proposed model has sufficient ability in predicting satellite recorded NDVI. The validation on the MODIS NDVI datasets achieved an average RMSE around 0.06. And, the model performs better in regions of Australia while worse in North Europe.

Leaf chlorophyll content (Chlleaf) is a crucial parameter in carbon cycle modeling and agricultural monitor. Taking advantage of remotely sensed red-edge vegetation index (VI) is an easy approach to estimate Chlleafat a large spatial scale. However, the spectral signals of Chlleafand other canopy/foliar/background factors (e.g. leaf area, leaf angle, soil moisture, etc.) are always coupled together, leading to the relatively low accuracy in direct Chlleafestimation. A new chlorophyll sensitive index (CSI) based on the red-edge band is proposed to estimate Chlleaf, with minimal canopy structural influences. Validation results using in-situ measurements show CSI performed better to estimate Chlleafof winter wheat and soybean at canopy scale: RMSE=8.24μg/cm2for CSI; RMSE=10.04 μg/cm2for the best existing index, MTCI. CSI also has the potential ability to estimate Chlleafacross diverse structural species types with high accuracy. Therefore, CSI provides an effective and convenient way to estimate Chlleafover large areas using satellite data.

High-quality Leaf Area Index (LAI) time-series is important for many ecological applications. Unfortunately, troubles of observations missing and low spatial-temporal resolution greatly restrict their further applications. Due to the increasing of algorithm uncertainty, the current time-series optimized (TSO) algorithms perform poorly when LAI observations are lost continuously or unavailable on the key phenology nodes. It is an effective way of improving performance of TSO by introducing prior knowledge which replenishes time-series detail information. Meteorological data is completely competent since its great potential of describing vegetation growing rules. In this paper, focusing on data missing trouble, we develop a new LAI time-series reconstruction algorithm, called MNNR (Meteorology and Neural Network based Reconstruction), by introducing external meteorological data and other prior information into a neural network model. The results demonstrate that the proposed MNNR algorithm is well capable of spatial-temporal LAI reconstruction and performs excellently when observations are lost continuously.

The objective of this article is a systematic investigation of the sensitivity of C- and X-band emissions to leaf shape and orientation for various growth stages of corn. To simulate these effects, we used the model developed at Tor Vergata University (TOV model), which is based on a matrix doubling algorithm considering multiple scattering. Corn leaves have specific properties of shape, curvature, and orientation. We have compared different approaches, including segmented elliptical disk oriented following leaf curvature, unique elliptical disk per leaf, and segmented circular disk with size determined by the shorter leaf dimension and following the leaf curvature. Moreover, widespread leaf inclination angle distribution functions combined with in situ measurements of leaf inclination angle are adopted. The scatterers' phase matrix calculations are based on the physical optics approximation. Simulations are conducted with the ground-measured soil and vegetation properties as inputs and evaluated against the corresponding ground-based, multifrequency radiometer observations carried out in four different years over Chinese sites. The investigations show that in most cases the segmented circular disk assumption shows the best correspondence to the measurements over intermediate growth stages when the vegetation heights lie between 50 and 200 cm, and the unique elliptical disk model achieves the best correspondence for the later growth stages when the vegetation heights are larger than 200 cm with prefer-erectophile distribution of leaf orientation. The use of in situ leaf inclination angle measurements can improve the model accuracy by up to 25 K for tall vegetation heights compared with random distribution assumption.

The angular and spectral kernel-driven (ASK) model distinguishes soil and vegetation spectral features by the component spectra and is a promising model which combines multisensor data for inversion. However, its global application is limited by the component spectra. This article proposes parameterization of the ASK component spectra of soil and leaf from global spectra libraries as ANGERS, GOSPEL, LOPEX, and USGS. A statistical ratio (y) of various leaf to soil spectra is used to capture their spectral differences and variations, with mean (m) + u (0, ±0.5, ±1) standard deviations (σ) [i.e., y (m + uσ)]. Optimization inversion is applied to determine the ratio candidates y(m + uσ), allowing more tolerance for spectral uncertainty, which releases the semiempirical nature of the kernel-driven model. Simulation data analysis proves its feasibility and good capture of vegetation-soil spectral differences. The model's bidirectional reflectance factor (BRF) fitting error [root-mean-square error (RMSE)] of 0.0245 is slightly larger than the true component spectra of 0.0178, and albedo RMSE is 0.0116 in Black Sky Albedo and 0.0182 in White Sky Albedo. The result also shows its good robustness to the noises, where the====level up to 20% noise conducts a 0.0277 error in BRF fitting and an ignorable influence in albedo. The synergistic-retrieved albedo from multisensor satellite data consists of in situ measurements with an RMSE of 0.0171, compared to 0.0131 from true component spectra retrievals. The new parameterization sacrifices some accuracy, but it is simple and operational for global retrieval with a satisfactory precision.

The availability of global high-resolution land cover maps provides promising a priori knowledge for characterizing subpixel heterogeneity and improving predictions of directional reflectance of coarse-resolution pixels. Due to mutual shadowing and sheltering effects between the adjacent forest and cropland patches, the spectral nonlinear mixing of patchy ecotones is significant, especially when the sun illuminates the ecotone from the forest side with high solar zenith angle. The spectral linear mixture (SLM) approach leads to overestimation of the bidirectional reflectance factor (BRF) in the red band in the principal plane (PP), with a maximum absolute error (MAE) of 0.0063 and a maximum relative error (MRE) of 52.5%, and to underestimation in the near-infrared band in PP with an MAE of 0.0940 and an MRE of 14.5%. In a scenario with randomly distributed boundary orientations, the overestimation of SLM increases with the degree of fragmentation and the view zenith angle. We propose a Radiative Transfer model for patchy ECotones (RTEC). which improves R2from 0.61 to 0.94 in the red band of Landsat-8 directional reflectance at the validation site. The RTEC model provides an efficient and analytical approach for directional reflectance predictions over heterogeneous patchy landscapes at coarse resolution and will be used for biophysical parameter retrievals [e.g., the leaf area index (LAI)] in future applications.

Understory vegetation has largely affected the accuracy of forest leaf area index (LAI) estimation. This study analyzed the components of overstorey and understory based on the forest reflectance and transmittance (FRT) model for six forest types in China: i.e. a planted fir forest, a mixed fir and pine forest, deciduous broad-leaved forest, evergreen broad-leaved forest, broad-leaved pinus koraiensis forest, and seasonal tropical rainforest. The contributions of understory were varied for different forest types within a year. The contributions of understory for the tropical rainforest and the coniferous and broad-leaved mixed forest were low due to the dense canopy with higher LAI of overstorey. The effect of understory was unneglectable for the whole wavelengths.

Because of noises from land surface temperature (LST) inversion and different spatial resolution between nadir and oblique views, we proposed a combined algorithm for soil and vegetation temperatures with Sea and Land Surface Temperature Radiometer (SLSTR) data, in which a Bayesian strategy was adopted and retrieval results from a multi-pixel algorithm were selected as a priori information in a multi-angle algorithm. The SLSTR LST that inverted by a general split-window algorithm was evaluated first to understand inversion noise. Then, the combined algorithm was evaluated using a synthetic dataset, which displayed a robust performance than the multiangle and the multi-pixel algorithm.

There are four widely-used kernel driven models in the thermal infrared domain, including LSF-Li, RL, Ross-Li and Vinnikov. They have great potentials to achieve the angular effect correction for land surface temperature products. RL and Ross-Li are direct extensions of kernel models in the visible and near infrared region and other two models were directly designed for the thermal infrared region. Their fitting abilities should be evaluated comprehensively at first. Some evaluation works have been reported based on simulated datasets or measured dataset with limited view angles. In this paper, two measured multi-angle datasets with over 104directions were selected to do evaluation. Both of them were extracted from the airborne measurement using spatial-temporal average method. We found that LSF-Li is always the best one, followed by Ross-Li, RL and Vinnikov.

In this paper, in order to produce long term fully global land surface temperature (LST) product, the generalized split-window (GSW) algorithm and dual-window (DW) algorithm was used to retrieve LST from different geostationary (GEO) satellite data, including the FY-2E/4A, MTSAT-2/Himawari-8, MSG2, and GOES13/15. First, the coefficients of the GSW and DW algorithm were obtained from a simulation database constructed using the MODTRAN 5.2 and the SeeBor V5.0 atmospheric profile database. Second, the emissivity was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity dataset (GED). Finally, the LST results of FY-4A AGRI and Himawari-8 AHI were retrieved and cross-validated. The results show that the LST algorithms developed in this work are capable of generating accurate high temporal resolution LST retrieval from global GEO satellite data.

In this study, we present an operational algorithm to retrieve the land surface temperature (LST) from MODIS thermal infrared data using physically retrieved emissivity product. This algorithm was implemented in the MUlti-source data SYnergized Quantitative (MuSyQ) remote sensing product system. First, the emissivity in the MODIS two split-window channels was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity dataset. Then, the LST was retrieved using a modified generalized split-window algorithm. The MuSyQ MODIS LST product and the C6 MxD11 LST product were evaluated using ground measurements collected from four barren surface sites in Northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment. The evaluation results indicate that the MuSyQ LST products provide better accuracy than the C6 MxD11 product during both daytime and nighttime at all four sites. This study demonstrates that physically retrieved emissivity products are a useful source for LST retrieval over barren surfaces.

The effects of Thermal Radiation Directionality (TRD) were originally evidenced through experiments in 1962, showing that two sensors simultaneously measuring temperature of the same scene may get significantly different values when the viewing geometry is different. Many models were developed to simulate the TRD effect aimed at mimicking the observed thermal infrared radiance with consideration of canopy structure, component emissivities and temperatures, and Earth surface energy exchange processes. In this paper, the models of vegetation canopies are classified into five groups. The basic assumption, and the disadvantages/advantages of them are summarized. Then, the recent progresses on the TRD modeling for vegetation canopy are given.

The accurate mapping of forest AGB using remote sensing dataset is hindered by the saturation problem and the terrain effects. Direct measurement of forest spatial structures and terrains should be the solutions of these problems. However, the information of forest vertical structure and ground surface terrain are always mixed together in remote sensing datasets which can directly measure the elevations of ground objects. One potential way is to separate them is to synthesize InSAR, stereo imagery and lidar waveform. Theoretical model is needed for this effort. In this study, a unified model was presented, which can be used to simulate InSAR, stereo imagery and lidar waveforms over mountainous forest landscapes.

Topography significantly complicates the radiative transfer process and further to influence the parameter inversion by remote sensing approach. Neglecting the topographic effects may lead to large uncertainties when estimating LAI (Leaf area index) over rugged terrain. In this study, the topographic effects are quantitatively investigated and analyzed based on the DART (discrete anisotropic radiative transfer) simulations and ANN (artificial neural network) -based LAI inversion approach. And the influence factors on LAI inversion is analyzed. The results reveal that the topography can account for more than 50% uncertainties of LAI and may result in not invertible cases. the topographic effects on LAI cannot be neglected in the inversion process.

This letter presents an experiment to explore the nonisothermal effects on temperature and emissivity separation (TES). The innovation of this experiment lies in its design, which highlights the contrast between isothermal and nonisothermal conditions in emissivity measurements. We artificially created a sharply contrasting nonisothermal soil surface using liquid nitrogen cooling and solar heating. The iterative spectrally smooth TES (ISSTES) algorithm was used to process the experimental data. The analyzed results of the experimental data show that the nonisothermal conditions have a significant effect on TES. The bias of retrieved emissivity increases with the component temperature difference as well as with wavelength. The bias around the split window band can reach up to 1% when the difference of the component temperature is 40K. Considering that 1% error in emissivity can cause approximately 1K error of retrieved land surface temperature (LST), the nonisothermal effects on emissivity cannot be ignored. We hope that this experiment will arouse attention of the nonisothermal effects on TES and call for more efforts to be devoted to this issue in the future.

The directional anisotropy of canopy emissivity and brightness temperature in the thermal infrared band has widely been studied. However, the contribution of different scattering orders has been an open scientific question for many years. The recently proposed CE-P model enables us to analytically evaluate the different scattering orders. Herein, we derive expressions for the first double collisions (DCE12) and first triple collisions (DCE123). Our result shows that DCE123can simulate the observed emissivity with an error less than 0.001 and that DCE12is reasonably accurate when leaf emissivity is greater than 0.96. Numerical analysis shows that the contribution of quadruple or greater collisions can be ignored when the leaf (soil) emissivity is no less than 0.90. Furthermore, we develop the CBT-P model to simulate the directional brightness temperature (DBT) based on the new optimized CE-P model (DCE123) and validate it by 4SAIL (4-Stream Radiative Transfer Theory of Scattering by Arbitrary Inclined Leaves) and DART (Discrete Anisotropic Radiative Transfer) models. Both of isothermal (soil temperature is equal to leaf temperature) and nonisothermal (soil temperature is higher than leaf temperature) cases are considered. The maximum differences between the CBT-P model and 4SAIL (DART) are less than 0.35 K (0.42 K), the average differences between CBT-P and 4SAIL (DART) are less than 0.10 K (0.13 K), and the R2is over 0.99 (0.95) with component emissivities larger than 0.90 and the difference between soil and leaf temperatures less than 20 K. The directional anisotropy of DBT is dominated by the zero-scattering and the single scattering terms according to the new developed CBT-P model.

In[1], the units of sky view factor and terrain view factor are printed incorrectly in the abstract section andFig. 4.

Many physical models have been proposed to simulate the directional anisotropy in the thermal infrared (TIR) region over vegetation canopies to produce angular corrected directional brightness temperature or land surface temperature. However, too many input parameters obstruct their operational use. Semiempirical kernel-driven models are designed to be a tradeoff between physical accuracy and operationality. Recently, four kernel-driven models have been proposed: the first two are direct extensions of kernel models in the visible- and near-infrared region and the last two were directly designed for the TIR region. In this paper, 153 continuous and 153 discrete canopies with varying structures and temperature distributions were considered in order to evaluate their accuracies against two physical models (4SAIL and DART). Their error distribution, scatterplots, and directional anisotropy patterns are compared. LSF-Li model, followed by Ross-Li, Vinnikov, and RL model, gave the best fitting results for all the scenes. The R2of all four kernel models can reach up to 0.82 for discrete scenes; however, the kernel-driven models underestimate the hotspot effect from continuous scenes; therefore, further improvements are necessary for operational use with future TIR satellite missions.

To advance the utility of polarimetric synthetic aperture radar observations for extracting biophysical information about vegetation canopies, it is important to develop good understanding of the scattering mechanisms that give rise to the polarimetric scattering response and to apply it toward the development of effective decomposition models of the polarimetric covariance matrices. In this paper, we introduce a more rigorous approach to characterizing the volume scattering component of the three-component scattering model developed by Freeman and Durden. The improved rigor has two aspects: 1) the use of a T-matrix model for computing the polarimetric scattering by an inclined dielectric cylinder and 2) the incorporation of the change in local incidence angle as a function of the orientation of the vegetation cylinders. The improvements address some of the current limitations of the Freeman-Durden model. This paper also provides a sensitivity analysis of the various components of the covariance matrix as a function of several physical parameters including cylinder size, its dielectric constant, and the orientation distribution of cylinders. Such an analysis is a precursor to the development of improved inversion algorithms.

Land surface temperature and emissivity (LST&E) are key variables for a wide variety of surface-atmosphere studies, and it is very important to validate the accuracy of different LST&E retrieval algorithms. In this paper, the accuracy of the Advanced Spaceborne Thermal Emission and Reflection (ASTER) temperature emissivity separation (TES) algorithm with the water vapor scaling (WVS) method and the ASTER standard LSE&E products (without WVS) was validated using 12 ASTER scenes from May 2012 to September 2012 with concurrent ground LST and emissivity measurements collected in an arid area in northwest China during the Heihe Watershed Allied Telemetry Experimental Research experiment. Both the National Center for Environmental Prediction (NCEP) and MOD07 atmospheric profile products were employed to perform the atmospheric correction. The results showed that the WVSTES LSTs retrieved from the two profiles both demonstrate good accuracies, with average biases of 0.34 and 0.24 K and average root-mean-square errors (RMSEs) of 1.52 and 1.46 K for the NCEP and MOD07 profiles, respectively. When the WVS was not applied, we obtained an average bias of 1.26 K and an average RMSE of 2.05 K for the AST08 product. The emissivities from the WVSTES algorithm with the two profiles both showed good agreements with the ground CE312 measurements, with mean differences of the five ASTER bands that were less than 0.01. AST05 showed anomalous emissivity spectra and underestimated the emissivity values of graybody surfaces when compared with the ground data, with mean differences of greater than 0.015, which were more obvious for cases with high water vapor. This paper demonstrated that the WVS method is critical for retrieving ASTER TES LST with accuracies within 1.5 K and emissivity within 0.015 for a wide range of atmospheric conditions and land surface types.

In this study, to improve the accuracy of land surface temperature (LST) products over barren surfaces, we present an operational algorithm to retrieve the LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) thermal infrared data using physically retrieved emissivity products. The LST algorithm involved two steps. First, the emissivity in the two MODIS split-window (SW) channels was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity data set. Then, the LST was retrieved using a modified generalized SW algorithm. This algorithm was implemented in the MUlti-source data SYnergized Quantitative (MuSyQ) remote sensing product system. The MuSyQ MODIS LST product and the Collection 6 MODIS LST product (MxD11_L2) were compared and validated using ground measurements collected from four barren surface sites in Northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment from June 2012 to December 2015. In total, 2268 and 2715 clear-sky samples were used in the validation for Terra and Aqua, respectively. The evaluation results indicate that the MuSyQ LST products provide better accuracy than the C6 MxD11 product during both daytime and nighttime at all four sites. For the daytime results, the LST is underestimated by the C6 MxD11 products at all four sites, with a mean bias of -1.78 and -2.86 K and a mean root-mean-square error (RMSE) of 3.16 and 3.94 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are within 1 K, with a mean bias of -0.26 and -1.03 K and a mean RMSE of 2.45 and 2.71 K for Terra and Aqua, respectively. For the nighttime results, the LST is also underestimated by the C6 MxD11 products at all four sites, with a mean bias of -1.60 and -1.26 K and a mean RMSE of 1.93 and 1.60 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are 0.16 and 0.58 K and the mean RMSEs are 1.12 and 1.25 K for Terra and Aqua, respectively. The results indicate that the underestimation of the C6 MxD11 LST product at all four sites mainly results from the overestimation of the emissivities in MODIS bands 31 and 32. This study demonstrates that physically retrieved emissivity products are a useful source for LST retrieval over barren surfaces and can be used to improve the accuracy of global LST products.

Topographic effects on canopy reflectance play a pivotal role in the retrieval of surface biophysical variables over rugged terrain. In this paper, we proposed a new canopy anisotropic reflectance model for discrete forests, Geometric Optical and Mutual Shadowing and Scattering-from-Arbitrarily-Inclined-Leaves model coupled with Topography (GOSAILT), which considers the effects of slope, aspect, geotropic nature of tree growth, multiple scattering, and diffuse skylight. GOSAILT-simulated areal proportions of four scene components (i.e., sunlit crown, shaded crown, sunlit background, and shaded background) were evaluated using the Geometric Optical model for Sloping Terrains (GOST) model. The canopy reflectances simulated by GOSAILT were validated against two reflectance data sets: Discrete anisotropic radiative transfer (DART) simulations and wide-angle infrared dual-model line/area array scanner (WIDAS) observations. Compared with a horizontal surface, the forest canopy reflectance over a steep slope (60°) is significantly distorted with absolute (relative) bias values of 0.048 (79.60%) and 0.056 (12.02%) for the red and near-infrared (NIR) bands, respectively. The GOSAILT-simulated component areal proportions show close agreements with GOST. Moreover, GOSAILT simulations have high overall accuracy (red band: coefficient of determination (R2) = 0.96; root-mean-square error (RMSE) = 0.003; and mean absolute percentage error (MAPE) = 3.91%; and NIR band: R2= 0.78, RMSE = 0.019; MAPE = 3.94%) when compared with the DART simulations. These extensive validations indicate good performances of GOSAILT in canopy reflectance simulations over sloped surfaces.

In this study, two latest Collection 6 (C6) MODIS level-2 LST products (MxD11_L2 and MxD21_L2) from both the Terra and Aqua satellites were evaluated against ground measurements collected in an arid area of northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment. Ground measurements of four and a half years at four barren surface sites were used to carry out the evaluation, which took place from June 2012 to December 2016. The results show that the C6 MxD21 LST products demonstrate a better accuracy than C6 MxD11 LSTproducts both at daytime and nighttime for the four sites. For the daytime result, C6 MxD11 products underestimate the LST at the four barren surface sites, with an average bias of -1.77 K for Terra and -2.63 K for Aqua, while the average biases of MxD21 LST products are much smaller, with an average bias of 0.34 K for Terra and -0.67 K for Aqua, respectively. For the nighttime result, C6 MxD11 products also underestimate the LST at the four sites, with an average bias of -1.41 K for Terra and -1.01 K for Aqua, while the average biases of K LST products are 0.14 K for Terra and 0.54 K for Aqua, respectively.

In the paper, a geometric optical model is proposed for a vegetation-trunk-soil scene. The effect of tree trunks was analyzed by comparing directional brightness temperatures (BTs) between vegetation-soil and vegetation-trunk-soil scenes. The comparison result reveals the tree trunk can cause directional BTs as a whole lower because of its shadow and shaded area. Therefore, the tree trunk should be considered when retrieving temperatures from thermal infrared observations over a leafless forest. Efforts using measured TIR data requires to be done in the future.

Multi-angle information extraction is very important for the validation of land surface models, such as bi-directional reflectance distribution function (BRDF) model and directional brightness temperature (DBT) model. The experiment can be done in the near surface, on the plane, and on the satellite. The spatial-temporal average method is widely-used on the scale of plane. In this paper, we try to extend it to extract the multi-angle information from near surface observation sensors. We found this method can obtain good result over homogeneous land surface, but the observation protocol is critical over heterogeneous land surface. For instance, the observation plane should be perpendicular to the row direction. In addition, we found that the hot spot area will be shaded by the observation platform which leads to the BRDF result to be affected seriously.

A new directional canopy emissivity model (CE-P) based on spectral invariants can separate the multiple scattering effect and single scattering in vegetation canopy. So we can further evaluate the contribution of scattering effect to the canopy emissivity and brightness temperature based on CE-P model. Numerical analysis shows that the contribution of more than three times scattering can be ignored when the leaf (soil) emissivity is no less than 0.90. Then, we optimize CE-P model and obtain the expressions containing the first twice collisions (ε2) and first three times collisions (ε3). The result shows that ε3 can simulate the emissivity in any case with an error less than 0.001. Furthermore, we simulate the brightness temperature distribution using the optimized model and compare it with DART model. The difference between them is less than 0.3K and the R2of them is over 0.96 in all of the selected samples.

Light use efficiency model is one of the methods to retrieval regional scale Gross Primary Productivity (GPP). In this study, GPP of the Heihe River Basin in China from 2011 to 2015 were inversed by light use efficiency model. In this kind of the models, Photosynthetic Active Radiation (PAR) and the fraction of absorbed photosynthetically-active radiation (FPAR) were the key parameters. Usually, PAR and FPAR were inversed without distinguishing direct and diffuse radiation. However, several researches show that due to the stronger transmission of diffuse radiation in the canopy and avoidance of the light-saturation phenomenon of direct radiation in the canopy leaves, photosynthesis is more efficient under the action of diffuse radiation. So in this article PAR and FPAR were inversed by models which can distinguish direct and diffuse radiation. And the retrieved daily GPP results were validated by field measurements from flux sites of GPP.

Modeling plays an important role to link the land surface physical/chemical properties with remotely sensed data. In the past decade of years, Multi-scale Remote Sensing Models have been developed (Chen, J. M. et. al. 1997, Huang, H. et. al. 2013). In Recent years, remote sensing communities tend to consider the complex terrain scenario more reasonable recently, such as mixed pixel, topography issues and so on. This paper present the newest progresses on how to and quantitatively measure the heterogeneity of the land surface. Spatial heterogeneity exists in the land surface at every scale, and it is one of the key factors that introduces inherent uncertainty into simulations of land surface radiative processes and parameter retrieval based on remotely sensed data. However, because of the lack of understanding of the heterogeneous characteristics of global mixed pixels, few studies have focused on modeling and inversion algorithms in heterogeneous areas. This paper presents a parameterization scheme to quantitatively describe pixel heterogeneity based on end member and boundary information and high -resolution land cover products (Li, X et. at 2011), which are used to characterize and quantify global land surface heterogeneity. Then, the recent BRDF modelling progresses for two typical mixed pixels are introduced.

High resolution thermal infrared (TIR) remote sensing data are widely used in many fields, such as environmental protection, land resources, urban construction and other scientific applications. This article presents a high resolution thermal infrared satellite concept to meet above requirements. The satellite can operate in a sun synchronous orbit, and provide high spatial resolution data (~5m) with a 2-day revisit in 19 spectral bands. The payload of the satellite can obtain radiance data which are subsequently processed into measurements of atmospheric and surface properties, such as land surface temperature (LST), water surface temperature, evapotranspiration, column water vapor, material composition and others. For users with urgent needs, remote sensing data of the payload can be processed by utilizing the Intelligent Onboard Data Processing (IODP) technology, so that event information is obtained onboard. Through the direct data downlink, event information is transmitted to ground stations immediately, so application users can quickly respond. In addition, because of the small swath of the high resolution satellite, the payload must be installed on an agile platform, which can effectively enlarge swath by multi-stripes splicing imaging. In general, this article provides an overview of the technical approach and application direction of high resolution thermal infrared satellites, which can be used as a reference for satellite applications and engineering development.

Because of climatic change and human activities, sandification is becoming a serious threat to the sustainability of human habitation. The aim of this study, therefore, was to propose a method for sandy land detection based on mixed pixel decomposition; the dynamic change monitoring and assessment was then conducted. Results showed that the pixel purity index is a viable indicator for endmember extraction for sandy land detection via remote sensing by linear mixed pixel decomposition methods. Results showed that when the endmember proportion of sandy land accounted for > 50% of the total (except for the vegetation), a pixel would be detected as sandy land. The extraction accuracy was verified to be 86.42% by field data. Early-middle August was believed to be the most reasonable time to assess sandy land coverage based on vegetation coverage. The sandy land areas in 2005 and 2014 were 5524 km2and 4109 km2respectively, reduced by 25.6%. Under the governance of sandy land in the last ten years, the sandy land area declined continually, but some areas were still degraded to a worse status and need special care to protect.

In this paper, we proposed a temperature and emissivity separation (TES) algorithm for the simultaneous retrieval of land surface temperature and emissivity (LST&E) from the thermal infrared data of Chinese GaoFen-5 (GF-5) satellite's Multiple Spectral-Imager (MSI) payload. In order to improve the accuracy of the TES algorithm, a water vapor scaling (WVS) method for atmospheric correction was adopted. The Seebor V5.0 global atmospheric profile database and MODTRAN 5 were used to simulate the WVS coefficients. A total of 11 ASTER scenes were used to simulate the MSI images and concurrent ground measurements acquired in the HiW ATER experiment were used to validate the algorithm. The results showed that the bias and root mean square error (RMSE) in the retrieved LST were 0.47 K and 1.70 K, respectively, and the absolute emissivity differences between MSI and the ground measurements were smaller than 0.01 for the four MSI TIR bands, which demonstrated that the proposed algorithm can be used to retrieve high accurate and high spatial resolution LST&E from GF-5 MSI data.

Global leaf area index (LAI) generally produced based on the satellite sensors with 1 km spatial resolution, such as the advanced very high resolution radiometer (AVHRR), moderate resolution imaging spectroradiometer (MODIS) and VEGETATION. At present, there isn't a LAI product estimated from the Chinese Feng Yun No.3 (FY-3) images. This study aims to generate a 10-day composite LAI product from FY-3A with a medium resolution spectral imaging (MERSI) at global scale in 2011. Making use of the land cover type as priori knowledge, the LAI for pure vegetation types was inversed from a lookup-table (LUT) based on an stochastic three-dimensional radiative transfer model (3D RTM). For the mixed water and vegetation types, LAI was inversed based on an improved linear decomposition method. The accuracy of LAI inversion from FY-3A MERSI was assessed by LAI field measurements from the Chinese ecosystem research network (CERN) in 2011.

Downward surface solar radiation (DSSR) plays an important role in the earth's surface energy budget. However, it has significant spatial-temporal heterogeneity over the rugged terrain. To accurately capture DSSR, many analytical terrain parameter algorithms based on digital elevation models (DEMs) have been proposed. However, the uncertainties of the DSSR components associated with these algorithms remain unclear. In this letter, we compared three types of terrain parameter algorithms and their respective DSSR component uncertainties at different spatial scales by using 3-D discrete anisotropic radiative model simulations under different atmospheric conditions. The comparison results indicated that differences in slopes, sky view factors, and terrain view factors can be up to 4°, 0.165°, and 0.264°, respectively. For a high atmospheric visibility, the maximum discrepancies of direct solar irradiance and adjacent terrain-reflected irradiance over the high reflective surface (e.g., fresh snow and ice) are 26.7 and 42.8 W·m2, respectively. In addition, for a low atmospheric visibility, a maximum difference of 31 W·m2is identified for diffuse skylight. These uncertainties are nonnegligible when using a high-resolution DEM (e.g., 30 m), but as the DEM resolution becomes coarser, the uncertainties decrease.

A new directional canopy emissivity model (CE-P) based on spectral invariants is proposed in this paper. First, we prove the existence of the spectral invariant properties in the thermal infrared (TIR) band using a Monte Carlo model. Based on it, the equation of the new model is derived from the perspective of absorption. In this expression, single-scattering and multiscattering effects are separated analytically in the TIR band. We find that the overall contribution of multiple scatterings is less than 0.005 when the component emissivities are over 0.90, and the overall contribution decreases with increasing leaf or soil emissivity. Furthermore, the new model can avoid the logical difficulty encountered when using the traditional cavity effect factor to simulate the emissivity of a sparse vegetation canopy. The results of 4SAIL and Discrete Anisotropic Radiative Transfer (DART) are selected to do cross validation. The CE-P can achieve a high accuracy compared with 4SAIL and DART, with an absolute bias less than 0.002 when the leaf (soil) emissivity is equal to 0.98 (0.94). Four widely used analytical models are selected for comparison. The resulting accuracies of these models are ordered from CE-P to REN15, FR97, FR02, and VALOR96 with the most serious error up to 0.002, 0.002, 0.007, 0.013, and 0.014, respectively. Three main conclusions are obtained through the sensitivity analysis: the multiscattering between vegetation and the background can be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), the second and higher order scattering within the vegetation can also be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), and the single-scattering effect within the canopy should be considered which can be calculated using three view factors.

Soil semiempirical dielectric models (SEMs) are powerful, and they are generally considered a useful hybrid of both empirical and physical models. In this paper, the Wang-Schmugge dielectric model is improved to more accurately estimate the relative complex dielectric constants (CDCs) of moist soils. Instead of the Debye relaxation spectrum of liquid water located outside of the soil (i.e., free out-of-soil water) adopted in the Wang-Schmugge model, the Debye relaxation formula related to the free-water component inside the soil [i.e., free soil water (FSW)], which is correlated with the soil texture, is employed in the improved SEM. In addition, the effective conductivity loss term related to both soil texture and soil moisture is introduced to explain the ionic conductivity losses of FSW. Since the soil moisture influence is reduced at high frequencies, the effective conductivity loss term related to only the soil texture is also analyzed for 14-18 GHz. As in the Wang-Schmugge model, the relative CDC of bound soil water varies with the soil volumetric moisture content when the soil moisture is lower than the maximum bound water fraction in the new model, which takes a different approach than the Mironov mineralogy-based SEM. The proposed model obtains better fitting results than the three most widely employed SEMs. The improved model exhibits a significantly improved accuracy with a higher correlation coefficient (R2), a closer 1:1 relationship, and a lower root-mean-square error, including in the L-band, and especially in the imaginary part of the L-band.

High-quality interferometric synthetic aperture radar (InSAR) data have become more available in recent years with InSAR missions such as the TanDEM-X. Theoretical coherent backscattering models of forest canopies at landscape scales are necessary for understanding the relationship between positions of scattering phase center extracted from InSAR data and spatial structures of forest stands growing on complex mountainous terrain. Unlike most existing scattering models of forest canopies that focus on the prediction of the scattering behavior within a pixel, a new model, referred to as LandSAR, was developed to fully account for compound effects of forest spatial structure, terrain, and geometrical distortion caused by slant range imaging on the scattering phase center. The LandSAR model was validated over a mountainous forest scene (about 8.8 km by 9.5 km) imaged by an airborne laser scanner. The interferogram, flattened interferogram, coherence, unwrapped phase, and digital surface model were successfully extracted from simulated InSAR data that have been processed as real InSAR data. The effects of wavelength, baseline length, land cover, and terrain features on decorrelation of simulated InSAR data were consistent with theoretical expectations. Both the height of the scattering phase center and the penetration depth were strongly correlated with forest heights. These results demonstrated that the LandSAR successfully modeled the InSAR features of forest canopies over a mountainous area at landscape scales.

Canopy radiation and scattering signal contains abundant vegetation information. One can quantitatively retrieve the biophysical parameters by building canopy radiation and scattering models and inverting them. Joint simulation of the three-dimensional (3D) models for multiband may combine the advantages of different spectral (frequency) domains. It is also a useful tool for validation in remote sesnsing because of the high precision of the 3D model. This manuscript presented a 3D joint simulation platform which was able to simulate the remote sensing responses of the 3D scene from visible to microwave bands. As a case study, we used the platform to simulate a virtual scene with populus, and did a sensitivity analysis on the radiation and scattering responses of different wavelengths to leaf area index (LAI). The application of the joint simulation platform was prospected.

While EVI and LAI are the most widely used vegetation parameters which can be used for remote sensing phenology extraction, this paper aims at assessing the differences of phenology information extracted from EVI and LAI time series and exploring either EVI or LAI time series performs well for all vegetation types over a large scale. To achieve this, GLASS-LAI phenology product(GLP) was generated by the same algorithm with MODIS-EVI phenology product(MLCD) over China from 2001 to 2012. The two phenology products were compared in different climate regions and vegetation types over a large scale and evaluated by ground observations. Results show that the missing rate of GLP(11.90%) is less than that of MLCD(22.84%). The difference between GPL and MLCD varies in different climate regions and vegetation types. GLP performs better than MLCD in croplands and forests, while MLCD performs better than GLP in grasslands.

In order to assess atmospheric effects on the directional reflectance of land surface, we have developed a new approach coupling the 3-D radiosity-based land-surface model [radiosity applicable to porous individual objects (RAPID)] with the atmospheric radiative transfer (RT) model [vector linearized discrete ordinate RT (VLIDORT)]. RAPID is used to generate a lookup table of bidirectional reflectance distribution function (BRDF) elements required by VLIDORT for the surface boundary condition. To test the RAPID-VLIDORT model, we used five natural 3-D scenes along with five aerosol optical depths (AODs). Results for top-of-atmosphere radiances show semiempirical analytical BRDF models are insufficiently accurate to represent bidirectional reflectance factors (BRFs) in hotspot regions and over wide angular variations. The large impact of AOD on BRF hotspot also underlines the importance of precise atmospheric corrections for multiangular remote sensing of the earth's surface.

Land surface temperature (LST) is often needed for using remotely sensed data to study the surface energy budget and hydrological cycle. However, LST is challenging to measure and simulate because of its high sensitivity to atmospheric instability and solar angle, particularly over large-scale heterogeneous scenes. We propose a model that combines radiosity theory and an energy budget method for surface temperatures; we also explore the anisotropic behavior of row-planted crop emissions. The surface thermodynamic equilibrium state is fulfilled via the interaction between the 3-D radiative transfer calculations of the thermal-region radiosity-graphics combined model and the energy balance equation. Despite its shortcomings, such as the time-consuming calculations, the proposed model is feasible according to the results of an intercomparison and validation analysis. The intercomparison shows that the model exhibits similar performance, in terms of surface temperature calculations, to that of the soil-canopy observation, photochemistry and energy balance model (root-mean-square differences) of 0.59 °C and 1.77 °C for the leaf and soil components, respectively. Excellent agreement with the observed directional variation over summer maize canopies is also obtained, with R2values exceeding 0.6 and a mean RMSE of 0.32 °C. Thus, we recommend the new combined model as an option for explaining directional anisotropy due to its potential application to 3-D scenes.

Surface upward longwave radiation (SULR) is a significant component of the surface radiation budget and is closely linked with evapotranspiration, soil moisture, and surface cooling on clear nights. Therefore, accurately estimating SULR is essential to better understand its spatiotemporal dynamics or to characterize the thermal environment of a given land surface. Currently, most methods for estimating SULR (including the physical and hybrid methods) fail to account for the thermal anisotropy, which can introduce significant errors into the calculation. We previously proposed the combined algorithm that considers the thermal anisotropy to more accurately estimate the SULR. However, this proposed method has several shortcomings. For example, it considers the directionality of the emissivity and the effective temperature separately under the support of a parametric directional emissivity model. However, the directional emissivity model is not maturely developed for different land surface types, especially on non-vegetated surfaces. And the separation of land surface temperature and emissivity may undermine the estimation accuracy. Furthermore, this proposed method requires a series of input parameters that is not always available, limiting its applicability. In this paper, we present a refined algorithm that uses a kernel-driven model and the technique of band conversion to calculate the SULR directly based on surface-leaving radiances. This direct physical algorithm is then applied to the Wide-angle infrared Dual-mode line/area Array Scanner data set and validated using longwave radiation data collected by automatic meteorological stations from the Heihe Watershed Allied Telemetry Experimental Research experiment. The results of these tests suggest that the direct algorithm works effectively. The root-mean-square error (RMSE) and mean bias error (MBE) of the direct algorithm on maize surfaces are 4.417 and 0.474 W · m-2, respectively. When the thermal anisotropy is incorporated, the RMSE and absolute MBE decrease by a maximum of 4.734 and 7.414 W·m-2, respectively. Different land types yield different results: for vegetable surfaces, the estimation biases of the direct model are approximately -2 W · m-2, whereas orchard surfaces yield biases are between -2 and -3.5 W · m-2, and village surfaces yield biases exceeding -10 W · m-2. These differences can be attributed to the varying effects of the kernel-driven model across different types of land surfaces. The RMSE and absolute MBE obtained using the direct algorithm are slightly smaller (0.587 and 1.685 W·m-2, respectively) than those obtained using the combined algorithm; they are also smaller than the results of the traditional temperature-emissivity algorithm (by 8.7 and 11.7 W · m-2, respectively).

In this paper, the land surface bidirectional reflectance distribution function (BRDF) and albedo on a small timescale are retrieved by the multisensor combined BRDF inversion (MCBI) model with improved accuracy. The accumulation period for this BRDF/albedo retrieval is shortened to 8 and 4 days with data from four satellite sensors, the Moderate Resolution Imaging Spectraradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), Visible Infrared Imaging Radiometer (VIIRS), and Medium Resolution Spectral Imager (MERSI), to obtain the dynamic features of land surfaces. All the four sensors have high revisit frequencies and dense angular sampling. The MCBI model provides an algorithm to form a virtual MODIS observation network with these four sensors, resulting in a multiband and multiangle sampling reflectance data set. It also provides a multisensor reflectance quality control index, the net information index (NII), for a robust BRDF/albedo retrieval. The performance of the MCBI is assessed by comparisons with MODIS BRDF/albedo product and the in situ measurement. The results show that the highly frequent angular sampling with four sensors allows for a full retrieval of BRDF/albedo with a shorter accumulation period of 8 and 4 days. The NII reduces the uncertainties when using different sensors' reflectance and allows for a high-quality BRDF/albedo retrieval. It reveals that the MCBI has the potential to generate a multisensor-based BRDF/albedo on a small timescale. The MCBI is a key algorithm for the BRDF/albedo product in China's multisource data synergized quantitative remote sensing production system and operationally implemented to generate a global product.

Figure 2 shows the observed gap fraction and gap size distribution along with the sampling line. According to the algorithms of LAIcc, LAIlxand LAIpl, the results are obtained using the observed gap fraction and gap size distribution. respectively. Although the difference between Ωee and Ωlx is only smaller than 0.1. the difference between LAIccand LAIlxcan reach up to 0.36. Path length algorithm docs not use Ω as middle result and calculated LAI directly. However. LAIplclose to LAIe and the difference between the true LAI and LAIplis reach up to 1.

A stripmap-mode raw data generator of an extended scene taking into consideration trajectory deviation with a non-zero squint angle is proposed. The approach utilizes a new approximation for trajectory deviations in the conical reference system. It relies on one-dimensional azimuth Fourier domain processing followed by range time-domain integration. Several simulation results are finally presented in order to demonstrate the effectiveness of the proposed approach.

In this study, a single-channel parametric model (SC-PM) algorithm were used to produce 300m LST product from HJ-1B IRS data. The NCEP atmospheric profiles and a parametric model were used for atmospheric correction. In order to improve the accuracy of the land surface emissivity (LSE), the 1km ASTER Global Emissivity Dataset (GED) and self-developed 5-day 1km vegetation cover product were used for estimating the LSE based on the Vegetation Cover Method. Two years of HJ-1B IRS LST product in Heihe River basin (Gansu province, China) from June 2012 to June 2014 were generated. The LST products were evaluated against ground observations collected during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment. Four barren surface sites and ten vegetated sites were chosen for the evaluation. The results show that the produced HJ-1B IRS LST products demonstrate a good accuracy, with an average bias of 0.10 K and an average root mean square error (RMSE) of 2.43 K for all the sites during daytime. In addition, the biases are within 1K for the four barren surface sites. This indicate that using ASTER GED can produce reliable LST products from HJ-1B IRS data, especially for the barren surfaces.

The state of the land surface and the water cycle over the South and East Asia can be determined by space observation. New or significantly improved algorithms have been developed and evaluated against ground measurements. Variables retrieved include land surface properties, i.e. NDVI, LAI, FPAR, albedo, soil moisture, glacier and lake levels. Based on these biophysical parameters derived from microwave and optical remote sensing observations, a hybrid remotely sensed evapotranspiration (ET) estimation model named ETMonitor was developed and applied to estimate the daily actual ET of the Southeast Asia at a spatial resolution of 1 km. The changes in glaciers and lakes on the Tibetan Plateau, and the drainage links between glaciers and lakes are determined in this climate-sensitive region.

Land surface temperature (LST) is a key parameter for a wide number of applications, which include hydrology, meteorology and model validation. In this paper a physical single channel algorithm was developed for retrieving LST from the Landsat 8 TIRS data. ASTER Global Emissivity Dataset (GED) and Vegetation Cover Method (VCM) were chosen to improve the accuracy of land surface emissivity and the fast radiative transfer model RTTOV was utilized for atmospheric correction which uses MERRA reanalysis data as inputs. The algorithm is evaluated by the ground measurements collected from in situ sites during the HiWATER experiment. The LST result shows a dynamical variation with the phenological changes and the average Bias and RMSE of the estimated LST for all sites after remove outliers are 0.09K and 2.20K, respectively. This indicates that the algorithm is suitable for producing LST product from Landsat 8 TIRS data and ASTER GED can be used to improve the accuracy of land surface emissivity in arid and semi-arid area.

Vegetation fraction is an important index for sandy land evaluation, but it changes with time and precipitation obviously, especially the annual ephemeral plants. How to choose the best time for vegetation coverage to evaluate the sandy land degree is an imperative question. In this paper, soil adjusted vegetation index (SAVI), pixel dichotomy model and pixel unmixed model for multi-endmumber were applied to estimate the vegetation fraction based on GF-1 multispectral image, taking Zhenglan Banner in Inner Mongolia as a study area. By validated from filed data, the pixel unmixed model for multi-endmumber was superior to the others, with the lowest RMSE. The vegetation fraction change was analyzed in growing seasons from May to October, taking the precipitation into consideration. It was shown that vegetation in sandy land grown steadily by the middle of July, but it appeared to grow much rapidly after this period due to the effect of precipitation on annual ephemeral plants. In the later period, the plants began to be restrained and became placid until August because of temperature reduction and lack of water. In order to reduce the unsteady effect of annual ephemeral plant, early-middle August was thought as the most reasonable time for vegetation coverage to evaluate the sandy land degree.

Leaf area index (LAI) is a critical parameter in many terrestrial ecosystem models. Continuous LAI measurements from global sites are an important dataset for the validation of remotely sensed LAI products. However, the spatial scale mismatch between the site measurement and the product pixel grid hinders the utilization of multi-temporal ground LAI measurements. In this study, a pragmatic method is presented for spatial upscaling of ground LAI measurements to the product pixel grid. The method is divided into three parts: retrieving high-resolution LAI maps, spatial representativeness grading and spatial upscaling. The proposed method was applied to the Järvselja site in the VALERI project. Results show that this method can reduce the scale mismatch error between the site measurement and the product pixel grid well. Moreover, this method has the potential to be applied to global site LAI measurements, which consequently can improve the reliability of LAI product validation.

Mix-scene of forest with the combination of different land cover types, such as grass, or crop, in one pixel is common, especially for the low resolution SAR images. Various radar models have been developed through years. However, the heterogeneous effect of mix-scene is not well understood quantitative so far, which might cause substantial uncertainty in the soil moisture and biomass estimations. We investigated this effect by using a coherence 3D vegetation radar model, which is firstly parametrized and validated using the ground measurements and airborne P band SAR data at pine forest of Metolius, Oregon, USA, 2013-2014. The airborne SAR data and simulated data are in good agreement with mean difference less than 1dB and correlation coefficient above 0.96. Secondly, Simulation of several mix-scenes of the same area, tree density and height but difference tree clumping patterns and mixed with different land cover types were performed. The largest difference can up to 3dB of extreme tree clumping patterns. This study demonstrates how important it is to consider the horizontal forest heterogeneity of mix-scene. Further study with quantitative description of horizontal heterogeneity of mix land cover and the effect to soil moisture and biomass estimation are needed.

There has been growing interest in the investigation of vegetation using polarimetric remote sensing techniques. During the past several decades, a number of theoretical models have been proposed to study the scattering mechanisms in the vegetation medium and are very useful for forest stand or short crops [1]-[4].

To obtain downward longwave radiation (DLR) product at high resolution and good quality, a method of calculating global DLR was proposed. Global geostationary and polar-orbiting satellite observations were used to cover the whole world, and several DLR algorithms and two atmospheric datasets were combined used to satisfy different conditions. The global DLR at 5 km was produced using Multi-source data Synergized Quantitative remote sensing production system (MuSQ). The MuSQ DLR was validated using field measurement around the world, and it had acceptable results at most sites. The uncertainty of DLR product was probably from the uncertainties of cloud detection and atmospheric parameters, and the limitation of DLR algorithms. The DLR product was compared with other radiation products, and it had much better or comparable results to current products.

Landscape heterogeneity is a common natural phenomenon but is seldom considered in current radiative transfer models for predicting the surface reflectance. This paper developed an analytical Radiative Transfer model for heterogeneous Agro-Forestry scenes (RTAF). The scattering contribution of the non-boundary regions can be estimated from the SAILH model as homogeneous canopies, whereas that of the boundary regions is calculated based on the bidirectional gap probability by considering the interactions and mutual shadowing effects among different patches. The multi-angular airborne observations and Discrete Anisotropic Radiative Transfer (DART) model simulations were used to validate and evaluate the RTAF model over an agro-forestry scene in Heihe River Basin, China. The results suggest the RTAF model can accurately simulate the hemispherica-directional reflectance factors (HDRFs) of the heterogeneous scenes in the red and near-infrared (NIR) bands. The boundary effect can significantly influence the angular distribution of the HDRFs and consequently enlarge the HDRF variations between the backward and forward directions. Compared with the widely used dominant cover type (DCT) and spectral linear mixture (SLM) models, the RTAF model reduced the maximum relative error from 25.7% (SLM) and 23.0% (DCT) to 9.8% in the red band, and from 19.6% (DCT) and 13.7% (SLM) to 8.7% in the NIR band. The RTAF model provides a promising way to improve the retrieval of biophysical parameters (e.g. leaf area index) from remote sensing data over heterogeneous agro-forestry scenes.

The environment of Greater Mekong Sub-region (GMS) was highly payed attention to its economic development. Remote sensing technology was a useful tool for global and regional environment monitor. The fractional vegetation cover (FVC) with 30m spatial resolution for GMS was extracted from the HJ-1/CCD data in this study. The vegetation covers were highly for the entire GMS, and the spatial differences were influenced by vegetation types. Besides, FVC product with high temporal resolution (5 days) and 1km spatial resolution were used to analyze the vegetation damages by typhoon Haiyan from 2edto 10thNov., 2013 based on a change detection method. The damage extents of forest by typhoon were much seriously than cropland and grassland for GMS memberships, especially for Vietnam and China (Guangxi). The vegetation damages varied from -50% to 10% in the 300 km suffer areas on the typhoon Haiyan pathway.

Land surface component temperatures are important inputs in longwave radiation and evapotranspiration estimation models. Most component temperature inversion approaches focus only on two components, namely, soil and leaves, because space-based multiangle observations are lacking. This approach is inconsistent with ground-based measurements, which suggest that the temperatures of sunlit and shaded soil may significantly differ. This paper explores a three-component temperature inversion scheme that uses airborne multiangle thermal infrared observations to decrease the difference between the retrieved data and the actual subpixel temperature distribution. The FR97 model, which is an analytical directional brightness temperature model that was modified by dividing the soil component into sunlit and shaded portions, is adopted to calculate the matrix of component effective emissivity, which links multiangular observations and component temperatures. The new forward model and the inversion scheme are assessed using simulated data sets from the Scattering by Arbitrarily Inclined Leaves (4SAIL) model. The results indicate that the modified FR97 model provides good precision and that the inversion scheme based on the modified FR97 model is appropriate because of the model's simplicity and accuracy and the inversion's low sensitivity to noise. The inversion scheme is validated using airborne data collected by the wide-angle infrared dual-mode line/area array scanner over an area planted with maize and ground measurements collected during the Heihe Watershed Allied Telemetry Experimental Research campaign. The results indicate that the root mean square errors of the component temperatures of the leaves, sunlit soil, and shaded soil were 0.72 °C, 1.55 °C, and 2.73 °C, respectively. Because of the modified FR97's straightforward form and acceptable precision, we recommend this new retrieval scheme as an option for retrieving the component temperatures of leaves, sunlit soil, and shaded soil.

Surface upward longwave radiation (SULR) is an important component of the surface energy balance and is closely related to land surface temperature and emissivity. The estimation of SULR plays an important role in the study of surface energy circulation and climate change. State-of-the-art methods to estimate SULR, including the physical method and the hybrid method, are conducted without considering directional thermal radiation (DTR), which may induce a large error in the estimation, particularly over sparsely vegetated surfaces. In this paper, we modified the physical temperature–emissivity algorithm by combining a directional emissivity model (FRA97) and a kernel-driven DTR model to estimate the SULR of vegetated surfaces while considering the thermal directionality of the land surface. The most suitable kernel-driven model and an angle combination of the DTR were selected from six kernel-driven models and five angular combinations. The sensitivity of the proposed algorithm to the input parameters was also analyzed. The proposed algorithm was then validated with the Wide-angle infrared Dual-mode line/area Array Scanner (WiDAS) data set and longwave radiation data of automatic meteorological stations from the Heihe Watershed Allied Telemetry Experimental Research experiment. The results showed that the five-angle combination with large-angle intervals performs the best. When the leaf area index (LAI) is less than 1.2, the RossThick-LiSparseR model performs the best; when LAI is larger than 1.2, the RossThick-LiDenseR model is the most accurate. The SULR is not sensitive to surface downward longwave radiation and LAI, is slightly sensitive to leaf and soil emissivity at certain LAIs, and is highly sensitive to DTR, which may greatly affect the accuracy of the estimated SULR. The root-mean-square error (RMSE) and the mean bias error (MBE) of the SULR estimated using the WiDAS data and the proposed algorithm are 5.618 and −1.642 W/m2, respectively, thereby improving the estimation accuracy by as much as 7.479 and 10.511 W/m2at most in terms of RMSE and MBE, respectively, compared with the results calculated without considering the DTR.

The electromagnetic scattering by a dielectric cylinder of finite length is important in many applications, particularly for microwave remote sensing of vegetated terrain. Yet, not only a unified analytical solution is still elusive in providing the scattering cross sections but also other important aspects, such as the phase of the scattering amplitude function, energy conservation, and reciprocity relation, have been scantly touched in the literature. The treatment of a dielectric inhomogeneous cylinder of finite length brings forth new challenges. Taking on such challenges is the focus of this paper. The main plan of attack is to extend the virtual partition method, which is a T-matrix-based semi-analytical model, that we have previously proposed to treat scattering from a homogeneous dielectric cylinder of finite length, to the inhomogeneous cases. The effectiveness of the proposed method is validated numerically, including: 1) high-fidelity prediction of the copolarized and cross-polarized cross sections for arbitrary bistatic scattering configuration; 2) high-fidelity predictions of the phase of the scattering amplitude function; 3) verification of energy conservation; and 4) verification of the reciprocity theorem.

Current bidirectional reflectance distribution function (BRDF) inversions using ordinary least squares (OLS) criterion can be easily contaminated by observations with residual cloud and undetected high aerosols, which leads to abrupt fluctuations in the normalized difference vegetation index (NDVI) time series. The OLS criterion assumes the noise has Gaussian distribution, which is often violated due to positive noise biases caused by clouds and high aerosols. A changing-weight iterative BRDF/NDVI inversion algorithm (CWI) based on a posteriori variance estimation of observation errors is presented to explicitly consider the asymmetrically distributed noise and observations with unequal accuracy in the BRDF retrieval. CWI employs a posteriori variance estimation and an NDVI-based indicator to iteratively adjust the weight of each observation according to its noise level. The validation results suggest CWI performs better than the Li-Gao and OLS approaches. The rmse was reduced from 0.074 to 0.028, and the relative error decreased from 13.4% to 3.8% at the U.S. Department of Agriculture Beltsville Agricultural Research Center site. Similarly, at the Harvard Forest site, the rmse was reduced from 0.086 to 0.031, and the relative error decreased from 9.5% to 2.7%. The average noise and relative noise of the CWI NDVI time series over ten EOS Land Validation Core Sites from 2003-2009 was smaller (0.028, 3.7%) than those of MOD13A2 (0.041, 5.2%), MYD13A2 (0.039, 4.9%) and MCD43B4 (0.030, 4.4%). The results demonstrate the robustness of the CWI approach in suppressing the influence of contaminated observations in BRDF retrievals by producing results that are less affected by undetected clouds and high aerosols.

Landscape heterogeneity is a common natural phenomenon but is seldom considered in current radiative transfer (RT) models for predicting the surface reflectance. This paper developed an analytical RT model for heterogeneous Agro-Forestry scenarios (RTAF) by dividing the scenario into nonboundary regions (NRs) and boundary regions (BRs). The scattering contribution of the NRs can be estimated from the scattering-by-arbitrarily-inclined-leaves-with-the-hot-spot-effect model as homogeneous canopies, whereas that of the BRs is calculated based on the bidirectional gap probability by considering the interactions and mutual shadowing effects among different patches. The multiangular airborne observations and discrete-anisotropic-RT model simulations were used to validate and evaluate the RTAF model over an agro-forestry scenario in the Heihe River Basin, China. The results suggest that the RTAF model can accurately simulate the hemispherical-directional reflectance factors (HDRFs) of the heterogeneous scenarios in the red and near-infrared (NIR) bands. The boundary effect can significantly influence the angular distribution of the HDRFs and consequently enlarge the HDRF variations between the backward and forward directions. Compared with the widely used dominant cover type (DCT) and spectral linear mixture (SLM) models, the RTAF model reduced the maximum relative error from 25.7% (SLM) and 23.0% (DCT) to 9.8% in the red band and from 19.6% (DCT) and 13.7% (SLM) to 8.7% in the NIR band. The RTAF model provides a promising way to improve the retrieval of biophysical parameters (e.g., leaf area index) from remote sensing data over heterogeneous agro-forestry scenarios.

The dielectric properties of vegetation reflect the coupling between the electromagnetic and physical properties of the material. Understanding dielectric behavior is essential for remote sensing applications. The traditional approach of measuring broadleaf permittivity involves exerting pressure on stacked leaves, which not only requires a complicated system to measure the pressure but also introduces a degree of uncertainty by changing the internal leaf microstructure. We propose a measurement technique that avoids the aforementioned shortcomings by using a vacuum packaging machine to remove the air from a sample bag, which is the first contribution of this paper. The proposed technique is validated against corn, cotton, and soybean leaf permittivity measurements. The second contribution of this paper involves how temperature is treated when modeling permittivity. The Debye-Cole dual-dispersion model, which is influential in vegetation remote sensing applications, is almost exclusively applied at 22 °C. However, it is assumed that this model can be extrapolated to other temperatures. Our analysis indicates that this model may overestimate permittivity at 28 °C, with mean relative errors (MREs) of up to 22% and 37% for relative permittivity and dielectric loss. Thus, we adopt the structure of the model due to its soundness and modify the model parameters. Overall, we used four different methods, among which the optimal method reduced the MRE by no more than -2.5%.

This study analyzed the relationship between the spatial heterogeneity of land surface temperature (LST) and the spatial scale using the Thermal Airborne Hyperspectral Imager (TASI) and satellite-based Advanced Spaceborne Thermal Emission Reflection (ASTER) data. The spatial heterogeneity of LST was quantified using variogram modeling in univariate and multivariate method. The results show that in both methods, the spatial heterogeneity of LST in a landscape as quantified by the dispersion variance increases with the spatial scale until the scale is larger than the characteristic scale, and the land cover types have significant influences on the spatial heterogeneity of LST. Additionally, the spatial heterogeneity of the land surface decreases obviously as the wavelength increases in the multivariate model.

We compare five slope correction methods developed by Walter et al., Montes et al., Schleppi et al., España et al., and Gonsamo et al. (referred to as WAL, MON, SCH, ESP, and GON, respectively) using artificial fisheye pictures simulated by graphics software and a lookup table (LUT) retrieval method. The LUT is built by simulating the directional gap fraction as a function of leaf area index (LAI) and average leaf inclination angle (ALIA) using the Poisson law. LAI and ALIA estimates correspond to the case of the LUT that provides the lowest root-mean-square error between the observed gap fractions after slope correction and the simulated ones. Three LAI values (1.5, 3.5, and 5.5), four ALIA values (26.8°, 45°, 57.5°, and 63.2°), and three slope angles (0°, 20°, and 50°) constituted 36 samples of random scenes. ESP is recommended because its results are accurate and independent on the leaf angle distribution (LAD), while GON only performs well for spherical LAD. The three other methods present less good performances with underestimation or overestimation of LAI and/or ALIA depending on the LAD, and the recommended order for them is MON, SCH, and WAL.

A new geometric optical model is proposed in this letter to simulate the directional brightness temperature (DBT) distribution over mixed scenes of continuous crop and road. The DBT distributions of the crop and road zones are separately calculated, and the road zone consists of a road and adjacent crop sides. A road distribution polar map is designed to show all of the roads of different lengths, widths, and orientations in the scene. The airborne multiangle data set of the thermal infrared band that was acquired during the Heihe Watershed Allied Telemetry Experimental Research experiment is used for validation. The results demonstrate that the proposed model can simulate the DBT of a heterogeneous scene$(90\times 90\ \hbox{m}^{2})$with a root-mean-square error equal to 1.1 K and good trend similarity.

This study investigates the effects of soil moisture (SM) on land surface emissivity (LSE) using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LSE data acquired in Heihe Watershed Allied Telemetry Experimental Research (HiWATER). Three bare surface sites with automatic meteorological stations that collected long-term SM data were chosen to evaluate the SM impact. The ASTER LSE retrieval was performed using the water vapor scaling method to improve the atmospheric correction results, and the validation results indicate that the emissivity uncertainties are better than 1%. The multitemporal LSE data reveal that there is an increase in the emissivity with increasing SM. A logarithmic linear relationship was established to describe the broadband emissivity dependence with SM over each site, with determination coefficients of 0.9429, 0.7705, and 0.4603. The modeled values calculated using coefficients derived in previous studies for samples with similar compositions yielded good agreements with ASTER broadband emissivities over two sites. The empirical model also shows that the diurnal variation in emissivity, particularly over one site, is so significant that it should not be neglected.

Spatial heterogeneity of land surface induces scaling bias in leaf area index (LAI) products. In optical remote sensing of vegetation, spatial heterogeneity arises both by textural and contextual effects. A case study made in the middle reach of the Heihe River Basin shows that the scaling bias in LAI retrieval is large up to 26% if the spatial heterogeneity within low-resolution pixels is ignored. To reduce the influence of spatial heterogeneity on LA! products, a correcting method combining both textural and contextual information is adopted, and the scaling bias may decrease to less than 2% in producing resolution-invariant LAI products.

A unique method for investigating continental surfaces uses backscatter data measured by a radar altimeter at the nadir point of a satellite, in contrast to other microwave sensors designed to work at oblique angles, such as scatterometers and synthetic aperture radar. To improve the altimetry resolution over land, we generated 0.5° × 0.5° merged altimetry backscatter maps covering 66° N to 66° S over the global land surface every six days for the period from January 2002 to June 2009 by combining three altimeter data sets (Jason1, Envisat, Jason2) in the Ku-band. The four backscatter products of Envisat RA2 from different retracking algorithms were evaluated prior to merging with the Jason1 and Jason2 data. The global pattern and the seasonal variation of the merged altimetry backscatter were examined, which show the merged results have better spatial sampling for the regional to global geophysical process due to the combination of three altimeters. To understand how the altimetry backscatter is related to land surface parameters, the advanced integral equation model for bare soil and water cloud model for vegetation are used to simulate the Ku-band backscatter response to soil and vegetation parameters at an incidence of 0°. Furthermore, we compared the time series of merged altimetry backscatter with the leaf area index (LAI) determined by an optical sensor and the backscatter coefficients obtained from a scatterometer (QuikSCAT) over seven selected vegetated areas over six years. The results confirm the sensitivity of ocean altimetry to vegetation. Further studies relating altimetry backscatter to geophysical parameter are needed.

Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is the fraction of the incoming solar radiation in the Photosynthetically Active Radiation spectral region that is absorbed by a photosynthetic organism. This biophysical variable is directly related to the primary productivity of photosynthesis and some models use it to estimate the assimilation of carbon dioxide in vegetation. The solar radiation reaching to the canopy can be divided into direct radiation and diffuse radiation. The processes of two kinds of radiation are different. But a lot of retrieval models of FPAR did not consider this difference. In this paper, we established a FPAR inversion model which divided direct and diffuse FPAR. And MODIS LAI and surface albedo products were used as the model input data. And at the end of this paper, the inversion FPAR was verified with observation data and MODIS FPAR product.

The charge-coupled device (CCD) is visible to near-infrared imaging sensors onboard the Chinese Huan Jing 1 satellites. Like many sensors, the CCD lack onboard calibration capabilities, so alternative methods are required, e.g., cross-calibration. The wide field of view of the CCD sensors provides challenges for cross-calibration with narrow field of view sensors. We developed a technique to take advantage of a site with a uniform surface material and a natural topographic variation. Due to the topography, near-nadir Landsat Enhanced Thematic Mapper (TM) Plus (ETM+) observations actually see the material at a wide range of illumination and viewing angles. These observations and Advanced Spaceborne Thermal Emission and Reflection Radiometer global digital elevation model data were used to develop a model of this site's bidirectional reflectance distribution function that covered most of the illumination and view angle range of the CCD data. We validated this model by comparing the simulations to actual ETM + and TM surface reflectances. The validated model was then used to calibrate the CCD instruments. The results were consistent to within 5 % of field intensive vicarious calibration data.

Leaf area index is usually estimated by gap fraction measurement. The mean projection of the unit leaf area on a plane normal to view direction is dependent with the view zenith angle except the view zenith angle is equal to 57.5°. Researchers tried to use this particular angle to overcome the influence of the leaf inclination distribution function. However, the ring of 57.5° is relative to the normal of slope on the slope ground while the measure platform is usually designed based on the flat coordinate. A general angle conversion strategy between slope coordinate and flat coordinate is required by the measurement on the slope. The conversion method was derived in this paper and the 57.5° rings of different slope angles were drawn and compared.

Normalized difference vegetation index is a key parameter to describe physical and biological processes of plants. Vegetation compositing technology offers a new method to obtain NDVI products with special consistency and continuity. MODIS BRDF compositing scheme reduces angular, sun-target-sensor variations with use of a BRDF model, but the Walthall BRDF model inversion required at least five good quality observations. This limits the temporal resolution of NDVI product and increases the change uncertainty in the composite period, especially when the vegetation grow fast. Therefore, the multi-sensor composite strategy was developed to improve the temporal resolution to 4 days. We use a similar MODIS NDVI compositing as the initial algorithm to analyze the multi-sensor datasets, and A Multi-sensor NDVI composing algorithm was developed. Cross validation with MODIS NDVI products also show a satisfactory agreement.

HJ-1/CCD data has both high spatial resolution and high temporal frequency. By using NDVI time series and SVM classifier with HJ-1/CCD data source, this paper proposed a new resolution for land use classification of Dahuofang Reservoir, Liaoning Province, China. The validation result demonstrates that this resolution could obtain finer result than others.

To extract information about the Earth's surface from Earth Observation data, a key processing step is the separation of pixels representing clear-sky observations of land surface from observation influenced by cloud. This paper presents a new method used for MDOIS data over Tibetan plateau and desert. The method for cloud detecting based on the difference on band 6 (apparent reflectance of blue band) and band 31 (bright temperature in thermal infrared band) between cloud and land surface. Experimental results show that the method is feasible.

Leaf area index is a key parameter to describe physical and biological processes of plants. Remote sensing technology offers a new method to obtain LAI at regional scales, but it generally records plants information in horizontal. Therefore, the canopy reflectance is easier to reach saturation when plants growth flourished. This paper firstly defines the issues of canopy reflectance saturation, and then analyzes the reflectance contribution of each layers based on multilayer SAIL and FRT model for continuous and discontinuous vegetation, respectively. Besides, this paper analyzes the factors influencing canopy LAI saturation. Results show that the lower part of plants has fewer contributions to canopy reflectance. The leaf angle distribution and view zenith angle are two mainly factors influencing canopy LAI saturation.

In this letter, land surface temperature (LST) is derived from Geostationary Operational Environmental Satellite (GOES)-East (GOES-E) and GOES-West (GOES-W) using a revised dual-window LST algorithm developed by Sun and Pinker in 2004. LST derived from GOES is also evaluated against ground observations. The results show that the LSTs from GOES-E are warmer than those from GOES-W in the morning but lower in the afternoon, while there is no big difference around noon and during night. It is found that the original brightness temperatures from GOES-E and GOES-W show similar patterns to LSTs over time. The discrepancy in LSTs is most probably due to the fact that the Earth surface is warmer in GOES-E than in GOES-W in the morning but cooler in the afternoon. Some other factors may include the difference in satellite viewing geometry, image navigation and registration, calibration, and spectral response functions. It is expected that these effects should be small as those demonstrated in nighttime difference. When evaluated against the ground observations, over the overlap region, the LST bias error is positive from GOES-E but negative from GOES-W in the morning, leading to a positive LST difference, while the bias from GOES-W is close to zero but negative from GOES-E in the afternoon, resulting in a negative LST difference. Nevertheless, the LST root-mean-square errors from GOES-E and GOES-W are very close and reach the maximum around noontime. To synergistically use LST from different satellite sensors, we suggest using nighttime data; further study is needed to best use daytime LSTs.

Vegetation clumping index is required for canopy reflectance modeling and leaf area index (LAI) retrieval. Frequently used is the average clumping index. The angle effect is normalized in the definition of average clumping index. Clumping index angular distribution with different canopy spatial pattern and the same architectural features of single plant is modeled and analyzed in this paper. The real structure of single plant and the canopy spatial pattern are simulated by modified L-systems. The gap fraction is calculated by Radiosity-Graphics combined Model (RGM) and the clumping index is calculated through a formula which is derived from Nilson's classic formula to solving gap fraction. It can be seen from the comparison of gap fraction of six scenes that the maximum is appeared in the direction of zenith and the shape between forward and backward is roughly symmetrical. It can be seen from the comparison of clumping index of six scenes that the maximum is not always appeared in the direction of zenith and the shape between forward and backward is not always roughly symmetrical. The results indicate that the asymmetry of canopy spatial pattern will bring the asymmetry of the clumping index angular distribution and the clumping index angular distribution depends on both the architectural features of single plant and the canopy spatial pattern.

We propose a new model to calculate the Directional Brightness Temperature (DBT) over the canopy of the row-planted corn. The most advantage of this new model is to simulate the DBT over the canopy cross the whole growth circle, both row structured and homogeneous structured. As the geometric structure of canopy changes greatly while the biomass grows from sparse to continuous, this new model is a better option as a tool to analysis remote sensing data.

In this paper, two atmospheric profile sources were assessed for land surface temperature retrieval purposes. One is the MODIS atmospheric profiles product (MOD07), and the other is the National Center for Environmental Prediction (NCEP) operational global analysis data. Atmospheric profiles were used as the input to the MODTRAN4 radiative transfer model to calculate atmospheric parameters involved in atmospheric correction with the aim of retrieving land surface temperature (LST) in the case of the TIR domain. The LST retrievals from the HJ-1B IRS data were compared with ground measured temperatures obtained from a series of field campaigns in Hebei province, China, from May to September 2010. Ground measurements were performed over four land cover types: bare soil, full-cover wheat, full-cover corn and water surface. Six days of measurements over the water surface and three days over land were collected. The results indicate that the LST derived from HJ-1B IRS data using the NCEP and MOD07 profiles both showed good agreement with the ground LSTs, with Root Mean Square Error (RMSE) of 1.19 and 1.51 K for NCEP and MOD07, respectively. The results presented in this paper show that the MODIS and NCEP atmospheric profiles are useful for accurate atmospheric correction in the TIR domain when local soundings are not available. In particular, the NCEP profile provides higher accuracy, whereas the MOD07 profile provides a higher spatial resolution.

Photosynthetically active radiation (PAR) can be calculated by radiation transfer model. With the progress of remote sensing application, more and more input parameters could be inversed by remote sensing data. Then using these inversion parameters, the PAR could be estimated. The normal method estimation PAR by remote sensing data is using instantaneous value at the time of polar orbital satellite passing to accumulate the daily amount of PAR. However, daily cloud cover changes very obviously. PAR is always affected by atmospheric composition and cloud condition. So in this paper, the method which could combine geostationary and polar orbital satellite to improve PAR product time resolution is the main content. At last, MTSAT geostationary and MODIS polar orbital satellite are used to this method to estimate hourly PAR in whole China area.

Land surface phenology dynamics reflect the response of terrestrial ecosystems to inter- and intra-annual dynamics of the climate. However, there are very few regional-to-global phenology products and existing phenology products still show some deficiencies in practical application. Meanwhile, none of existing methods for monitoring vegetation phenology has consistent performance for all vegetation types. Based on the existed research work, this paper developed a mixed model to monitor vegetation phenology in China. Different methods are used in this model according to different situation. This model was employed in China in 2007 assessed using field observed phenology and MLCD data. The root mean square error (RMSE) for different vegetation types are 4.0-33.5, the mean absolute error are -20.6-15.3 and the correlation are 0.404-0.887. By comparison with MLCD data, the success rate and the accuracy of the method have been highly improved.

In this study, land surface temperature (LST) is assimilated by using Common Land Model (CoLM) and Ensemble Kalman Filter (EnKF). To found the most reasonable method on model error treatment and remote sensed land surface temperature assimilation, some methods on the model ensemble generation and the construction of observe operators are compared. Though experiments show the two methods have similar result, forcing and parameter perturbation can generate ensemble more reasonable compared with perturbing only state variables, and is more easily to achieve in realistic assimilation. In observe operator comparison, by the component temperature decomposition method, the land surface temperature of remote sensing can update ground surface temperature in the CoLM model directly, which has more obvious physical meaning than other observe operator. A synthetic experiment also shows this method have the best result in the comparison.

Leaf area index (LAI) is an important ecological and environmental parameter. Currently, most LAI inversion methods are based on single model. For continuous vegetation canopy, PROSAIL model used to retrieve vegetation biophysical properties based on lookup table or neural network methods. As for discontinuous vegetation canopy, vegetation canopy reflectance was simulated from Geometric-Optical model, and the relationship between LAI and vegetation indexes was established using simulated reflectance. This paper introduces a method combining the PROSAIL and four-scale model simulated the continuous and discontinuous vegetation canopy reflectance respectively, and then establishes lookup tables among multispectral reflectance, vegetation indices and LAI. The new method using land cover map has been employed to inverse HJ-1B CCD2 LAI in zhangye region of the middle reaches of the Heihe river basin. Results show that the new inversion results have much more deviation with observed dataset, but have good agreement with MODIS LAI in crops.

Albedo is essential in earth radiation budget and global climate monitoring. GLASS albedo is a newly developed global broadband land-surface albedo product with 1-km spatial resolution and 1-day temporal resolution. AB algorithm is employed to generate daily albedo product by building a linear regression relationship between narrowband directional reflectance and broadband albedo. AB algorithm avoids the restrictions on the observing angles, but suffers from the assumption that the land surface should be homogeneous and flat without terrain relief. In our research, we proposed a technique to improve AB algorithm for retrieving albedo in rugged terrain, which is called TAB algorithm. The preliminary validation using simulation data indicated that the accuracy of AB algorithm could be significantly improved when the terrain effect is considered.

Because bidirectional reflectance (BRF) and backscattering coefficient (Sigma0) are sensitive to LAI (Leaf Area Index) and LWC (Leaf Water Content) in different growth stages, fusing optical and microwave remote sensing data is useful to accurately monitor the two parameters. Joint simulation is one way to find the sensitive optical wavelength or microwave frequency of the unified canopy scene. Due to the inputs of the optical and microwave model are different, in the paper, three intermediate models were used to achieve the integration of critical inputs for joint simulation. They were leaf radiative transfer model PROSPECT, exponential soil reflectance model, and a transforming model. The simulation results indicated that BRFλ=680and BRFλ=660were sensitive to LAI when it is lesser than 3.5, while Sigma0X-band,VVand Sigma0C-band,HHwere sensitive to LAI when it is between 3.5 and 6. Besides, Sigma0X-band,HHcan reflect LWC variation independent on structural parameters.

Desert plays a very import role on earth radiation budget and calibration research. In this paper, we propose a new algorithm for retrieving BRDF of desert using time series of MODIS imagery. The central idea of this algorithm is to detect the "clearest" observation during a temporal window for each pixel. For desert, the temporal window can be one year since its surface is highly stable. The clear observations are then used to fit the desert BRDF. Finally, the fitted BRDF is used to simulate the MODIS images under "real" conditions and the simulated MODIS images are compared with MODIS surface reflectance product (MOD09 and MYD09), which shows that the R2 and RMSE of the simulated surface reflectance is much better than those of MOD09 product. Therefore, the derived BRDF from this new algorithm much more accurately describe the directional characterization of the desert site.

Remotely sensed land surface temperature (LST) is of great value to the research in the fields of climatology, hydrology, ecology, and biogeochemistry,as well as a wide range of interdisciplinary research areas, since it isan efficient and practical way of acquiringtemperature variability globally and continuously. In the paper, the generalized split-window algorithm proposed by Wan and Dozier (1996) is used to estimate LST from Visible and Infrared Radiometer (VIRR) onboard the second generation of China's polar-orbiting meteorological satellite (FY3A).MODTRAN 4.0 and the Lhermodynamic Initial Guess Retrieval database 3 (TIGR-3) are used to simulate the data for fitting the algorithm's coefficients. Lhe algorithm fitting accuracy is improved by dividing the LST, the average emissivity (ε) and the water vapor content (WVC) into several sub-ranges. Finally, the validation at five locations is performed and the results show thatthe LSTs estimation from FY3A/VIRR dataagree with the ones extracted from the MODIS 1 km LST products very well.

Land surface emissivity (LSE) is an essential parameter in deriving land surface temperature form remote sensing data. According to the single channel characteristics of HJ-1B Infrared Scanner (IRS), a combined method for estimating LSE was proposed based on the vegetation cover method and classification-based method. The proposed method requires inputs such as static land cover product, vegetation and ground emissivity for each land cover and vegetation cover product. The sensitivity analysis indicates that this method could achieve good accuracy with LSE relative errors vary from 0.4% to 2%.

Land surface temperature (LST) is required for a wide variety of scientific studies, from climatology to hydrology and ecology. The feasibility of using atmospheric profile extracted from NCEP data for LST retrieval from HJ-1B/IRS data was analyzed in this paper. A series of ground measurements were carried out to validate the IRS LST results in Hebei province, China, from May to September, 2010. The results indicate that the LST derived from IRS data by using NCEP data showed a good agreement with the ground LSTs, with RSEM lower than 1.5K. Therefore, it can be concluded that the profile extracted from NCEP data is a useful source for LST retrieval from HJ-1B/IRS data.

Clumping index is an important vegetation structure parameter to describe the foliage clumping in canopy quantitatively. It is defined as the ratio of the effective leaf area index to the true leaf area index. In previous studies, it is generally considerate that cluster of canopy and below canopy scale in pure pixel. However, the in-pixel spatial heterogeneity need be taken into account estimating clumping index in mixed pixel, which is different from clumping index of pure pixel. A new method to calculate clumping index of mixed pixel based on fine spatial resolution image is proposed in this paper. The sensitivity analysis has been processed and its results show that the pixel spatial heterogeneity and view zenith angles cannot be ignored for calculating the mixed-pixel clumping index. The method is capable of correcting the scale difference caused by the heterogeneity of the vegetation cover inside the mixed pixel the view zenith angle. The formula presented can estimate clumping index of the mixed pixel more accurately, which is significant for LAI inversion of coarse spatial resolution and the precision accuracy application of carbon cycle model.

The backscatter values of altimeter and scatterometer have been investigated for possible use over land surface, especially for vegetation covered area. The spatial and temporal variations of backscatter coefficient of merged altimeter data (JASON1/ ENVISAT RA2), QuikSCAT, and CYCLOPES Leaf Area Index (LAI) over seven selected vegetated areas from 2002 to 2007 are calculated and compared. Initial results indicated that over vegetated areas with a strong seasonal cycle, the altimeter and scatterometer SigmaO measurements are strongly correlated to LAI. The Linear relation between VV/HH QuikSCAT backscatter and LAI is found at short and sparse vegetation, which is not valid at the dense vegetation. The VV/HH is not sensitive to low LAI value. The altimeter and scatterometer backscatter measurements of Ku band are useful to monitor vegetation dynamics as an independent data source compared to the products of optical sensors.

Unlike ERS1/2 and JASON1, ENVISAT RA2 provides the backscatter coefficient from four retrackers (Ocean, Ice1, Ice2, Sealce), which is useful to study the behavior of RA2 backscatter over land. This paper presented an analysis of RA2 four Ku band backscatter performance over global, especially over vegetated area, and provided a detailed comparison with JASON1 data. The temporal evolution of the RA2 backscatter coefficients was evaluated for main vegetation type (ENF, EBF, DNF, DBF, mixed forest, croplands, savannas, grasslands) from October 2004 to June 2009. Results indicated that SigmaO values from four retrackers show large variability. The backscatter of Ice1 and Sealce retrackers are systematic larger than that of the Ocean and Sealce. The inter-comparison with JASON1 showed that the greatest degree of correlation of the RA2 SigmaO with JASON1 is obtained with Ice1 and Sealce.

The atmospheric downward longwave radiation (DLR) is a key variable of land surface energy budget. In this study, the clear-sky DLR at high spatial resolution in Heihe River Basin was estimated from MODIS data, and three algorithms containing empirical and hybrid methods were compared. Compared with field measurement, the Idso scheme and Tang's scheme had relatively better performance than Wang's scheme, with RMSE of 27.50, 26.96, 30.23 W/m2, respectively. For Idso scheme, the major error was caused by the distance between the lowest level of MOD07 and the surface level. For Wang and Tang's scheme, the major error came from the difference between the atmospheric profiles used to train the schemes and the actual atmospheric condition in the study area. Net longwave radiation (NLR) was also calculated, and the RMSE of all sites was 31.24 W/m2.

As a main component of Net Surface Radiation (NSR), the Net Surface Shortwave Radiation (NSSR) significantly affects the climatic forming and change. However, it is impossible to observe NSSR directly over large areas especially for rugged surfaces such as Qinghai-Tibet Plateau. The primary objective of this study was to estimate daily NSSR over rugged surfaces at 1-km resolution for both clear and cloudy days. All the atmospheric input data were derived from MODerate resolution Imaging Spectroradiometer (MODIS) data, and therefore the errors from multi-sensor architectures were avoided. The performance of the model was evaluated by comparing its results with field measurements at four sites, and the correlation coefficients of NSSR varied between 0.71-0.93. Possible error may arise from the lower temporal resolution of the cloud products and the spatial resolution of DEM.

In this paper, we propose a method to extract the feature of Bi-directional Reflectance Distribution Functions (BRDF) over Badain Jaran Desert using Landsat-TM/ETM+ and ASTER GDEM data. Badain Jaran Desert is characterized with homogeneous and rugged terrain, which forms a natural Bi-directional Reflectance data sets with hypotheses that the surface structure of each slope element does not vary with the variations of slope and aspect; therefore, we can use nadir view Landsat-TM/ETM+ imagery reconstruct the BRDF characterization of this experimental site. The results show that this method can simulate the BRDF feature of land surface accurately.

Temperature and emissivity separation (TES) algorithms designed to work with mid-infrared (MIR) hyperspectral data are extremely limited. Two TES algorithms originally designed for long-wave infrared hyperspectral data, specifically, the iterative spectrally smooth (ISS) algorithm and the stepwise refining algorithm, are extended into MIR and renamed the extended iterative spectrally smooth (EISS) and extended stepwise refining algorithms (ESR), respectively. Numerical experiments are first conducted to evaluate their feasibility. The results of the numerical experiments indicate that the accuracy of the ESR algorithm is higher than that of the EISS algorithm. Moreover, the ESR algorithm is more robust than the EISS algorithm under sunlit conditions. Their accuracy is then validated with in situ measurements. Finally, the emissivity root mean square errors (RMSEs) of the EISS and ESR algorithms are compared with the data derived with the ISS algorithm using in situ measurements. Results show that the average emissivity RMSEs of 0.03 in 2000-2200 cm-1 and of 0.03-0.30 in 2400-3000 cm-1 for nighttime, and 0.02 in 2000-2200 cm-1 and 0.03 in 2500-3000 cm-1 for daytime, can be obtained from ground-based MIR hyperspectral data using the ESR algorithm.

A two-level radiosity algorithm was developed here for the computation of global radiation transfer at complex land cover called HRAD, which rapidly simulates the surface leaving radiances in three dimensional (3D) landscapes. The algorithm is an integration of a ray casting module and an adapted hierarchical radiosity method with heterogeneous volume clusters. The time cost of our algorithm was nearly linear to the polygon number. The algorithm was first validated by comparing the simulated radiances with that of Radiosity-Graphics combined Model (RGM) at small scenes. The single scattering results were highly correlated with that of RGM (R2> 0.99 and RMSE2> 0.99 and RMSE2> 0.98 and RMSE = 0.015). By using the Gauss scene in RAMI-III website, we also validated our algorithm.

Land surface temperature (LST) is required for a wide variety of scientific studies, from climatology to hydrology and ecology. This paper proposes a single-channel parametric model (SC-PM) algorithm for retrieving land surface temperature from the HJ-1B/IRS thermal infrared data. The SC-PM algorithm is based on the parametric model (PM) developed by Ellicott et al. (2009), the coefficients of PM are updated for HJ-1B/IRS, and the altitude is considered when extracting atmospheric profile from NCEP data. The proposed algorithm is evaluated by simulated data and MODIS LST products. The results show an root mean square error (RMSE) of 0.22K for the simulated data, and 1.73K for the MODIS LST product. This indicates the algorithm is suitable for producing HJ-1B/IRS LST product.

In this paper, we proposed a localized land surface temperature retrieval method using water vapor and in situ data, and applied it in Yingke area. With the ground measurement of emissivity and water vapor simulated, we recovered LST from MODIS/Terra Level-1b data. ASTER temperature product was used to compare with the MODIS retrieval result. The comparison showed the MODIS retrieval result agreed with ASTER data with an average difference of 1.64 K.

It is a special way to investigate continental surfaces using radar altimeter, besides scatterometers and Synthetic Aperture Radar among the active microwave remote sensing sensors for the altimeter works at the nadir point of satellite and at dual-frequency, which can measure the backscatters from different layers instantaneously. The aim of this paper is to merge multi-radar altimeter dual-frequency backscatter measurements over land surface to achieve better spatial and temporal resolution. The 0.5°×0.5° merged altimetry backscatter maps between 66° N to 66° S over land surface every 6 days are generated in Ku, C, and S band for the period from January 2002 to June 2009. Temporal profiles of backscatters are examined for main land types (desert, forest, savanna) to validate the dataset and to analyze radar response to land surface variability through 8 years.

In order to make improved use of the altimetry waveform data near the China coastal area, we present a new retracker (OceanCS) consists of waveform Classification and Sub-waveform extraction based on coastal altimetry waveform analysis and existing retracking algorithm comparison. The OceanCS retracker combines the advantages of Ocean retracker and OCOG retracker. The new retracker is further compared to other retrakers (Ocean, Ice-2, OCOG, Threshold, Beta5, and GDR product) using one year (Mar. 2006 to Feb. 2007) Jason1 measurement and in-situ tide gauge station Sea Surface Height and buoy Significant Wave Height measurements. Comparison shows that the OceanCS retracker performs better than other trackers. Furthermore, Calculating the SWH by combining two retrackers is a way to compare different retracking algorithms without taking into account the error of the atomosphere and sea state corrections.

The radiation from the sun to satellites in the sky is always modulated twice by atmosphere. Aerosol is one of the most active components in atmosphere and it usually contaminates the remotely sensed imagery severely so that most remotely sensed imagery cannot be used without atmospheric effect correction. However, the remotely sensed imagery is always the coupling of atmosphere and land surface information, which makes it very difficult to decouple the remotely sensed information to retrieve accurate atmospheric information and land surface information respectively from remotely sensed imagery alone. Based on the physical mechanism of radiative transfer model, many researchers assumed the specific surface condition, such as dark objects and invariant objects, so the atmospheric information like aerosol optical depth (AOD) can be decoupled from remotely sensed information. Since these assumptions are just for some specific surface conditions, the atmospheric information of many other surface conditions, such as sparsely vegetated areas and snow covered areas, are not available. Therefore, we propose an algorithm of aerosol information retrieval for agricultural areas based on land surface modeling. By analyzing the soil, the leaf and the canopy spectra, we selected a set of models to calculate the surface reflectance of different land surfaces, which include bare soil areas, sparsely vegetated areas and densely vegetated areas. In addition, the water vapor effect of 2.1μm spectrum band has been considered in this paper for retrieving more accurate canopy and soil reflectance. Finally, North China Plain is selected as experimental area for aerosol information retrieval through the proposed algorithm, and the AOD value measured by sunphotometer is taken as true value to evaluate the algorithm's accuracy.

Due to limited vertical resolution, the waveform of vegetation whose height is relatively low will superpose on soil waveform. Therefore, lidar full-waveform data were mainly used in forestry, but no research in the crop. In this paper, in order to derive crop height, a gaussian decomposition algorithm based on transmitting waveform is adopted to distinguish the crop waveform from soil waveform, and to extract peak location and pulse width from raw waveform data, proving it is a reliable and highly accurate decomposition algorithm. Moreover, the decomposition algorithm lays the proper foundation for obtaining other crop biophysical parameters.

Light use efficiency model is one of the method to retrieval regional scale Gross Prime Productivity (GPP). Absorbed Photosynthetic Active Radiation (APAR) and Light use efficiency are the main parameters of this kind of model. At the same time, light use efficiency is affected by air temperature and precipitation. In this article, one of the Light use efficiency model is used to retrieval daily GPP of the Chinese five typical ecosystem experimental station in 2003. The inversion results are compared with MOIDS NPP product and station measurement data. Based on the different air temperature and precipitation condition of the different station, it also analyses the sensitivity of parameters.

This paper proposes a new multi-angular & multi-spectral BRDF model (ASK Model) base on the kernel-driven conception, and outlines an algorithm suitable for broadband albedo retrieval with the new model. By adding component spectra into kernels as prior known driven variables, the new model express BRDF as a linear combination of wavelength independent kernel coefficients and kernels expressed as functions of both observation geometry and wavelength.

Over the last few decades, there has been a focus on better determining evapotranspiration and its spatial variability, but for many regions routine prediction is not generally available at a spatial resolution appropriate to the underlying surface heterogeneity. Over agricultural regions especially in China, this is particularly critical, since the spatial extent of typical field scales is not regularly resolved within the pixel resolution of satellite sensors. Clearly, for landscapes with significant variability in vegetation cover, type/architecture, and moisture, the spatial resolution of the remote sensing data is crucial for discriminating fluxes for the different land cover types and hence avoiding significant errors due to application of a land surface model to a mixed pixel containing large contrasts in surface temperature and vegetation cover. High resolution remotely sensed data is seemly to discriminate the differences over heterogeneous landscape, but we are inclined to use MODIS data (high temporal resolution, free of charge) to estimate regional ET. At such coarse spatial resolutions, the capability to monitor the impact of land cover change and disturbances on ET or to evaluate ET from different land covers is severely hampered. So, understanding the role of landscape heterogeneity and its influence on the scaling behavior of surface fluxes as observed by satellite sensors with different spatial resolutions is a critical research needed. In this study, we are inclined to use the classified data provided by high-spatial-resolution remotely sensed image to improve regional ET from coarse-spatial-resolution data.

Routine (i.e., daily to weekly) monitoring of surface energy fluxes, particularly evapotranspiration (ET), using satellite observations of radiometric surface temperature has not been feasible at high pixel resolution because of the low frequency in satellite coverage over the region of interest (i.e., approximately every 2 weeks). Cloud cover further reduces the number of useable observations of surface conditions resulting in high-resolution satellite imagery of a region typically being available once a month, which is not very useful for routine ET monitoring. Radiometric surface temperature observations at more than 1 km pixel resolution are available multiple times per day from several satellites. However, this spatial resolution is too coarse for estimating ET from individual agricultural fields or for defining variations in ET due to land cover changes. In this paper, Landsat ETM+ data in the visible and near-infrared wavelengths, are used for computing vegetation indices provide higher resolution information(60 m resolution) on vegetation cover conditions. Then the vegetation index-radiometric surface temperature relationship is exploited and utilized in a disaggregation procedure for estimating subpixel variation in surface temperature. In addition, a remote sensing-based energy balance model is used to compare output using Landsat ETM+ data versus estimated surface temperatures. From these comparisons, the utility of the surface temperature disaggregation technique appears to be most useful for estimating subpixel surface temperatures at resolutions corresponding to length scales defining agricultural field boundaries across the landscape. Finally, the simplified model to estimate daily ET is revised considering the scaling effect on heterogeneous region.

Cloud cover detection is an essential step for the retrieval of solar radiation using geostationary satellite imageries. To obtain the accurate estimates of cloud cover of MTSAT-1R image, a multispectral threshold technique which makes take use of all five spectral channels of MTSAT-1R was developed. The algorithm has been applied to detect cloud and snow areas in the high altitude region of the northwest of China, during the Watershed Airborne Telemetry Experimental Research (WATER) experiment in the Heihe River Basin. The remote sensing of cloud cover was validated by the ground-observed cloud cover fraction. The results indicated that the multispectral threshold method performed satisfied.

The high spatial and high temporal satellite data is necessary in the operational agricultural applications of remote sensing. But till now the advantages of high spatial and high temporal resolution still can not be realized in single sensor. The PSP method (Patch Spectral Purification Method) is capable of retrieving field patch average information from high temporal but moderate spatial resolution satellite data, which meets the requirement of high spatial and high temporal resolution information in the real monitoring applications. In this paper a PSP-based methodology is proposed to retrieve the high spatial and high temporal resolution information for the growth and drought monitoring using multiple satellite data. An application demonstration was made in Xinjiang, China to monitor the cotton growth and drought with MODIS and Landsat/TM data. And the processing software-AgRsis (Agricultural Remote Sensing Inversion System) was realized to generate the daily crop parameters standard maps(e.g. NDVI, TVDI) for the crop growth and drought monitoring.

Satellite image simulation is one of the key methods to check the expected performance of the satellites before they launched or when satellites can not provide images in other time. In order to provide a useful tool to analyze whether the payload of HJ-1B (a small satellite of the environment-monitoring constellation) is enough, we develop a simulation system for the infrared cameras, which consists of four bands including NIR band (0.75-1.10¿m), SWIR (1.55-1.75¿m), MIR (3.50-3.90¿m) and TIR (10.5-12.5¿m). The spatial resolution of NIR and SWIR band is 150 meter, while 300 meter for the MIR and TIR band. The sensor is an optical-mechanics multi-scanning system with maximum scanning degree of 29 degree.

It is suitable for remote sensing monitoring and precision agricultural for Xinjiang cotton for its unique natural and ecological condition and growing and cultivating characteristic. However, precise monitoring is weak in Xinjiang cotton. We took the farm land of Xinjiang Production and Construction Corps as an example and made the cotton leaf area index (LAI) inversion. It is befitting to invert cotton LAI for Mini-satellite of Beijing-1' wide scope (600 kilometer), middle spatial resolution (32 meter) and high temporal resolution (2-3 days). The LAI is inverted for Beijing-1 mini-satellite data based on the physical canopy reflectance model and lookup-table inversion method. The LAI is also inverted for TM data considering scaling problems. It is feasible to invert LAI for Beijing-1 through the comparison between the inverted LAI and the LAI from the experiment.

Row planted crop is the transitional crop type with the discrete structure and continuous structure. There are different canopy structures for different growth stages. The canopy structure will transfer from row structure to continuous structure around the elongth growth stages. Furthermore, the elongth growth stage is key growth stages for the crop. For parameter inversion, it is significant to propose the key growth stages to simplify the model selection and to improve the parameters inversion accuracy. We put the object on one row period for the similar structure of the row planted crop. For each period, four components (sunlit vegetation and soil; viewed vegetation and soil) can be computed based on the bidirectional gap probability model, and structure parameters (W (row width), H (row height), S (row spacing), etc.) and view and solar zenith/azimuth angles. At the same time, the equivalent radiance for vegetation and soil from direct illuminated light and from the diffused and multi-scatted light will be computed. The key growth stages model (KGSM) is the sum of the four component radiance which is the product of each component area and the corresponding equivalent radiance. Through the validation based on the RGM and SAILH model, the canopy bidirectional reflectance can be simulated based the KGSM. The model validation is also done for experiment measurement.

Aerosol optical thickness (AOT) is retrieved from HJ-1A/B images using Structure Function Method (SFM) over Beijing and its surrounding area. SFM is discussed by establishing structure function formula, choosing window size and distance value. Retrieved result is validated by the ground-based observation.

A simultaneous airborne, satellite and ground based remote sensing experiment which is aiming to improve the observability, understanding, and predictability of hydrological and related ecological processes at catchmental scale is implemented in a typical inland river basin of northwest China. The experiment is composed of the cold region, forest, and arid region hydrological experiments as well as a hydro/meteorological elements and Doppler radar precipitation observation experiment. Airborne microwave radiometers at L, K and Ka bands, hyperspectral imager, thermal imager, and lidar are used. Various satellite data are collected. Based on these observations, the remote sensing retrieval models and algorithms of water cycle variables can be developed or improved, and a catchment-scale land/hydrological data assimilation system is going to be developed.

Evapotranspiration (ET) plays an important role in energy exchange and hydrological cycle between the land surface and atmosphere. Over the last decades, scientists have developed many models and algorithms to have a better determination of regional ET by remote sensing data. However, at regional scale, the field measured data and remote sensing data are inconsistent with each other due to the underlying surface heterogeneity. In this study, remotely sensed ET was carried out using Landsat ETM+ and MODIS data to understand the role of landscape heterogeneity and its influence on the scaling behavior of surface fluxes. This knowledge is very useful for correction of scale error of ET estimation, and can also be used to study the spatio-temporal effect of remotely sensed ET, which is going to be carried out in the future.

Few in-situ solar radiation data is available over mountainous areas. As a result, the global solar radiation in these areas is generally accessed by the estimation of modeling. In this study, a distributed model was applied to estimate the global solar irradiance in Zhejiang using horizontal direct and diffuse irradiance from 6SV (Second Simulation of a Satellite Signal in the Solar Spectrum, Vector) lookup table, and topographical parameters extracted from DEM (digital elevation model). The results indicated that the estimation of global solar irradiance is satisfied with 6SV LUT (coefficient of determination (R2) is 0.9816, and root mean square error (RMSE) is 45.0W/m2). The global solar irradiance at 1-km resolution in Zhejiang Province show spatial variations, especially with the variations of aspects.

More and more wide-view angle or multi-angular sensors provide the possibility to retrieve vegetation parameters. It is an important issue to access the accuracy and uncertainty of the products retrieved from different view angle observations. This paper presents an approach to evaluate the information content of the multi-angular remote sensing data. The proposed method is based on information theory. By using the entropy difference between all unknown parameters and non-target parameters for the remote sensing data, the information content is quantified. The presented methodology revealed the information content in the remote sensing data. The accuracy of the vegetation parameters retrieved from canopy reflectance depends mainly on the information about target parameter contained within observations. The relationship between information content and the LAI inversion accuracy is listed in this paper.

Beijing and Xianghe sites are selected as study area. MODIS/Terra C005 aerosol product of the whole 2005 year is validated by matching the MODIS product and AERONET within ±30 min of MODIS/Terra overpass times considering the aerosol movement velocity of given sites, then to analyze their applicability. The results show that MODIS C005 aerosol product overestimates the real value greatly at Beijing site, for that the reflectance in the bright surface is not really described, and the urban aerosol characteristic could not be defined exactly. MODIS C005 product is very accurate and reliable at Xinaghe site except in winter, could be used. The results show that the method of the surface reflectance determination used in the new algorithm is feasible, but is not suitable to all types of surface. Otherwise, season characteristics of two sites are analyzed in detail.

it is well known that soil emissivity exhibits large uncertainty in thermal infrared spectral region. In order to find a way to derive soil emissivity accurately, we examine several existed typical temperature emissivity methods (e.g. NEM, ISSTES, ADE, MMD and TES). Based on the 58 soil spectra of the ASTER Spectral Library, several sets of thermal infrared hyperspectral data were simulated to assess the applicability, stability and accuracy of these methods respectively. This work also brings some improvements of the algorithms based on the results analysis, including: a new optimal maximum emissivity has been suggested for NEM, a better empirical relationship has been discovered to substitute the original mean-minimum maximum difference relationship in MMD method, the original NEM module has been replaced by ISSTES to acquire the accurate initial value of emissivity in TES. As a conclusion, we find the ISSTES is the best. Finally, we present an example of soil emissivity extraction using five methods mentioned above with ground-based measurement hyperspectral data. The distribution of derived emissivity spectrum verifies the results of algorithm analysis.

The paper firstly points out the defect of conventional temperature and emissivity separation algorithms when dealing with hyperspectral FTIR data: the conventional temperature and emissivity algorithms can not reproduce correct emissivity value when the difference of ground-leaving radiance and object's blackbody radiation at its true temperature and the instrument random noise are on the same order, and this phenomenon is very prone to occur in the extremity of 714-1250 cm-1in the field measurements. In order to settle this defect, a three-layer perceptron neural network has been introduced into the simultaneous inversion of temperature and emissivity from hyperspectral FTIR data. The soil emissivity spectra from the ASTER spectral library have been used to produce the training dataset, and the soil emissivity spectra from the MODIS spectral library have been used to produce the test dataset, the result of network test shows the MLP is robust. Meanwhile, ISSTES algorithm has also been used to retrieve the temperature and emissivity from the test dataset. By Comparison the result of MLP and ISSTES, we find MLP can overcome the disadvantage of conventional temperature and emissivity separation algorithms, although the RMSE of derived emissivity using MLP is lower than ISSTES as a whole. Hence, the MLP can be regarded as a beneficial complementarity to the conventional temperature and emissivity separation algorithms.

Based on the four step method, the paper puts forward a method for deriving mid-infrared emissivity. This method obtains thermal infrared emissivity and temperature with high accuracy by utilizing the ISSTES algorithm from thermal infrared data, then introducing the derived temperature into mid-infrared emissivity extraction, reducing the number of parameters need to be inversed in mid-infrared, forming redundant observation, and using the least square method to solve the equation at last. More attention has been paid into analyzing the impacts of instrument calibration error and simplification of radiative transfer equation on the extraction of mid-infrared emissivity. Finally, the paper gives out the reason for large error of emissivity inversion in some bands of mid infrared based on the simulated data.

The vegetation water condition is determined by soil water content. So soil water condition can be reflected by means of vegetation water condition indirectly. There have been many detailed studies on crop water stress index from the micrometeoological aspect before, for example, CWSI (crop water stress index) and crop water stress microclimatic model based on CWSI. The generation of these indices need many meteorological data and is not suitable to regional water stress study because of the difficulties in limitation of data acquisition of related data on the surface. Moreover, there are a few relevant studies using remote sensing techniques, such as WDI (water deficit index), VCI (vegetation condition index) and TCI (temperature condition index). Although these indices are suitable to regional study, they utilize the statistic value of remote sensing data for years and they are on the basis of pixel scale. Therefore, the precision of quantitatively assessing surface water is limited to some extent. In view of problems of these water stress indices, this article is going to discuss a not only simple but also reasonable method to derive vegetation water. Different vegetation water condition could cause the variation of vegetation spectrum, so vegetation water stress can be shown by remote sensing vegetation indices, such as vegetation condition index and anomaly vegetation index et al. In view of this, soil water content can be assessed indirectly. But vegetation indices neglect some environment factors, eg. temperature and precipitation. The less evaportranspiration, the higher vegetation and soil temperatures. Therefore the vegetation temperature is a direct indicator of vegetation water stressed and drought. In a word, the soil water is positive correlation with vegetation index and negative correlation with temperature. It takes the North-West semi-arid area - Xilingole district of Inner Mongolia as study area. Different degraded grasslands are selected as objects in this study. MODIS (moderate resolution imaging spectroradiometer) has thirty-six bands of visible/near-infrared and thermal infrared with abundant information. This study selects MODIS as data source. In consideration of the spectral character of MODIS data and different degraded grasslands, the reflective spectrum of vegetation is greatly affected by soil. MSAVI (modified soil- adjusted vegetation index) is selected to weaken the disturbanceof soil information. MSAVI and NDWI (Normalized Difference Water Index) are deduced using one visible band (0.66 mum) and two near-infrared bands (0.86 mum, 1.24 mum). In order to estimate the vegetation water more accurately. Vegetation component temperature is inversed using two thermal infrared bands (8.6 mum, 11 mum) according to the emissivity distribution of vegetation with the wavelength from eight to twelve micrometer and the correlation analysis of MODIS data. Sequentially, the vegetation water synthesis index is acquired by analyzing the coupling character of three indexes, which can reflect the vegetation water condition. Then the vegetation water content can be extracted using the synthesis index effectively. Lastly, the vegetation water content is validated using measured data. The matching results show that the synthesis index is directly proportional to the measured data. It proves that the synthesis index and the vegetation water content are credible and the method is reasonable. This study has discussed a new method to know the regional vegetation water condition from satellite remote sensing data directly and quickly.

Some typical topographic correction methods, such as cosine model, C correction model, SCS model, SCS+C model and Minnaert model, have been assessed in detail in this paper using GOMS model. A BRF model also presented for topographic effects eliminating and its application in Jiangxi rugged area. The result shows that the BRF model has the topographic correction ability.

In rugged area, the solar radiance is accepted by the sensor after a complicated interactive process between solar incidence, atmosphere and earth surface target. In this paper radiance received by one earth target is analyzed. Solar direct radiance, sky diffuse radiance and background terrain reflective radiance were obtained using a fit model. Combined with radiative transfer code and bi-directinal reflectance factor, atmospheric and topographic effects of Landsat/TM that covers Jiangxi rugged area had been eliminated. Several criterions were taken as the correction result validation. This paper shows that the method has robust atmospheric and topographic correction ability.

The consistent end-to-end simulation of them is an important task, sometimes the only way for the adaptation and optimisation of a sensor and its observation conditions, the choice and test of algorithms for data processing, error estimation and the evaluation of the capabilities of the whole sensor system. It is essential to accomplish simulation of atmospheric radiative transfer, if a complete imaging simulating system is to be expected. Based on given resolution and directional capabilities of the instrument, and combination with land surface temperature and emissivity data obtained from airborne imagery, TOA (top of atmosphere) radiance images have been simulated pixel by pixel coupling the atmospheric radiative transfer analytic model extended from MODTRAN4 and the atmospheric adjacency effect model derived from point spread function (for atmospheric directional and adjacency effect). In this way, all major scattering and emission contribution of atmosphere were considered. Through analysing results, it indicates that analytic model and adjacency effect model is more adequate for thermal infrared imaging simulation than others existing models.

The ear is the top layer of mature wheat and has very different geometric and thermal characteristics from that of leaves. Compared to the directional brightness temperature (DBT) of wheat canopy without ears, the DBT at the ear stage has specific features, and the ear effects could not be explained by previous models. This paper proposes a hybrid geometric optical and radiative transfer model to reveal the combined influences of the geometric structure of ears and leaf; the temperature distribution of ear, leaf, and soil; and the Sun-target-sensor geometry on the canopy DBT. The soil, leaf, and ear layers are taken into account in the model so it is named as the Soil Leaf Ear Combined (SLEC) DBT model. We compare the model prediction with the field measurement data. The results show that the new SLEC DBT model can simulate the DBT of wheat at the ear stage with an accuracy of 0.78 K.

Radiosity-graphics combined model (RGM) has been proposed to calculate the radiation regime and bidirectional reflectance distribution function of complex 3D scene, which is limited in visible and near-infrared wavelength (0.3-3 mum) region. In this paper, RGM is extended to thermal region (named as TRGM) based on thermal-radiosity theory and thermal-emission directionality of vegetation canopy. The TRGM has been implemented on Microsoft Windows platform, and a parameterization scheme for crop canopies is introduced in this paper. It is then evaluated by comparing with two row-crop directional thermal emission models and one thermal radiative-transfer model. Field experiment data has been used to validate the TRGM for row structural wheat and maize canopies. The root mean square error of directional brightness temperature (DBT) is smaller than 1.0degC for the wheat canopy and 0.5degC for the maize canopy while the canopy DBTs vary more than 4degC. Model sensitivity analyses have also been conducted to illustrate influences of component temperature distribution, component emissivity, incident atmospheric radiation, and canopy structure on the crop canopy DBT.

A high-precision method for mapping the fraction of wheat Area in Luancheng County, North China Plain is presented and validated. The method is based on a spectral mixture analysis model and a optimal temporal TM image election where six main endmembers (greenhouse , soil, wheat, roof, nursery garden, shadow), constitute the observed pixel reflectance from the satellite. Given the reflectance observation, the fractional wheat area(FWA) is solved from the spectral mixture analysis(SMA) model. The high-precision estimation for six endmember SMA can be expected at an optional temporal range, which is the last ten days of March month every year. The simple landcover in March month specifies the effect of best simulation on the FWA of TM image pixels while no disturbed vegetations have grown green leaves. This approach enables operational wheat planting area mapping for extensive areas with an almost 95% classification precision to sub-pixel level. Our study area covers 346 km2. comprising whole Luancheng County of Hebei Province. Applying SMA to Landsat/TM cloud-free data acquired on March 21th, 2004, we estimated the areal fraction of wheat cover for the whole County field. The validation against statistical data from the Luancheng County Statistic Bureau indicates that with finite SMA, 92.3% accuracy is gained. Better results were also obtained from the validation against statistical information, for example, 95.8% of wheat-covered area were recognized when heavy haze area-free TM image is applied. A general formula for deriving the fractional wheat planting area mapping provided by SMA is presented, too. It can be concluded that wheat planting area to sub-pixel level in NCP can been operatively monitored with limited endmember SMA in a good precision.

Modeling the soil component temperature distribution is useful to study multi-angular thermal remote sensing. SVAT (soil-plant-atmosphere transfer) model could be a good choice because it can predict canopy temperature distribution. However, most of them, including CUPID model 111. were unable to separate shade soil and sunlit soil. They only gave a single temperature for the soil surface. In this paper, based on the difference of net radiance and evaporation rate between the shade and sunlit soil, an extended model from CUPID was proposed to simultaneously retrieve the shaded temperature and sunlit temperature of soil surface. The comparison showed good agreement between simulated soil temperatures and measured ones.

Dust storms greatly affect the environment and the resources in arid and semi-arid areas. In this paper, we study the process of an Asian dust storm in the north of China in 2005 by means of Terra and Aqua MODIS data, which could monitor dust storms both in daytime and nighttime. We use three thermal infrared (TIR), 8.5 mum, 11 mum, and 12 mum, and a tri-spectral technique presented by Ackerman (1997). We find that combining brightness temperature differences BT8.5- BT11 with negative BT11- BT12 can detect dust storms in China. Furthermore, the pre-processing of images, such as cloud screening, is important for dust storm monitoring. Compared to daytime visible and near infrared RGB images, the TIR method predicted dust storm very well and could also obtain useful information in nighttime. We also analyzed dust storm motion by four MODIS images within 24 hours. Our results showed that the dust storm motions from inter land to ocean and relies on the main wind direction. This information is very important for studying the processes of dust storm.

Measurement of polarized properties of the skylight from the ground is one of an effective means of investigating the optical and physical parameters. A new system to measure the natural skylight polarized radiance distribution has been developed. The system is based on the field spectrometer Analytical Spectral Devices (ASD) with a dichroic linear polarizing filter. With this system sequences of radiance data were obtained which can be determined the linear polarization components of the skylight. The ground-based measurements are compared with simulations based on semi-empirical Rayleigh model.

Although the development of a spectral library has been a hot topic in China and abroad since the '90s, it has defects and cannot meet the demands of theoretical research and application of remote sensing nowadays. The gap is further extended for the featured agricultural remote sensing application in South China. Aiming at establishing a practical spectral library of South China's featured crops (including lichee, longan and sugarcane etc.), crop spectra and their environment parameters, application models are integrated based on Web techniques. The paper concentrates on the spectral data measurement method, store and organization, realization of querying and presentation and Web-interface design. It offers the details of regional featured crop spectra and an application demonstration for remote sensing application of spectral library of featured crops in South China.

Abstract -The spectra of maize field are of great uncertainty duo to the difference in planting date, irrigation condition, fertilizing and soil etc. The spectral library is a quick means acquiring the crop spectrum in a growth stage. The variance of image and in-situ maize spectra is analysized for extracting pure pixels of maize crop. It presents a good result for removing disturbed greensward, residential area, and nursery garden from variance based pure maize pixel classification. Keywords- Image endmember, spectral library, variance, maize, pure pixel, recognition Ⅰ. INTRODUCTION Remote sensing of the extent and distribution of individual crop types has proven useful to a wide range of end-users, including governments, farmers, and scientists (Qi-Jing Liu et al,2005). Maps of cropland distributions are usually generated by supervised classification of multiple Landsat images throughout the growing season. These approaches require amounts of manual interpretation and cloud-free high spatial resolution imagery that are prohibitive for operational implementation over large areas and in multiple years (David B. Lobell,2004). The NASA Moderate Resolution Imaging Spectroradiometer MODIS, including the daily global coverage, moderate spatial resolution (0.25 to 1 km), has rapid availability of various products, and cost-free status may allow for operational mapping of croplands. However, the large size of even the 250-m MODIS data relative to most fields results in MODIS pixels containing mixtures of different fields, crop types, and non-crop surfaces. As a result, approaches that assign a single hard classification to each pixel may be prone to significant errors when mapping crop types ( Fisher, 1997). If the pure spectral pixels of different crops can be gained in finer resolution, a big problem will be solved for mixture pixel unmixing and scaling reversion in quantitative remote sensing application by coarser resolution satellite , for example Landsat TM to Terra MODIS sensor. Maize is one of the most important crops in China. During maize growth with rain season of May to September month every year in North China Plain, it is low available for Landsat TM images. During maize growth of June to September, 2003, there is only a scene of TM image captured while the maize spectrum experiment of ground all-growth period is performing. It is necessary to research the maize spectrum characteristic of each month all over the growth period. Another, the every-day gained MODIS image is becoming an important data source of crop monitoring. The ground orchard, greensward and woodland (using in virescence of urban and road) usually disturb the maize classification by TM image. Therefore, it is of great meaning to extract pure maize pixels for improving the classification of the pure maize pixel recognition for maize condition monitoring and yield estimation by scaling method. In this study, we investigated the impact of greensward

As we don't know clearly about the soil salinity spectral behavior and that it is not easy to obtain adequate data, the Grey System Theory is developed in this study. On the base of studying the spectral characteristics of salt and the Mechanism of surveying salt concentration using remote sensing data, took 9 bands of ASTER bands as the sub-factors and salt concentration as the mother factor. The spectral data was calculated by using field spectral data based on ASTER channel function. The field spectral data was obtained by using ASD(0.35-2.5�m). We got 28 points in Aibi lake in Xinjiang,China, in July,2004. According to the ASTER band series, the correlative degree between reference and salt concentration is 0.761,0.760,0.757,0.741,0.610,0.740,0.740,0.738,0.736 respectively. We can see that ASTER band1 has the greatest correlative degree. Band1 are the sensitive band to soil salinity. The next is band2, then band3. It means that the shorter wavelength, the greater correlation in visual and near visual bands. It accords with that blue band is sensitive to salt. Further we will use ASTER image data to analysis it and establish model to predict it basing on the Grey System Theory.

Soil salinity is a major environmental hazard. For mapping and monitoring the spatial distribution of it using remote sensing data, a thorough understanding of their spectral behaviour is of paramount important. This paper presents an approach to estimating soil salinity through correlation and regression analysis between the EC with R (reflectance), A(log(1/R)),and their derivatives respectively. The field spectral data is obtained by using ASD. We got 28 points in Aibi lake in Xinjiang,China, in July, 2004. The results show that A' yield the highest performance in the calibration stage. The selected wave bands are 0.45-0.51μm,2.295-2.365μm. SEC is 37.3141, R2is 0.48 and a of constant, band1,band2 is 0.056,0.008,0.013. On the whole, we can accept that these two bands are the sensitive bands to soil salinity, and the regression procedure of A' can give a quantitative model for estimating EC. From previous theories and experiment results of other people, soil salinity has a high reflectance in blue band and a carbonate absorption festure is also reported. Our outcome is accord with it. It should be noted that we also need more data to validate it.

In China, one of the most common ecological problems of inland water bodies is represented by the eutrophication which diminishes water quality. And the chlorophyll-laden water becomes an obvious sign. Chlorophyll-a concentration measurement is usually used for assessing tropic status of lakes. The development of spectral resolution enables hyperspectral technology possible to monitor water quality successfully, which is based on developing relationships between radiance/reflectance in single band or band ratios and chlorophyll concentration. In this paper, a spectral unmixing model was established based on single-phase field hyperspectral data. Three data types were supported for this model: original data, normalization data and differential data. Selected end-member from known reflectance spectrum, we retrieved chlorophyll-a concentration. The result shows the spectral unmixing model based on differential data gives the best result. Validated this model and shows a good precision and stabilization. Finally, three-phase field hyperspectral datum were processed and chlorophyll-a concentration was extracted using the best model. The result shows that spectral unmixing model is a feasible model in the practical application of remote sensing water quality monitoring.

It is feasible to retrieve LAI over large area from remote sensing data with physical models;however,it is quite difficult to get accurate LAI and thus limit the remote sensing application without enough prior knowledge due to the underdetermined parameters in the physical inversion models.A spectrum database system of typical objects in China(SpecLib) has been set up recently,which may provide a priori knowledge of typical land cover for LAI inversion.MODIS data is used to retrieve LAI after atmosphere correction,geometrical correction and cloud identification.The SAIL(Scattering by Arbitrarily Inclined Layers) model is applied for the inversion of LAI for MODIS data.The vegetation coverage of the mixed pixels of the MODIS data are calculated based on the TM data sets.The LAIs of pure pixels(computed from the retrieved LAIs and vegetation coverage) are compared with the field measurement data in Luancheng,Heibei Province,China.Meanwhile,the LAIs of pure pixels are also compared with the MODIS LAI data products.The inversion results show that the!SpecLib effectively improved the accuracy of leaf area index inversion.

Due to the failure of Landstat 7 ETM+, Landstat 5 images are widely used in many fields again since May 2003. In this letter, a method, based MODTRAN+TM+topographical maps, was used to solve the preprocessing problems of TM images towards retrieving of the endmembers. It proved effective to retrieve the reflectance values of surface substances and confirm the pure surface endmembers before an in-site spectral experiment. The result was also consistent with actual reflectance of surface substances. A case of retrieving endmembers in Luancheng, China was too demonstrated.

This work presents an application in which field data, TM data and MODIS data are used for mapping LAI of Qianyanzhou in Jiangxi province, south China. The field LAI data are collected by Tracing Radiation and Architecture of Canopies (TRAC). We get the linear relationship between measured LAI and simple ratio vegetation index (SR: the ratio of NIR reflectance and Red reflectance) from the corresponding pixels, then the LAI image from TM data is produced and is referred as LAI ground true for further studies. After comparing and analyzing the different LAI images, it shows that the uncertainties really existed among the different kinds of the LAI images with same resolution, it also reveals that the spatial up-scaling effects are closely related to sub-pixel complicacy.

Usually we use multi-channel image data, such as TM, and empirical relationship, such as NDVI-LAI relation or SR-LAI relation, to estimate LAI. Multi-angular remote sensing data provide more information for canopy structure. This paper presents a method to estimate LAI using multi-angular data and model inversion method. The airborne multi-angular data were acquired by AMTIS (Airborne Multi-angle TIR/VNIR Imaging System), which was a prototype sensor designed by the Institute of Remote Sensing Applications of Chinese Academy of Science. Our study is based on two datasets: one was acquired in Beijing Shunyi in April 11, and the major crop is sparse winter wheat; another was acquired in Haerbin in August 24, and major crops are dense corn and soybean. Both datasets have been geometrically atmospherically corrected. Ground based measurements were carried out during the flight experiment. SAIL model is chosen to predict reflected radiance of a presumed LAI. Various view angles relate to the different components ratio in view field, and the reflected radiance is different accordingly. Hence, a certain LAI value was given, SAIL model predicts a set of reflected radiances of various angles. We compare the model predict radiance with the radiance viewed by an multi-angular sensor, to find the optimized LAI which can make the radiance predicted by the model be closest to the viewed radiance, then take this LAI value as the right value

Crop growth monitoring is one of major directions of remote sensing applications. A widely used way is to draw out the NDVI curve of the interesting region in crop growth seasons, then determine whether crop is good or not according to the characters of the NDVI curve and some empirical knowledge. Whether the pixel used to draw NDVI curve exactly describes the target crop species strictly affects the accuracy of the result. The objective of this research is to design an algorithm to find out pure pixel of target crop species from hyperspectral image. The algorithm is based on the spectral library, and it will obtain the sample spectra from the library. If the library returns zero sample canopy spectra, the algorithm will automatically simulate the sample spectra. Then it will aggregate narrow bands into broad bands to match the sensor bands, and at last compare the pixel spectra with the sample spectra. In this research, we use Hyperion, OMIS and MODIS data of different spatial and spectral resolution and use different methods to calculate distance

In this paper, a generalized split-window method to derive land surface temperature (LST) from MODIS (Moderate Resolution Imaging Spectroradiometer) data is applied. A major problem in land surface temperature inversion is that there are too many unknown variables, especially for MODIS data which is in low resolution, one pixel is a mixture of several cover types. To analysis the uncertainties of the LST retrieval algorithm based on MODIS images, the field measurements, together with fine resolution images, AMTIS (the airborne multi-angle TIR/VNIR imaging system) data and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data have been used

Vegetation indices are subjected to many external perturbations such as soil background variations, atmospheric conditions, geometric registration, and especially sensor viewing geometry. Subsequent use of these indices to estimate crop yield and monitor crops growth would result in substantial uncertainties. To reduce the uncertainties due to sun-view angle variations, some methods mere generated by use the reflectance or albedo generated from the BRDF models. MODIS vegetation composition algorithm uses the empirical BRDF model (developed by Walthall et al. to normalize the sun/view angles to certain angle, and then composite the VI by several day's data. In this paper, we present a new method based on prior knowledge to normalise vegetation index on pure pixels of crops, which can be recognized from MODIS image by high resolution land cover map. We simulated different BRDFs of winter wheat in different grow stages by radiative transfer models, using the plant canopy parameters obtained from prior knowledge. Then, we use this BRDF to normalize vegetation indices. The method was tested by the ground based measurements and MODIS Data. It shows our results are good consistent with the ground based measurements. We compare our methods with the algorithm of MODIS vegetation composition, it proved that the result calculated by our method is in better agreement with the surface reflectance characterizations and our method is more effective to monitor the crop growth in regional scale

The water quality of Taihu Lake is declining due to eutrophication, and the chlorophyll-laden water becomes an obvious sign. As to reflectance spectra of water vary with concentrations of organic and inorganic sediments, in this paper field reflectance spectra have been applied for monitoring the water quality of Taihu Lake, China. As the key-monitoring index, the chlorophyll-a contents were evaluated by linear spectral unmixing using water and chlorophyll-a endmember spectra of known content the results were compared to laboratory analyses of in situ, water samples.

A novel method was proposed to simulate heat fluxes above partially vegetated surfaces. The interaction of the heat fluxes between the two components (soil and foliage) in canopy was modeled using the concept of "micro-advection", which refers to small-scale movement of air in a restricted area. To adjust the heat balance of the local plant-atmosphere system is the main function of the micro-advection in this model, i.e., part of the heat emanated from soil surface is transported to the foliage surface and then consumed by the transpiration effect of the leaves in this process. The magnitude of this part of heat transport between components can be estimated from the temperature gradient between them as well as the diffusion coefficients of them. Therefore, the overall effect of the micro-advection is to diminish the total sensible heat flux and increase the level of total latent heat flux. This model was validated using the data of row crop, and the result showed good agreement with field turbulent measurements.

Land-cover classification represents one of the most fundamental applications of remote sensing, and is widely used to estimate carton stocks and parameters hydrological and biogeochemical models. Several studies reveal that changing the spatial resolution of land-cover maps has important effects on the proportion of a landscape occupied by a particular land cover type. We study the proportions of vegetations based on multi-scale land-cover classifications in the area of Qianyanzhou in the province of Jiangxi in the South China on the base of ground investigations. The viability of coarse spatial resolution data for land-cover classification is evaluated using degraded Landsat Thematic Mapper (TM). The uncertainties of multi-scale land cover classifications are finally analyzed based on the different aggregated TM land-cover maps.

A revised sunlit time computation model based on the DEM data in rough terrain has been developed in This work. In order to improve the accuracy of the calculation, an earth curvature revising factor Q was carried out in the model base on the former researchers. In this study, 6 sample areas of representative terrain types within P. R. China were selected and their spatial distributions of sunlit time on the vernal and autumnal equinoxes, the Summer Solstice and midwinter day in two different resolutions (500 m and 1000 m) were calculated using the model developed in the study. In This work, the calculated results of sunlit time using revised and unrevised models were finely compared and the topographic and spatial-scaling effects on the sunlit time were deeply analyzed through two aspects: the different terrain types and different spatial scales of the original DEM data. The changing rules of sunlit time according to the geomorphology and spatial scale were found out and very significative in applications.

Vegetation Index (VI) and Leaf Area Index (LAI) are very important parameters for crop growth situation monitoring and crop yield estimation. However, it is not easy to get accurate VI or LAI. One of the reasons is because of the difficulty in the inversion of aerosol optical depth (AOD), which is the key factor in atmospheric correction. This study addresses an algorithm of AOD inversion in Agriculture region based on the support of Spectrum database. It is usually supposed that we can get the surface reflectance of blue band for most of the algorithm of AOD inversion. As the Dense Dark Vegetation (DDV) method, the surface reflectance in blue and red bands is calculated from the reflectance at 2.1 or 3.8 mum band. However, it is not easy to retrieve the AOD value of each pixel for a whole satellite image because of the unknown surface reflectance on some regions such as the sparse vegetation area. For agriculture area, the surface reflectance varies from bare soil, sparse vegetation, and then Dense Dark Vegetation, during the whole crop growth period. We have carried out a series of field spectrum measurement during different crop growth period and set up a crop spectrum database. By analyzing the soil, the leaf and the canopy spectra, we selected a set of models to calculate the surface reflectance of agriculture region during different crop growth period, which include bare soil model, sparse vegetation model and continuous vegetation model. Then, the surface condition is put to the atmospheric radiation transfer model to calculate Look-up Table (LUT) for MODIS bands, which is used to retrieve the AOD value of MODIS image. North China Plain is selected as the experiment area, the AOD value measured by sun-photometer is taken as true value to evaluate the inversion algorithm's accuracy and the results show good agreement

In the process of applying the theoretic results of BRDF model study to remote sensing image data, an important step is to extract BRDF features from multiangular images. Because of the limitations of registration, pixel alignment and the intrinsic scale scene, it is necessary to perform spatial average to the georeferenced multiangular images before extracting BRDF feature. Otherwise the feature will no be representative to the surface property. Based on the analysis of geometrical limitations, this paper discussed how the apparent BRDF feature changes from random to order after the spatial average.

Presents a new method to analyze the relation between canopy spectral reflectance and component spectral properties. The polynomial decomposition method differs from linear spectral unmixing because it takes into consideration the multiple scattering inside canopy. It is consistent with physical BRDF models and more flexible because it does not depend on certain assumption on canopy structure. This method is superior for some kinds of canopy whose structure is ambiguous between homogeneous and discrete. The output of the analysis is "angular-structural coefficients" which is possible to be related directly to canopy biophysical parameters.

A series of environmental and resource problems have merged due to the rapid urban development, including the encroachment on agricultural land, land reclamation and silt deposition in rivers. The paper has demonstrated that remote sensing can be used to monitor dynamic changes of the coastal areas, such as coastline move and urban expansion, land use, shoals and deep channels. Remote sensing data from 1978 to 1998 are used to reveal the rapid changes that have taken place in the study area. Geographic information systems are also used to assist planners in the analysis of such changes.

The purpose of this paper is to validate the BEPS model in crops for net primary productivity (NPP) estimation and to study the spatial scaling effects of NPP using both airborne and field data. The results show that the highest differences between modeled NPP at resolution 15 m and 30 m are greater than those re-sampled from modeled NPP at 3 m resolution, especially at the boundary of winter wheat.

Data experiment of Mont-Carlo directional radiation transfer model for continuous vegetation was made to decide the optimum view angle of thermal temperature for reliable estimation of sensible heat flux. The true heat fluxes were simulated with classical two-layer model. The conclusions of this study are: 1) the optimum view angle varies within a large range according to the change of leaf area index, leaf angle distribution, soil moisture and other parameters, so it's difficult to define a universal optimum angle; 2) however, the fractional coverage of vegetation in FOV (field of view) under optimum angle is relatively stable and could be used in a new corrective method.

In this paper, temperature/vegetation index relation is used to evaluate soil moisture conditions by 8 km, 10-day composite AVHRR data set. To define the T/NDVI slope, window size and automatic linear fit are studied. Then T/NDVI slope is analyzed spatially and temporally. Results show that soil moisture is closely correlated to the T/NDVI slope. A new approach is proposed to estimate soil moisture availability by T/NDVI slope and T/NDVI space. Finally, soil moisture and drought distribution maps are produced in China.

In order to retrieve land surface component temperature from multi-spectral remote sensing images, such as ASTER, we choose a component equivalent emissivity model, and iterative linear regression inversion algorithm. The method is tested with ASTER image of VNIR and TIR channels, as well as supplementary and validation data acquired from ground experiment. The atmospheric effect is corrected with the dark-object method; surface structural information is derived from ASTER VNIR observations; component emissivity is measured in situ with the BOMEN MR-154 spectrometer; validation data are also measured in ground experiments. Finally, the accuracy of the results and sources of error are analyzed.

A couple-inversion algorithm that retrieves geophysical parameters from MODIS measurements was developed. The retrieved geophysical parameters include atmospheric temperature-humidity profiles, pixel-averaged surface temperature and emissivity within the thermal infrared regions (6/spl sim/16 /spl mu/m). The Modtran atmospheric radiative transfer code was used to simulate the measured radiances and atmospheric transmittance. Then the genetic algorithm was employed to generate a regularization solution that updates the first-guesses of atmospheric temperature, water-vapor profiles, surface skin temperature and emissivity. The algorithm proposed in this paper was first tested with simulated data.

In order to determine surface temperature at large scale from space, a new definition of effective emissivity has been proposed for the whole pixel area for heterogeneous and non-isothermal surfaces. This new effective emissivity depends on the structure of the whole pixel, the optical features of components in the pixel. In order to further illustrate the new effective emissivity, the continuous canopy vegetation is taken as an example, its approximate expression of effective emissivity has been studied by the aid of the Monte Carlo algorithm.

Usually a vegetated area pixel is composed of the basic components, soil, leaf with different proportions, while soil and leaf have different water content. There are lots of canopy models to describe the mechanism with which these component spectra compose the canopy spectrum. For practicability, a simplified model is suggested to calculate the surface vegetation coverage, and water contents of leaf and soil from pixel reflectance, such as multi-spectral remote sensing data. Experiment results show that the accuracy of this method can satisfy the application's request.

In order to evaluate and improve the remotely sensed land surface parameters' accuracy and assimilate accumulating remote sensing data with land surface models, an integrate field campaign was carried out during the winter wheat growth season in 2001. The field campaign was funded by the Chinese Special Funds for Major State Basic Research Project: Quantitative remote sensing theory and application of land surface parameters (QRSLSP). Research on scale effect and angular characteristics of remote sensing data is the most important objectivity. Three different scales of research area are responding to different spatial resolution of remote sensing sensors. The experimental database include space-borne remote sensing data, airborne remote sensing data and field observation data. Some significant research advances have been achieved based the experiment data set, while most research proposals are carrying on.

The classical Shuttleworth-Wallace two-layer model has a modification version proposed by Norman et al. (1995) and Kustas et al. (1999) to estimate evapotranspiration over row crop surfaces. However, the assumption of this modified model is controvertible, and more theoretical analysis and data validation seem necessary to figure out whether this model is a "layer" model or a "patch" model essentially. A field data validation of this kind was carried out in this paper and it was found that a "patch" method could provide more accurate results. A new method to improve S-W model for row crop was given at the end of this paper. This model uses "layer" method to account for the in-canopy exchanges and modified surface resistances to account for the heterogeneities of row crops. The data from the composite remote sensing experiment in Shunyi, Beijing April 2001 were used to validate this model. Surface fluxes were measured by Bowen ratio system while surface radiometric temperature; component temperatures and other parameters were also measured at different atmospheric boundary layer conditions. Results show that estimation of sensible and latent beat fluxes by the new model agree well with the observed fluxes.