Reconstruction of land surface temperature (LST) under clouds has been an area of significant research interest in recent years. Solar-cloud-satellite geometry has significant impacts on satellite-derived land surface biophysical parameters, such as radiation flux and LST; however, current studies often neglect these influences on reconstruction of cloudy LST. To address this challenge, we developed an integrated methodology for generating seamless all-weather LST based on surface energy balance (SEB) theory with consideration of the solar-cloud-satellite geometry effects both on LST and radiation. Cloudy pixels were categorized (radiation-unobstructed and radiation-obstructed clouds) and reconstructed separately to account for geometry effects. Moreover, corrections were incorporated to mitigate geometry effects on net surface shortwave radiation (NSSR), the crucial intermediate input data for estimating cloudy LST. Compared to the existing method, validation results using ground measurements from the Surface Radiation Budget (SURFRAD) network demonstrate significant improvements, with average errors decreasing from 5.62 to 1.86 K under radiation-unobstructed conditions and from 3.26 to 1.33 K under radiation-obstructed conditions, respectively. This study contributes valuable insights to reconstructing LST under varying cloudy conditions, indicating the importance of considering geometry effects for robust and reliable cloudy LST assessments.

Land surface temperature (LST) is a crucial parameter driving water and heat exchange at the surface-atmosphere interface. Satellite-derived LST require rigorous validation to ensure its reliability in Earth system modeling and climate change research. To address validation accuracy degradation caused by cloud contamination artifacts and satellite-ground spatiotemporal mismatch errors, conventional mean- and median-based outlier detection methods were commonly used in the validation of satellite-derived LST products using in situ measurements. However, both methods are based solely on the degree of deviation within statistical data itself, without considering the uncertainties associated with satellite-derived and ground-based LST. This limitation could result in biased identification of outliers in satellite-derived LST validation. In this study, an uncertainty-based method was proposed to detect outliers in the validation of MODIS-derived LST using in situ measurements. This method quantifies total LST uncertainty budgets to flag anomalous data points by integrating uncertainties from both satellite retrievals and ground observations. Validation results across SURFRAD sites demonstrate the method’s efficacy when compared with those without outlier detection. Daytime implementation achieves significant root mean squared error (RMSE) reductions, notably at the BND site with a 3.1 K improvement, while nighttime applications yield marginal enhancements (< 0.4 K), reflecting diminished thermal contrast and uncertainty components during nighttime. The uncertainty-based method consistently outperforms conventional mean- and median-based methods during daytime, with RMSE improvements ranging from 0.2 K at DRA to 2.6 K at BND. Site-specific variations highlight the method’s sensitivity to surface heterogeneity and vegetation dynamics. All methods exhibit comparable performance at night (ΔRMSE < 0.15 K).

Cloud base height (CBH) is crucial for determining cloud radiation effects, and uncertainty in CBH retrieval can lead to significant errors in the estimation of surface downwelling longwave radiation (SDLR) under cloudy-sky conditions. This study proposes a new CBH retrieval model utilizing top of atmosphere (TOA) reflectance and brightness temperature observations from the Fengyun-4A (FY-4A) satellite, employing the random forest algorithm. The accurately retrieved CBH is then used to estimate cloudy-sky SDLR, accounting for the radiative effects of the entire cloud layer. The CBH retrieval algorithm relies solely on TOA observations and cloud top parameter information, enabling CBH retrieval even in the absence of cloud optical thickness. The verification results indicate that the newly proposed CBH retrieval algorithm achieves good accuracy, with a bias of ﹣0.19 km and an RMSE of 1.52 km. Compared to existing studies, the new algorithm performs well in both water cloud and ice cloud phases. Based on the accurately retrieved CBH, this study employs an estimation scheme that uses cloud effective temperature to characterize the radiation contribution of the entire cloud layer, enabling accurate estimation of cloudy-sky SDLR. Ground-based measurements from the Baseline Surface Radiation Network (BSRN) and the National Tibetan Plateau Data Center (TPDC) sites were used to validate the SDLR estimation scheme. The results showed that the cloudy-sky SDLR estimated using the retrieved CBH achieved reasonable accuracy, with a bias of 7.15 W/m2and an RMSE of 27.42 W/m2.

Latent heat flux (LE) is pivotal in the regional water-energy nexus, exemplifying complex interplays between atmosphere and land surface. Thermal infrared (TIR) land surface temperature (LST) offers direct and vital information for estimating LE through the single-source energy balance method. Nevertheless, variations in the viewing angles of remote sensing sensors can introduce angular effects in the retrieval of LST, potentially causing significant incompatibility issues in estimating LE. To alleviate this uncertainty, we adopt a viable approach by using two composited LSTs derived from the integration of soil and vegetation component temperatures from Sentinel-3 SLSTR, combined with fraction vegetation coverage (FVC) obtained from both the GEOV2 FVC product and MODIS LAI-derived estimates. This composited LST was subsequently used as one of the inputs of a single-source energy balance system (SEBS) model driven by measured meteorological and ERA5 reanalysis data in Heihe River Basin in China during 2016-2022, respectively. The results demonstrate that two types of composited LST reduced the root mean square error (RMSE) of estimated LE by 4.8 W/m2and 8.8 W/m2, respectively, by using measured meteorological data; and using ERA5 meteorological data, the RMSE was reduced by 6.8 W/m2and 11.0 W/m2, respectively. Regardless of the meteorological data and FVC used, the RMSE for all stations assessed in the study decreased. This indicates that by partially mitigating the angular effects of TIR LST, improvements in TIR-based surface LE estimation can be achieved over regional scales.

Surface soil moisture (SSM) is essential for understanding the interactions between the atmosphere and Earth’s surface. The rapid development of remote sensing technology in recent decades has provided feasible alternatives for SSM retrieval. The triangle model is one such method that uses the relationship between land surface temperature (LST) and vegetation index (VI) on a triangular space to estimate SSM. However, the traditional LST-VI triangle models inherently suffer from two major drawbacks. First, the subjective requirements for a sufficient number of pixels are characterized by a wide range of vegetation and SSM under uniform atmospheric conditions. Second, this is the need for date-to-date calibration. To overcome these limitations, the present study proposed a novel scheme of the feature space, the single pixel in time-series (SPTS) triangle model. The basic assumption of this feature space is that a given satellite pixel for cropland or grassland will undergo distinct vegetation cover and SSM status due to natural growth and soil moisture dynamics over a relatively long period. Unique triangles for 44 sites in two networks of the International Soil Moisture Network (ISMN)—the TxSon (US) dominated by grassland and REMEDHUS (Spain) dominated by cropland—were constructed based on Landsat data over a period of ~10 years (2013–2023). Compared to the traditional triangle model, the proposed model reveals enhanced skills for SSM retrieval, with a decrease in root-mean-square error (RMSE) by 13.5% (~0.050 m3/m3) over the study sites.

Remote sensing-based evapotranspiration (ET) has been widely used in the study of global climate change, water resources management and precision agriculture. However, due to the relative coarser spatial resolution of thermal infrared data obtained by remote sensing, the retrievals of fine resolution ET through different remote sensing-based models were full of challenge. In this paper, a general ET downscaling method based on the land surface temperature-vegetation index (Ts-VI) triangle was proposed. 990 m resolution ET datasets obtained by aggregating 90 m surface energy balance algorithm for land (SEBAL)-derived estimates from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over a spatial dimension of 9.9 km by 9.9 km around the AmeriFlux US-Ne1 site were downscaled to 90 m by using this new proposed Ts-VI-based ET downscaling method. Compared with the original 90 m ASTER ET, the 90 m downscaled ET results had a mean absolute error (MAE) of 19.2~40.2 W/m2, a root mean square error (RMSE) of 28.8~52.0 W/m2and a bias of 1.7~2.4 W/m2.

Land surface temperature plays a crucial role in ecosystem energy balance and material exchanges. Remote sensing is vital for investigating brightness temperature variations. The intricate canopy structure poses challenges, especially with strong directional anisotropy in brightness temperature, leading to assessment inaccuracies. The radiative transfer model provides valuable insights into how canopy structure influences directional brightness temperature (DBT). Traditional models, assuming a turbid medium or randomly distributed ideal geometry, exhibit notable errors. However, the PATH_RT model, incorporating path length distribution, shows commendable performance in the optical domain. To enhance applicability, we modify the PATH_RT model, successfully implementing path length distributions for simulating DBT in the thermal domain. Validation using abstract scenes, cross-validated with SAIL and FRT, and referencing DART, highlights significant improvement attributed to the efficacy of path length distribution.

Evapotranspiration (ET) plays an important role in water resources, crop management and other fields. Thermal infrared (TIR) land surface temperature (LST) provides essential information for estimating ET by using single-source energy balance method. Nevertheless, variations in the viewing angles of remote sensing sensors can introduce angular effects in the retrieval of LST, potentially causing significant incompatibility issues in estimating ET. To alleviate this uncertainty, we adopt a viable approach by using two composited LSTs derived from the integration of soil and vegetation component temperatures based on Sentinel-3 SLSTR and two kinds of fraction vegetation coverage data. This composited LST was subsequently applied in a single-source energy balance system (SEBS) model driven by measured and ERA5 reanalysis meteorological data in Heihe River Basin in China, respectively. The results demonstrate that our improved approach with two kinds of composited LST reduced the root mean square error (RMSE) of estimated ET by 4.84 W/m2 and 8.81 W/m2 using measured meteorological data driven model; and using ERA5 meteorological data driven model, the RMSE was reduced by 6.78 W/m2 and 10.97 W/m2, with a reduction observed at each site.

Temporally incomparability across the scan lines in polar-orbiting satellite-derived land surface temperature (LST) affects their widespread application. Some challenges persist in the existing research on this issue, such as the absence of a universal algorithm applicable for the partly clear-sky condition in the daytime and scale inconsistency of the used datasets, when LST varies non-linearly over time. Given this situation, we proposed an improved approach for temporal normalization, integrating ensemble regression models and a new LST variation Rate Model (RM), which captures typical LST variation characteristics over time during polar-orbiting satellite overpass periods. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) LST data across the Continental United States (CONUS) were collected to investigate its effectiveness. Moreover, cross-validation was conducted using the time-interpolated Geostationary Operational Environmental Satellite 16 (GOES-16) Advanced Baseline Imager (ABI) LST. The Normalized LST had remarkable consistency with the GOES-16 LST, with superior accuracy in contrast with the original LST. The root-mean-squared error (RMSE) was improved by approximately 1.56 K, and bias was enhanced up to 1.80 K. This study exhibited relatively superior performance in terms of quantitative outcomes and spatial distribution of LST compared with the previous studies. These evaluations indicate that the proposed method could be a dependable and general solution for addressing temporal inconsistencies in clear-sky LST during polar-orbiting satellite overpass periods.

Full-spectrum Sun-induced chlorophyll fluorescence (SIF) offers profound physiological insights into plant functional status compared to single-band SIF. We propose a Fourier series-based method (FSM) for retrieving full-spectrum SIF, aiming to address the limitations of existing methods, such as reliance on reflectance training datasets and the limited spectral range of retrieved SIF spectrum. The core principle of the FSM involves modeling reflectance as a wavelength-dependent function, which can be approximated by successive summations using high-order expansions of the Fourier series. The performance of the FSM was thoroughly evaluated through a combination of simulations and field measurements. The findings illustrate FSM’s capability to achieve high-precision full-spectrum SIF retrieval, with an average relative root-mean-square error (RRMSE) of 2.468% based on synthetic data. Moreover, the corresponding RRMSE values in the O2-A and O2-B bands, at 1.1% and 3.724%, respectively, indicate accuracy comparable to the spectral fitting method (SFM) and advanced FSR (aFSR) methods and superior to the SpecFit method. In the field full-spectrum SIF retrieval, FSM exhibited improved reflectance reconstruction and produced more reasonable results for the diurnal variation of full-spectrum SIF. The diurnal comparison of single-band SIF at both Italian and German sites further highlights the close alignment between FSM-retrieved SIF and the SFM SIF, with$R^{2}$values exceeding 0.96 and a maximum RMSE of 0.118 mW/m2/sr/nm. Conversely, the aFSR method encountered challenges stemming from an under-representation of the training dataset, resulting in the maximum RMSE at the Italian site reaching 0.506 mW/m2/sr/nm, along with a minimum$R^{2}$of 0.809. The FSM demonstrates the promising potential for full-spectrum SIF retrieval, accompanied by fewer limitations.

Land surface temperature (LST) is a critical parameter in global long-term meteorological and climatological studies. The Visible and Infrared Radiometer (VIRR) sensor aboard the Chinese Fengyun-3 (FY-3) series satellites provides a continuous collection of thermal infrared (TIR) data, facilitating the generation of global long-term LST products. Notably, the FY-3C VIRR has served as a key instrument in collecting global TIR data since 2013. In this study, we proposed an operational split-window (SW) algorithm for retrieving LST from FY-3C VIRR TIR data. Initially, the Thermodynamic Initial Guess Retrieval 2000 atmospheric profile library, the atmospheric transfer model MODTRAN, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral library were employed to construct a simulation database for fitting the algorithm coefficients. To enhance the accuracy of the SW algorithm, LST, atmospheric water vapor content (WVC), and average emissivity were segmented into various subranges. Subsequently, land surface emissivity (LSE) was dynamically estimated by combining the ASTER Global Emissivity Database (GED) with the normalized difference vegetation index threshold approach. Finally, in situ measurements from the Surface Radiation Budget (SURFRAD) network, along with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST products (MOD11A1 and MOD21A1), were utilized to evaluate the accuracy of the retrieved LSTs. The results indicate: 1) the retrieved LSTs showed a high correlation with the in situ LSTs, with a coefficient of determination of 0.94, a root-mean-square error (RMSE) of 2.6 K, and a bias of 0.3 K; 2) the retrieved LSTs were consistent with MODIS LST products, showing a root-mean-square difference (RMSD) of approximately 2.4 K; and 3) compared to the result of MOD11A1, MOD21A1 exhibited a significantly smaller bias. These results indicated that the proposed algorithm is effectively capable of estimating global LST from FY-3C VIRR TIR data with reasonable accuracy.

Evapotranspiration (ET) is an essential parameter in the water cycle and surface energy balance. Accurate estimation of daytime or daily ET is of great significance for many activities in human economic and social development. This study aims to propose a novel method for estimating daytime average ET by using temporal measurements of land surface temperature (LST) and net surface shortwave radiation (NSSR), following a previous developed elliptical relationship between diurnal cycles of LST and NSSR under cloud-free days. The method was primarily developed from the simulated data of a physics-based Atmosphere–Land Exchange (ALEX) model under different underlying surfaces and atmospheric conditions. Based on the simulated data, the proposed method showed considerable accuracy with the overall coefficient of determination (R2) of 0.958 and the root mean square error (RMSE) of 25.3 Wm-2. In addition, ground ET measurements at four Ameriflux sites (US-ARM, US-SRM, US-Whs, and US-Wkg) during the 2018 growing season were collected to assess the estimated daytime average ET. Results show an overall RMSE of 64.7 Wm-2for the estimated ET at the four sites, and the US-Whs site reveals a best accuracy (R2=0.825, RMSE=44.4 Wm-2). These results indicated a potential for generating daytime ET with geostationary satellite observations at regional scales in future development.

It is urgently needed to obtain the hourly near-real-time all-weather land surface temperature (NRT-AW LST) for immediately monitoring the disaster and environmental changes. Nevertheless, studies on estimating hourly NRT-AW LST are in the preliminary stage. In this study, we proposed a Spatio-TEmporal Fusion (STEF) method for fusing the reanalysis dataset derived from China Land Surface Data Assimilation System (CLDAS) and thermal infrared (TIR) data derived from the Chinese Fengyun-4A (FY-4A) geostationary satellite to estimate the hourly NRT-AW LST with 0.04° resolution. STEF method can produce NRT-AW LST without relying on the data after the target moment. STEF is tested in the Tibetan Plateau. Validation results on DOY 215-366 of 2020 indicate that STEF has good accuracy: RMSEs (MBEs) under clear-sky, cloudy-sky, and all-weather conditions vary from 2.74 K (-1.06 K) to 3.77 K (0.14 K), from 3.31 K (-1.40 K) to 4.46 K (-0.22 K), and from 3.10 K (-1.11 K) to 3.87 K (-0.22 K), respectively. STEF method can improve the accuracies of FY-4A LST, and RMSEs are reduced by about 0.77 K to 1.82 K. The NRT-AW LSTs estimated by STEF have better accuracies than CLDAS LSTs under all-weather conditions. The SETF also exhibited similar results in 2021. We believe that the proposed STEF method can meet the requirements of NRT-AW LST estimation and contributes to improving the timeliness of region monitoring and related parameter estimations.

Land surface temperature (LST) is crucial in many global and regional scientific studies and applications. The observation time differs along the scan line due to the intrinsic scanning characteristics of instruments, making satellite-derived LSTs incomparable. Although many algorithms have been developed to address this issue, they have many limitations and uncertainties in application. On the basis of the temporal evolution of clear-sky LST, this study proposed a hybrid and practical algorithm with good applicability for the temporal normalization of satellite-derived LSTs. The proposed algorithm was applied based on Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data across the Continental United States (CONUS) in 2020 and mainly validated by cross-comparisons with the Geostationary Operational Environmental Satellite R-Series 16 (GOES-R16) Advanced Baseline Imager (ABI) LST product over each season and various land cover types. The normalized MODIS LSTs had a superior agreement with the GOES-R16 LSTs. Especially for the temporal differences (original observation time minus the reference time) between 0.5 and 1.0 h, the root-mean-square error (RMSE) and bias of the normalized LSTs were improved by 0.32 K-1.03 K and 0.30 K-1.27 K, respectively. These results demonstrate that the proposed hybrid method has advanced potential for the temporal normalization of polar-orbiting satellite LST.

Land surface temperature (LST) is an important physical parameter at the interface between the Earth’s surface and the atmosphere. Accurately quantifying LST uncertainty is essential for the generation of a long-term and consistent LST Climate Data Record (CDR) or Earth System Data Record (ESDR) from either multiple sensors or algorithms. In this study, a physical-based method was proposed to quantify the uncertainty of LST retrieval from satellite thermal infrared (TIR) data using the generalized split-window (GSW) algorithm. LST uncertainties were parameterized as a function of brightness temperature at the top of the atmosphere (TOA) and surface emissivity in two split-window channels, which are two key input parameters in the GSW algorithm, as well as their uncertainties. The performance of the parameterized uncertainty model was evaluated according to the simulation dataset at six prescribed viewing zenith angles (VZAs) of 0°, 33.56°, 44.42°, 51.32°, 56.25°, and 60°, with a root mean squared error (RMSE) of 0.001 K. The coefficients of the parameterized uncertainty model at arbitrary VZA within a sensor’s field of view (FOV) can be obtained by linear interpolation of the coefficients at the six prescribed VZAs. Once the coefficients of the parameterized uncertainty model for each pixel are available, total LST uncertainties can be quantified on a pixel-by-pixel basis. As an example, the parameterized uncertainty model was applied to actual MODIS data for displaying the spatial distribution of LST uncertainties. The results indicate that the parameterized uncertainty model can characterize the spatial variation in LST uncertainties well over various land cover types.

The monthly mean land surface temperature (MMLST) reflects more stable intra- and interannual temperature variations, and therefore, it has a wider range of applications than instantaneous land surface temperature (LST). This study aimed to generate a high-resolution global MMLST product by temporally upscaling the Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km instantaneous LST. First, six current methods were comprehensively evaluated using cross-validation technology. These six methods are the cross combinations of two temporal aggregation schemes: the average by observations (ABO) and average by days (ABD), and three conversion models: the diurnal temperature cycle model (DTC), the simple average of two instantaneous LSTs (TSA), and a weighted average model for multiple instantaneous LSTs (MWA). The analysis with measurements from 235 flux stations worldwide revealed that the choice of conversion model considerably affected the overall retrieval accuracy, whereas the influence of the aggregation scheme was minor. From the conversion model standpoint, MWA performed best, followed by DTC, and finally TSA; this order remained the same even if DTC and TSA were improved with mean bias correction. Notably, the errors of ABDMWA decreased as the number of daily mean LST (NOD) increased, whereas the errors of ABOMWA were not related to NOD. Accordingly, we deduced that the optimal strategy for estimating MMLST is using ABOMWA when NOD is$\ge 20$. Subsequently, we adopted this combination method to process MODIS instantaneous LSTs and produced a global 1-km MMLST dataset for the years 2003–2020. The validation showed a satisfactory accuracy with a root mean square error (RMSE) of 1.6 K. The intercomparison with MMLSTs from geostationary (GEO) satellites (containing complete LST daily cycle) presented a good agreement (biases < 0.3 K and STDs < 2 K). Compared with atmospheric infrared sounder (AIRS) L3 monthly standard physical retrieval (AIRS3STM) product which had the same temporal span, the newly generated product exhibited a high consistency in reflecting temporal variations of global temperature. Most importantly, it had a prominently better ability to retrieve spatial details of temperature variations due to its higher resolution. Our new method and product show promising prospects for applications in global change studies, where accurate spatially resolved MMLST data are one of the fundamental geophysical variables required.

Satellite-derived high-resolution LST observations are essential for environmental studies. However, the tradeoff between spatial and temporal resolutions largely restricts the application of current LST products. As a consequence, many spatial downscaling or spatiotemporal fusion methods were proposed to overcome this limitation. In this paper, we design a novel empirical weighting method to combine the results from the popular downscaling and fusion methods, thermal sharpening algorithm (TsHARP), and spatial and temporal adaptive reflectance fusion model (STARFM). Specifically, the error of the two methods are firstly estimated and the predictions are blended based on the inverse ratio of the corresponding error. Our method is tested with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) data in Beijing. Compared with the actual ASTER LST, the combining results could both enhance the accuracy and structure similarity, as our method utilizes spatial-temporal-spectral information. Moreover, our method also has the potential for generating more accurate daily high-resolution LSTs.

Thermal infrared (TIR) land surface temperature (LST) products derived from geostationary satellites have a high temporal resolution in a diurnal cycle, but they have many missing values under cloudy-sky conditions. Therefore, it is pressing to obtain all-weather LST (AW LST) with a high temporal resolution by filling the gap of TIR LST. In this study, a method integrating reanalysis data and TIR data from geostationary satellites (RTG) was proposed for reconstructing hourly AW LST. Then, taking the Tibetan Plateau, which is a focus of climate change as a case, RTG was applied to the Chinese Fengyun-4A (FY-4A) TIR LST and China Land Surface Data Assimilation System (CLDAS) data. Validation based on thein-situLST shows that the accuracy of the AW LST is better than the FY-4A LST and CLDAS LST under clear-sky, cloudy-sky, and all-weather conditions. The mean RMSEs are 3.02 K for clear-sky conditions, 3.94 K for cloudy-sky conditions, and 3.57 K for all-weather conditions. Uncertainty and coarse resolution of the original FY-4A and CLDAS data affect the accuracy of the obtained AW LST. The results of the LST time series comparison also show that the reconstructed AW LST is consistent within-situLST. The reconstructed AW LST also has good image quality and provides reliable spatial patterns. RTG is practical in obtaining high temporal resolution AW LST from the Chinese FY-4A to satisfy related applications. It can also be extended to other geostationary satellites and reanalysis datasets.

A new two-step physical method was developed to retrieve the land surface temperature (LST) from infrared atmospheric sounding interferometer (IASI) observations. This method relinearized the radiative transfer equation (RTE) by the tangents around the initial estimates of the LST, land surface emissivity (LSE), atmospheric equivalent temperature ($Ta$), and water vapor content ($q$). The Tikhonov regularization method and discrepancy principle (DP) iteration algorithm were employed to stabilize the ill-posed problem and obtain the final maximum likelihood solution of the LST with updating the initial estimation of LST, LSE,$Ta$, and$q$. A new channel selection scheme was proposed for this physical method to obtain an accurate LST estimation. This physical-based algorithm was tested on both simulated and real data obtained from the IASI. The root-mean-square error (RMSE) of the simulated LST is ~1 K based on an initial LST estimate with an RMSE of 2 K (1.9 K). The sensitivity analysis shows that the LST retrieval accuracy is ~1 K based on an LST with a random error of 3 K, constant initial LSE (0.97), 10%$Ta$error, and 40%$q$error. Compared with the Advanced Very High Resolution Radiometer onboard Metop (AVHRR/Metop) LST product, the physical method achieves the LST retrieval accuracy of 1.5 and 1 K for real daytime and nighttime IASI data obtained in the study area. Based on the new method, the LST can be retrieved with an accuracy similar to that of the AVHRR/Metop LST product.

Trapezoidal configurations constituted by land surface temperature and fractional vegetation cover has been frequently used to estimate surface soil moisture (SSM). Determination of the SSM status over the trapezoidal dry and wet edges is required to decouple the volumetric SSM content from the trapezoid-derived M0 because of the coupling of volumetric SSM content and soil texture (i.e., soil moisture availability,M0). Currently, soil hydraulic characteristics generated from soil pedotransfer functions (PTF) provide a preferred solution for describing the SSM status over trapezoidal dry and wet edges; however, most PTF have been developed from laboratory-based soil measurements which have not been fully integrated into remote sensing models for SSM retrieval. This study investigated a practical calibration approach for PTF-derived soil hydraulic characteristics to enhance SSM retrieval using these trapezoidal configurations. Three years of high-resolution SSM measurements were estimated using trapezoidal configurations with Landsat-8 data over a semi-arid network in Spain. For the uncalibrated trapezoid, fair accuracy with a root mean square error (RMSE) of 0.062 m3/m3and bias of 0.040 m3/m3was achieved when compared with in situ measurements. Furthermore, a practical PTF-calibration approach with local measurements was proposed and subsequently integrated into the trapezoid to obtain SSM values. Our results indicated enhanced SSM estimates with an RMSE of 0.050 m3/m3and bias of 0.012 m3/m3with the calibrated PTF. Finally, we found that the calibrated trapezoid can eliminate overestimation and underestimation when the SSM was lower or higher, respectively, which occurred frequently for optical SSM retrievals.

Accurate retrieval of land surface temperature (LST) over urban areas is of great significance for urban thermal environment monitoring. In previous studies, most of the urban LST retrieval methods were developed based on the assumption of a flat surface without considering the influence of urban 3-D geometry structure, which has a significant impact on the retrieval accuracy of LST over urban areas. In this study, a radiative transfer equation (RTE)-based single-channel method was developed to retrieve LST with urban geometry effect correction from the Landsat 8 thermal infrared (TIR) data in band 10. The increase in adjacent radiance from the surrounding pixels and the decrease in atmospheric downwelling radiance caused by urban geometry structure were taken into account in this method. Because it is difficult to directly validate the retrieval accuracy of LST over urban areas usingin situLST measurements, the performance of the RTE-based LST retrieval method was evaluated via comparing brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the discrete anisotropic radiative transfer (DART) model and the urban RTE over three subregions. There is a good agreement between BT at the TOA simulated by the DART model and the urban RTE, with a root-mean-squared error (RMSE) of less than 0.25 K. The variations in LST retrieved with urban geometry effect correction over different local climate zones (LCZs) were analyzed. In general, built-up LCZs have relatively higher LST than land cover LCZs. The differences between LST retrieved without/with urban geometry effect correction over different LCZs are greater than 0.2 K. The largest average LST difference over built-up LCZs is approximately 0.9 K, whereas that over land cover LCZs is approximately 0.65 K. LST retrieved without/with urban geometry effect correction was used to calculate urban heat island intensity (UHII) in terms of the LCZ-based method. The results indicate that UHII calculated from LST with urban geometry effect correction is lower than that calculated from LST without urban geometry effect correction, with an average difference of approximately 0.5 K.

The derivation of microwave land surface emissivity (MLSE) under various weather conditions from the microwave radiometer plays a crucial role in acquiring land surface and atmospheric parameters. Nevertheless, currently, most existing studies mainly focus on the clear-sky scenarios owing to a lack of cloudy-sky land surface temperature (LST) and uncertainties in simulating the scattering and emission properties of atmospheric hydrometeors. Under this background, with satellite observations and the random forest (RF) model, this study proposes a method to estimate the MLSE under cloudy skies. First, clear-sky MLSEs with satisfactory accuracy are retrieved by using the brightness temperatures (BTs) from the Advanced Microwave Scanning Radiometer-Earth sensor, LSTs from the Moderate Resolution Imaging Spectroradiometer, and atmospheric profiles from the ERA5 reanalysis. Then, the relation among the clear-sky MLSE and related impact factors is built with the RF and extended to the cloudy-sky environment for generating all-weather MLSEs with a 0.25°. The results show that the input datasets present a considerable impact on the calculation of instantaneous MLSE, and a 5.73 K bias of ERA5 LST may generate a 0.014-0.021 error in the MLSE from 6.9 to 89 GHz horizontal polarization, while the impacts of BT and profile uncertainties on the MLSE are smaller. The retrieved clear-sky MLSE is coincident with the existing MLSE for the spatiotemporal variations, and there is an average difference range from -0.035 to 0.035 in January 2008. Meanwhile, the constructed RF model can successfully apply to cloudy-sky status and recover the MLSE image gaps affected by cloud contamination.

Land surface temperature (LST) and soil moisture (SM) are two important parameters in land surface ecosystem at regional and global scale. The accurate acquisition of LST and SM can benefit various fields, including agriculture and climate which are closely related to human life. This study proposed a simultaneous retrieval method of LST and SM based on the approximate and correction of passive microwave radiation transfer equation. Compared to LST and SM in simulated database, the accuracy of retrieved LST is approximately 1.63 K and the accuracy of retrieved SM is about 0.063 m3/m3.

Land Surface Emissivity (LSE) is an important parameter in thermal infrared remote sensing, which is of great significance to temperature inversion. In this study, the Gradient Boost Regression Tree (GBRT) was proposed to directly retrieve LSEs of MODIS thermal infrared channels 29$(8.4-8.7\ \mu \mathrm{m}), 31(10.78-11.28\ \mu \mathrm{m})$, and 32 ($11.77-12.27\ \mu \mathrm{m}$) from the visible and near infrared (VNIR) data. We selected the variables related with LSE, including reflectivity, view zenith, solar zenith, land surface type, vegetation index (EVI), Normalized Difference Water Index (NDWI) and Leaf Area Index (LAI). The results of the test set showed that RMSEs of the estimated LSEs were 0.013 in channel 29, 0.005 in 31 and 0.004 in 32, which were more accurate than existing methods. Eight regions with different ground features were also selected to further evaluate the applicability of the model. In most areas, the RMSEs were below 0.015 in channel 29, below 0.005 in channel 31 and 32. In addition, the spatial distributions of the estimated LSEs and those extracted from MYD11B1 and MYD21A1D in H19V08 were compared, which were also reasonable. In general, it is feasible to use the selected variables with the GBRT model to directly retrieve the LSEs.

There have been considerable interests in the lunar-based synthetic aperture radar (LBSAR) for monitoring large-scale geoscience phenomena. However, the signal distortions given rise by lunar orbital perturbations, especially the apsidal precession, are particularly severe in the LBSAR. The apsidal precession effects can induce a coordinate drift of the LBSAR, which can further lead to the variation in the range history of the LBSAR. As a result, LBSAR’s image performance might be affected. In this letter, we thoroughly investigate whether the apsidal precession effects cause the phase decorrelation in the signal of the LBSAR, and how such effects impact the LBSAR imaging. The theoretical result shows that the impact of the lunar apsidal precession mainly results in the first-order and second-order Doppler errors, which further influence the geometric location and focusing quality along the azimuth direction. Numerical simulations using the point target response show good consistency with the theoretical analysis. To this end, the lunar apsidal precession effects deserve special care in the LBSAR for high imaging quality.

Numerical simulation of random rough surface finds wide applications in scientific disciplines, e.g., radar remote sensing of terrain and sea. In scattering simulation of rough surface, not only energy conservation must be ensured, but also, perhaps equally important, the surface inherent properties must be preserved. However, the proper choice of surface and grid sizes that are statistically representative poses a problematic issue. This study applied the entropy measure to determine such parameter settings by examining the relative error of sample entropy associated with roughness parameters and by noticing the fact that a rough surface with certain roughness parameters, including power spectrum density function, must have unique sample entropy. It is found that if the two criteria are met, proper choice of surface length and grid size is attainable to warrant minimum uncertainties of rough surfaces and maximum information content for different roughness spectra density functions under different correlation lengths. The feasibility and superiority of the proposed entropy-based method are validated in terms of minimum error of roughness parameters and also the energy conservation in bistatic scattering coefficients of rough surfaces generated using obtained simulation parameters.

Relative humidity (RH) is one of the key variables for understanding the water, energy, and carbon exchange between the Earth and the atmosphere. Traditional methods for deriving RH from remotely sensed data usually require ground meteorological observations or are limited to clear-sky conditions, thereby making it a significant challenge to obtain spatially complete RH under all-sky conditions, especially over the regions with sparse meteorological instruments for observation. To this end, a new approach for deriving all-sky RH entirely based on Moderate Resolution Imaging Spectroradiometer (MODIS) data was proposed in the present study. Two key assumptions in the approach under cloudy conditions are that the actual water vapor is linearly related to the total precipitable water vapor (PWV) and that air temperature is linearly related to land surface temperature (LST). Results from a total of 30 AmeriFlux stations proved the aforementioned assumptions based on MODIS data collected over a study period of three years from 2009 to 2011. For different aridity conditions, RH retrieval revealed reasonable accuracy with a root-mean-square error (RMSE) of approximately 15.3% over an arid and semiarid region, whereas a comparable RMSE of 17.0% was obtained over a humid area. Further results also indicated that the aforementioned linear relationships were generally temporally stable, thereby indicating that the proposed method can be used to obtain all-sky RH at a regional or global scale entirely based on MOD06_L2-derived LST and MOD05_L2-derived PWV data given that the assumed linear relationships can be easily determined by historical MOD07_L2-derived atmospheric profiles.

Net surface shortwave radiation (NSSR) is a key parameter that drives the surface material exchange and energy balance. Herein, we propose an improved artificial neuron network (ANN) with parameterized (ANN-P) method to first calculate the albedo at the top of atmosphere (TOA) by considering the surface non-Lambertian effect. Subsequently, the NSSR is estimated based on the relationship between TOA broadband albedo and the Earth's surface-absorbed shortwave radiation using a parameterized method under clear sky. The modeling process is implemented with Chinese Gaofen-5 (GF-5) visible/near-infrared channels data simulated via MODTRAN. For comparison, a previously reported lookup table (LUT) with parameterized (LUT-P) method and an ANN method are also employed. The performances of all these methods are evaluated. In terms of model simulation part, the root-mean-square errors (RMSEs) are 15.01 (17.07), 10.04 (13.67), and 20.39 (29.99) W/m2for land, water, and snow/ice surfaces, respectively, for the ANN-P (versus LUT-P) method. Their mean bias errors (MBEs) are within 0.9 W/m2. With respect to the direct ANN method, it shows the highest accuracy yet relatively large deviation for water surface. Additionally, the sensitivity analysis of water vapor content (WVC) confirms that the ANN-P method is more stable than the LUT-P and ANN methods and is, thereby, recommended for clear-sky NSSR estimation. Finally, the ground validations indicate that the mean RMSEs (MBEs) for the LUT-P, ANN-P, and ANN methods are 49.33 (-3.01), 47.55 (1.75), and 104.24 (-75.72) W/m2, respectively.

This article presents a new index, polarization-conversion ratio (PCR) to characterize depolarized bistatic scattering from rough surfaces with dielectric inhomogeneity and spatial anisotropy. We then investigate the dependence of PCR on both surface and radar parameters. Numerical results show that the distribution of PCR on the scattering plane varies with the polarization state of the incident wave and incident angle. The PCR clusters more in the cross-plane for horizontally polarized incidence. However, for vertically polarized incidence, the PCR disperses as “triangular shape” on the whole scattering plane with a sharp valley occurring in the incident plane. The following points can be drawn: 1) the inhomogeneity effectively enhances the PCR in the cross-plane; 2) the effect of anisotropy on the PCR is relatively weak, because the scattering is less affected by correlation length; 3) the impacts of surface rms height on the PCR are negative on the whole scattering plane; and 4) as the background permittivity increases, at the horizontally polarized incidence, the PCR is enhanced in the backward and forward regions, while at vertically polarized incidence, it is enhanced in the incident plane and the forward region. As is demonstrated, the PCR is an effective measure of the sensitivity of depolarization, making it potentially useful as a new reliable index for surface parameter inversion.

Accurate estimation of land surface temperature (LST) is crucial for ecological environment monitoring and climate change studies in mountainous areas. The current LST retrieval algorithms were developed without accounting for the topographic effect, which can only be used to retrieve LST over relatively flat surfaces. Due to the impact of 3-D structure of mountainous surfaces, rugged terrain makes the processes of thermal radiation more complex. In this study, a radiative transfer equation (RTE)-based single-channel algorithm was proposed to retrieve LST with topographic effect correction from the Landsat 8 thermal infrared (TIR) data in mountainous areas. This algorithm accounts for the changes in the thermal radiation components in the TIR RTE caused by the topographic effect. According to the analysis of simulation data, sky-view factor (SVF), atmospheric water vapor content, surface emissivity of target pixel, and average LST of the surrounding terrain have significant influence on the magnitude of the topographic effect. The differences between the LST retrieved without/with topographic effect correction from the Landsat 8 TIR data are related to SVF. The topographic effect should be taken into account in the LST retrieval algorithm when SVF is smaller than 0.7. The largest LST difference of approximately 1 K occurs in the deep valley. The results indicate that LST without topographic effect correction could be overestimated to be as high as 1 K. Due to a lack ofin situLST measurements, the performance of the LST retrieval algorithm in mountainous areas was only evaluated by comparing the brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the DART+MODTRAN model and the TIR RTE over mountainous surfaces at three subregions. There is a good consistency between BT at the TOA simulated by the DART+MODTRAN model and the TIR RTE over mountainous surfaces at the three subregions, with a root-mean-squared error (RMSE) of less than 0.23 K.

Fine spatial resolution land surface temperature (LST) data derived from a thermal infrared remote sensing image are essential to the study of land surface energy, water and carbon cycles. As an alternative and effective way to obtain fine spatial resolution LST, a large number of LST downscaling methods have been proposed in recent decades to enhance coarse resolution LST to fine resolution. However, the drawbacks of the random selection of scaling factors and the establishment of statistical regression relationships are obvious. In this context, a general and physical LST downscaling method based on surface energy balance (DTsEB) is proposed in this study. Moderate Resolution Imaging Spectroradiometer (MODIS) LST data at 990 m spatial resolution were downscaled to 90 m by using this new proposed SEB-based LST downscaling method in this study. Compared with the concurrent 90 m resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST data, the downscaled results have a mean absolute error (MAE) of 1.37 K and a root mean square error (RMSE) of 1.84 K.

The tradeoff between spatial and temporal resolution in satellite observations substantially restrains the potential applications of Land Surface Temperature (LST) products. So far, many spatiotemporal fusion models have been developed to address the issue and a unified comparison in LST data fusion is still required. In this paper, four popular spatiotemporal fusion algorithms including Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Unmixing-based data fusion method, Flexible Spatiotemporal Data Fusion (FSDAF) and Spatio-Temporal Integrated Temperature Fusion Model (STITFM) were adopted to generate high spatial resolution LST using polar-orbiting and geostationary satellite data. The predicted LST was evaluated by the actual LST product and the result indicates that the overall accuracy of FSDAF is satisfied (about 2.87K) and the FSDAF algorithm is recommended to generate LSTs at high spatial and temporal resolution in heterogeneous area.

Evapotranspiration (ET) is one of the most critical parameters in water- and energy-related domains. Two basic assumptions with respect to soil-moisture variation have been widely investigated for the retrieval of ET based on the trapezoid methods. Specifically, soil moisture within the surface and root-zone layers was assumed to vary synchronously in most of the earlier analyses. However, several recent investigations assumed that soil moisture within the upper soil layer should be dried up before the root-zone layer is stressed. To this end, the retrieval of ET under different aridity conditions over North American grasslands was investigated with the two assumptions, and the estimated ET was assessed using the flux data collected from eight AmeriFlux sites. Based on the available data from 2002 to 2018, results showed that the “asynchronous-assumed” method can obtain better ET estimates than the “synchronous-assumed” method over semiarid and subhumid areas, whereas the “synchronous-assumed” method can obtain better ET estimates in humid areas. Moreover, because of the different closure techniques used for the ET correction, no consistent conclusions could be found for the arid conditions to determine which trapezoid was better. Specifically, it was found that the cases of surface soil with zero water availability that were defined by the asynchronous-assumed trapezoid method rarely occur, even in arid areas, which indicated that the critical boundary that determines whether the root-zone layer begins to be water-stressed may need to be redefined.

Different from bidirectional reflectance, temperature variation takes some time with the change of illumination. However, previous thermal radiation directionality (TRD) models have less considered the influence of this temperature inertia (TI) effect. By using the concept of conversion component, this article proposed an improved geometric optical (GO) model, called MGP_TI model. This model considers the TI effect by further dividing the background component into the continuously sunlit, continuously shaded, converted from sunlit to shaded, and converted from shaded to sunlit backgrounds. Upon combining with in situ measurements and a comprehensive simulated data set of component temperatures and prescribing three levels of TI and six observation times, the TI influence on TRD modeling was comprehensively analyzed. Results indicated that: 1) the overall absolute and relative greatest influence were 0.34 °C and 6.9%, respectively, suggesting that the TI influence on the value of TRD was less significant compared with the land surface temperature (LST) retrieval accuracy and the TRD extent; 2) the TI would weaken TRD on the direction of sun motion, whereas it enhanced the TRD on the opposition direction, and the primary influence was enhancing first and then weakening during the period from 10:30 to 15:30, which were determined by the differences in conversion component fractions; and 3) the TI effect could also result in the delay of the hotspot, and the occurrence and degree of the delay were influenced by the TI strength, local solar time and temperature differences of sunlit/shaded components.

This article attempts to examine the Brewster effect of incoherent scattering from a lossy inhomogeneous rough surface with a vertical dielectric profile. Five typical dielectric profiles are selected for the purpose of illustration. In calculating the reflection coefficients, a transition model is used to convert the angle of incidence to local incidence, which accounts for the surface roughness. Numerical results show that the Brewster angle gradually moves to the large incident angle with an increase in the background dielectric constant and in the surface root-mean-squared (rms) height. The angular dependence of reflection coefficients, both the level and the trend, is slightly affected by the correlation length. The scattering strength is much more sensitive to the rms height than to the correlation length. The results could be useful in the retrieval of vertical soil moisture content when proper radar observation is available.

Land surface temperature (LST) is a key parameter for many fields of study. Currently, LST retrieved from satellite thermal infrared (TIR) measurements is attainable with an accuracy of about 1 K for most natural flat surfaces. However, over urban areas, TIR measurements are influenced by 3-D structures and their radiation that could degrade the performance of existing LST retrieval algorithms. Therefore, quantitative models are needed to investigate such impact. Current 3-D radiative transfer models are generally based on time-consuming numerical integrations whose solutions are not analytical, and are therefore difficult to exploit in the methods of physical retrieval of LST in urban areas. This article proposes an analytical TIR radiative transfer model over urban (ATIMOU) areas that considers the impact of 3-D structures and their radiation. The magnitude of this impact on TIR measurements is investigated in detail, using ATIMOU, under various conditions. Simulations show that failure to acknowledge this impact can potentially introduce a 1.87-K bias to the ground brightness temperature for street canyon whose ratio “wall height/road width” is 2, wall and road temperature is 300 K, wall emissivity is 0.906, and road emissivity is 0.950. This bias reaches 4.60 K if road emissivity decreases to 0.921, and road temperature decreases to 260 K. ATIMOU is also compared to the discrete anisotropic radiative transfer (DART) model. Small mean absolute error of 0.10 K was found between the models regarding the simulated ground brightness temperatures, indicating that ATIMOU is in good agreement with DART.

The change of global thermal environment plays an important role in land surface processes. In this study, global thermal environment was analyzed using the vertically polarized brightness temperature at 36.5 GHz. The daily brightness temperature from 2003 to 2010 were decomposed using the annual temperature cycle (ATC) model, and the annual cycle parameters (ACPs) were obtained. The results show that the brightness temperature decreases with the increasing latitudes respectively for the northern hemisphere and the southern hemisphere. The land covered by vegetation is colder than the desert and barren. Some plateaus lead to lower brightness temperature than surrounding areas. In addition, the atmospheric and ocean circulation also affect global brightness temperature. The ACPs from brightness temperature can generally characterize the global thermal environment.

Passive microwave data are much less affected by clouds than TIR data for the retrieval of land surface temperature (LST), providing its unique advantages in global mapping of LST. In this study, a physically-based algorithm for LST retrieval was applied to AMSR2 global brightness temperature data. The performances of this algorithm applied on different land cover types were further evaluated against nighttime MYD11A1 thermal infrared LST products. The results showed that (i) the overall accuracy of the algorithm is about 5.42 K by root mean square error (RMSE) and 2.99 K by bias against MODIS LST during nighttime; (ii) the algorithm overestimates the LST over all land types. The overestimation is most evident over barren/sparsely vegetated surfaces. The algorithm shows that the algorithm has a robust performance comparing with MODIS LST and could be applied to estimate LST effectively.

Evapotranspiration (ET) is widely considered as one of the key parameters in a variety of practical applications. However, because of the contamination of cloud cover, the retrieval of daily ET from optical remote sensing data under cloudy sky conditions is full of challenge. In this paper, we reconstructed daily ET on cloudy days using the relationship between the potential evapotranspiration ratio (RPET) and the available water fraction (FAW). The field data from 8 Ameriflux sites were chosen to explore this relationship and applied for gap-filling of daily ET at these sites at first. Then MODIS data and meteorological data from Yucheng site in China were used to estimate daily ETs on clear days, and daily ETs on cloudy days at this site were reconstructed using the explored relationship between the RPETand the FAW, as an application of this reconstruction method. The results from 8 flux sites showed that daily ET reconstructions had good agreement with ET measurements, with the root mean square error (RMSE) less than 31.25 W/m2. Based on MODIS and filed data from the Yucheng site, the reconstructed daily ETs under cloudy sky conditions were also consistent with measured daily ETs, with a bias of 2.62 W/m2and an RMSE of 35.21W/m2.

In this work, land surface temperature (LST) was downscaled by statistical regression model based on the nonlinear relationship with environment variables, including land surface reflectance, spectral indices, terrain factors, land cover type, reanalysis data and geolocation information. The correlation between predictor variables and LST was examined and compared with each other, in which 16 variables were finally selected into model, the variable dataset was credited to have relatively best performance with the trade-off between algorithm accuracy and computational complexity. With the optimal variable dataset, the LST of Moderate Resolution Imaging Spectroradiometer (MODIS) was downscaled from 990m to 90m by using random forest (RF) regression algorithm. Results of visual and quantitative analysis showed the satisfied downscaling results on 13 May, 2017 in Qinyang City, with the bias, coefficient of determination (R2) and root mean square error (RMSE) of -0.02, 0.9 and 2.18 K, respectively. Comparison with the algorithm for sharpening thermal imagery (TsHARP) also demonstrated the accuracy and robustness of RF model with selected variable dataset.

The development of ground-based sampling strategies is vital to the validation of medium- or coarse-resolution satellite-derived land surface temperature (LST) products, extremely over heterogeneous ground with dramatic diurnal LST change. An optimal sampling strategy in support of LST validation across both spatial and diurnal scales (SDS) was proposed in this study. The SDS integrated prior knowledge of land-cover, multi-temporal feature information and spatial distribution of samples to improve the representativeness of the samples. The SDS were also compared with three sampling strategies including random, systematic, and land-cover base sampling. The results obtained by the remote sensing simulation data indicated that the SDS performed best with stable root mean square errors (RMSE) less than 0.1k when sample ratio was more than 2%, and the representativeness of samples selected by the SDS in both diurnal space and spatial space was superior to the current sampling strategies.

Evapotranspiration (ET) plays a key role for energy transfer and water circulation in the biosphere, lithosphere, hydrosphere, cryosphere and atmosphere. In present study, spatially complete ET over the Heihe river basin, Northwest of China, was estimated from the synergistic use of MODIS (MODerate-resolution Imaging Spectroradiometer) data and CLDAS (China Meteorological Administration Land Data Assimilation) gridded meteorological data from June 1 to September 15 in 2012. For the estimation of ET over clear-sky pixels, a pixel-to-pixel pattern of land surface temperature (LST)-vegetation index (VI) feature space was developed where meteorological data were used to determine the dry and wet edges for each pixel; whereas the traditional Penman-Monteith equation was implemented to obtain ET over clouds pixels. Finally, ground ET measurements collected at two sites (corn and orchard) were used to evaluate the estimated results, root mean square error (RMSE) of 77.2W/m2and 74.9W/m2can be obtained for the two sites, respectively, indicating that spatially complete ET can be derived from currently available satellite images and meteorological data.

The angular effect is a primary obstacle for wide applications of land surface temperature (LST) products. Current directional thermal radiation models do not fully consider the difference between visible/near infrared and thermal radiative, i.e. thermal inertial effect, and are not practical enough. Therefore, this study proposed a practical method for angular normalization of LST products based on the component temperature and fractional vegetation cover (FVC). Analyzing with simulated data indicated that the proposed method could improve the LST retrieval accuracy caused by angular effect from 1.2 K to 0.8 K. In addition, the retrieval accuracy of component temperature would affect the performance of the proposed method whereas the retrieval accuracy of component emissivity had almost no effect on the performance.

Net surface shortwave radiation (NSSR) is a key parameter for the estimation of surface energy budget. This paper proposes a method to directly estimate the NSSR from simulated Chinese Gaofen-5 (GF-5) data without using any ancillary information. Firstly, the narrowband reflectances of visible/near infrared channels at the top of the atmosphere (TOA) were converted to the TOA broadband albedo. Secondly, by categorizing the land surface into three types, the NSSR was estimated under clear and cloudy skies separately based on the relationship between TOA broadband albedo and the Earth's surface absorbed shortwave radiation. The estimation error of the absorption coefficient for each land type is lower than 0.05. Finally, by employing a look-up-table acquired in the process of narrowband-to-broadband conversion, and the parameters in the NSSR estimation model for each land type, the performance of the proposed method was evaluated, where the root mean square errors (RMSEs) were 25.85 (13.97) W/m2, 20.39 (7.97) W/m2, and 40.54 (11.26) W/m2for land, ocean and snow/ice surfaces for clear (cloudy) skies, respectively.

Microwave land surface emissivity (LSE) is an important parameter for retrievals of land surface and atmospheric characteristics, and it is also crucial as an input parameter for numerical weather prediction model data assimilation. This study develops a method for retrieving all-weather LSE over China based on radiative transfer model through reconstructing spatial-temporal continuous land surface temperature (LST) data using China Land Data Assimilation System (CLDAS). Atmospheric effect is also removed with the relationships among atmospheric transmittances, atmospheric effective radiating temperature, precipitable water vapor (PWV), and cloud liquid water (CLW). The retrieved LSE are preliminarily validated by the simulations of Community Radiative Transfer Model (CRTM) and two LSEs show a determined parameter (R2) of 0.81 at 18.7 GHz.

Sensor-observed energy from adjacent pixels, known as the adjacency effect, influences land surface reflectivity retrieval accuracy in optical remote sensing. As the spatial resolution of thermal infrared (TIR) images increases, the adjacency effect may influence land surface temperature (LST) retrieval accuracy in TIR remote sensing. However, to our knowledge, few studies have focused on quantifying this adjacency effect on TIR measurements. In this study, a forward adjacency effect radiative transfer model (FAERTM) was developed to quantify the adjacency effect on high-spatial-resolution TIR measurements. The model was verified to be in good agreement with moderate resolution atmospheric transmission (MODTRAN) code, with a discrepancy3 K in some cases. These findings indicate that the adjacency effect should be considered when retrieving LSTs from TIR measurements, at least in some specific conditions. The proposed FAERTM provides a useful model for quantifying and addressing the adjacency effect on TIR measurements.

Leaf area index (LAI) is a very important vegetation parameter and has been used in growth monitoring, yield estimation, land surface modelling, among others. When the retrieval function built at a local scale are further applied at a large scale for heterogeneous surface, up-scaling effects would appear. The computational geometry method (CGM) is regardless of whether or not retrieval function is continuous or derivable. According to the theory of computational geometry, the exact LAI always falls into the interval determined by the lower and upper boundaries of the convex hull of the retrieval function. The mean value of those lower and upper bounds is assumed to be close to the exact LAI and are used to reduce upscaling effects. However, the constant weights of the scaling model are the key limitation of traditional CGM because the required uniform distribution rarely happens. To overcome this limitation, this paper tries to use variable weights rather than constant weights and successfully reduce the RMSE of retrieved LAI from 0.247 to 0.054

Radiances observed by satellites are influenced by both land surface and atmospheric parameters, and it is difficult to retrieve these parameters simultaneously from multispectral measurements with high accuracies. Even though several methods have been proposed, those methods focus on the retrieval of land surface or atmospheric parameters. Generally, those atmospheric parameters are the atmospheric water vapor and temperature profiles. Thus, this study aims to establish a back propagation artificial neural network (ANN) to retrieve land surface emissivity, land surface temperature (LST), atmospheric transmittance, upward radiance and downward radiance simultaneously from hyperspectral thermal infrared data suitable for various air mass types and surface conditions. The principle component analysis (PCA) technique is first used to compress and remove noise from the data. The evaluation of the ANN using the simulated data indicated that the root mean square error (RMSE) of LST is approximately 0.643 K; the RMSEs of emissivity and transmittance do not exceed 0.011 and 0.016. The RMSEs of upward and downward radiance of all channels are approximately 0.72K and 2.95K, respectively. The results show that the proposed ANN is capable of retrieving atmospheric and land surface parameters with promising accuracies. Because of its simplicity, the proposed ANN can be used to produce preliminary results employed as first estimates for physics-based retrieval method.

Daily evapotranspiration (ET) is considered more significant in many practical applications, compared to the instantaneous ET obtained from remote-sensing based models. The constant reference evaporative fraction (EFn the ratio of actual to reference ET) method is one of the well preformed upscaling methods used to extrapolate instantaneous ET to daily timescales. The constant decoupling coefficient (Ω) method requires similar input data to the constant EFr method and can be used to calculate daily ET directly. This study evaluated the performances of the two methods underlying the estimation of daily ET. The results from field data only showed that (i) daily ET were both overestimated by two methods when compared to the uncorrected Eddy covariance (EC) measurements; (ii) the estimated daily ET had a good agreement with the measurement corrected by the Bowen Ratio (BR) method. Based on MODIS and filed data and when the ET measurements were corrected by the BR method, the results showed that (i) the constant EFr method overestimated daily ET by a bias of 5.6 W/m2and a root mean square error (RMSE) of 18.6 W/m2; (ii) the constant Ω method underestimated daily ET by a smaller bias of -4.8W/m2 and a RMSE of 22.5 W/m2.Therefore, the constant Ω method had a similar performance with the constant EFr method, and could be applied to estimate daily ET effectively.

This study proposes a land surface temperature (LST) downscaling method to downscale the LST of Moderate Resolution Imaging Spectroradiometer (MODIS) from 990m to 90m by using random forest (RF) regression algorithm. The LST product of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), with 90m resolution, serves as the validation reference at the finer scale. The proposed method is based on the relationship between LST and a variety of surface parameters including band reflectance, spectral indices, land cover types and terrain factors. The proposed downscaling method is evaluated in Segovia, Spain, the Pefiarora mountain region. Comparison between downscaled LST and referenced LST proves that the proposed method shows a great accuracy in downscaling LST. Furthermore, another downscaling method, an algorithm for sharpening thermal imagery (TsHARP), is also implied to get finer resolution LST to make a more complicated comparison with the proposed method. The results are evaluated by root mean squared error (RMSE) and bias, which demonstrate that the accuracy and robustness of RF downscaling method compared with TsHARP.

Long-term global land surface temperature (LST) is a very important data source for climate change study. By adding a quadratic term of two adjacent channels' brightness temperature difference, this paper proposed to use a refined generalized split-window (GSW) algorithm to retrieve LST from series NOAA-AVHRR data. Results of the simulation analysis and the sensitive analysis indicated that the refined GSW method had a high retrieval accuracy and a robust performance. The overall root mean square errors (RMSEs) varied from 0.55 K to 0.59 K for NOAA 7-AVHRR to NOAA 19-AVHRR data. In terms of the wet atmosphere, the refined algorithm had a better ability than the GSW algorithm, and the proportion of sub-ranges with RMSE below 0.5 K was 61.7%. Most RMSE errors were within 0.2 K and 0.7 K for sensor noise$(\mathrm{NE}\Delta \mathrm{T})=0.1\ \mathrm{K}$and$\mathrm{NE}\Delta \mathrm{T}=0.2\ \mathrm{K}$, respectively, compared with the cases of no$\mathrm{NE}\Delta \mathrm{T}$. Given the uncertainties of emissivity around 1%, the errors were mainly within [0.9K, 1.2K] for dry atmosphere and [0.3K, 0.7K] for wet atmosphere.

As the main process parameter of water and energy exchange, evapotranspiration (ET) is defined as the water being converted from liquid to gaseous and from land surface to atmosphere. Potential evapotranspiration (ETO) is defined as the evapotranspiration when water supply is sufficient of the land surface and reflect the ability of the surface to supply moisture. In this study, we explored the relationship between annual averaged ET (ET/ETO) and annual averaged 36.5 GHz emission, and provided a new train of thought of how to use passive microwave data to estimate annual averaged evapotranspiration. We found a non-linear relationship with a R2 of 0.52 between annual averaged 36.5 GHz emission and observed annual evapotranspiration at 28 flux tower sites of Asia and North America. We estimated ET and ETO of China and found a linear relationship with a R2 of 0.51 between the annual averaged (ET/ET0)1/2and the annual averaged 36.5 GHz emission at 9 flux tower sites of China.

This paper proposed a workflow to estimate land surface temperature (LST) from unmanned aerial vehicle (UVA) loaded thermal infrared imager FLIR data. The radiance received at the FLIR's sensor (ZENMUSE XT) was assumed to be the sum of the radiance from the land surface itself and the reflected downward atmospheric radiation, ignoring the upward atmospheric radiation due to UVA's low-altitude flying. The total radiation from the land and the downward atmospheric radiation were calculated from the brightness temperature extracted from the thermal image shot on the land surface and into the sky, respectively, over a farmland field at Shunyi District, Beijing, on October 27, 2017. The emissivity of the winter wheat was measured by the Portable Fourier transform thermal infrared spectrometer (102F). Finally, LST in a target scene was estimated. The range of LST in this area is around 27.8~36.5 °C. A well textual feature was depicted owing to the relatively high resolution of the UAV data.

This work addressed the estimation of Land Surface Temperature (LST) from Chinese Gaofen-5 (GF-5) satellite Thermal Infrared (TIR) data, using a Generalized Split-Window (GSW) algorithm. The numerical values of the GSW coefficients were obtained using a statistical regression method from synthetic data simulated with an accurate atmospheric radiative transfer model MODTRAN 5 over a wide range of atmospheric and surface conditions. The LST, mean emissivity, and atmospheric Water Vapor Content (WVC) were divided into several tractable sub-ranges to improve the fitting accuracy. The experimental results showed that the combination of two adjacent channels CH8.20(centered at 8.20 μm) and CH8.63(centered at 8.63 μm) was comparable with the combination of two adjacent channels CHlO.SO (centered at 10.80 μm) and CH11.95(centered at 11.92 μm) for estimating LST using the GSW algorithm, with Root Mean Square Errors (RMSEs) below 0.8 K, provided that the Land Surface Emissivities (LSEs) are known. Particularly, for the high emissivity surfaces under wet and hot atmospheric conditions , two not adjacent channels combination of CH8.63and CH11.95could also be used to estimate LST with RMSEs within 0.5 K.

Continuous monitoring of high spatial resolution evapotranspiration (ET) is critical for water resources management at both regional and local scales. This research employs a multi-sensor satellite data fusion approach (ESTARFM: Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) combined with a Two-Source N95 model and a constant evaporative fraction method to compute daily ET at 30 m spatial resolution. Two schemes are followed: the first scheme is to apply ESTARFM on the LST data to estimate daily ET at 30 m spatial resolution. The second scheme is to apply ESTARFM on the ET derived from MODIS and Landsat 8 images. The results show that the ET fused by both schemes is in good agreement with the reference ET data from the Landsat 8, while the first scheme (applying the ESTARFM on LST) is observed with more variations.

The traditional methods used in the atmospheric correction depend on the empirical relationships or atmospheric radiative transfer model. However, both of them have some deficiencies. For example, as the empirical method depends highly on the training data, it will be applicable under certain conditions. On the other hand, the method that based on the atmospheric radiative transfer model has to run the code each time, which is not an appropriate choice for operational correction of the atmospheric effects. In this paper, a fast parametric model of estimating atmospheric parameters for Landsat 8 thermal infrared sensor is proposed. The results show that the RMSE (Root Mean Squared Error) of the total transmission is 0.003, the RMSE of for both the atmospheric upward and downward radiances are 0.0004. Therefore, the proposed model could be used without the help of any atmospheric radiative transfer model. That is, this model would have a better application market.

The Net Surface Shortwave Radiation (NSSR) at the Earth's surface drives evapotranspiration, photosynthesis and other physical and biological processes. The primary objective of this study is to estimate NSSR using multispectral narrowband data of the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Visible Infrared Imaging Radiometer Suite (VIIRS). A method to convert narrowband reflectance to broadband albedo at TOA with VIIRS data is developed and retrieval of NSSR from TOA broadband albedo is also carried out. Accurate radiative transfer model MODTRAN 5 was used to simulate these physical quantities, and the least squares method is applied to get coefficients. The RMSE of Narrowband-to-broadband albedo conversion results is 0.011. And the RMSE of between actual and simulated NSSR in clear sky condition is 50.2 W/m2.

Daily Evapotranspiration (ET) is of great significance among various practical applications in the fields of water management, drought monitoring and climate change study. This paper utilized instantaneous decoupling coefficient to estimate daily LE (used interchangeably with ET in this paper) with atmospheric and surface resistances calculated from MODIS data. The field data were used only at first to identify the errors induced by the parameter retrieval from remote sensing data. The estimated daily LE was compared with measured data and the result showed that the coefficient of determination (R2) was 0.960, with a root mean square error (RMSE) of 12 W/m2and a bias of −4 W/m2. When MODIS data were involved in the calculation of decouple coefficient and resistances, the R2of the estimated daily LE was 0.949, with a RMSE of 33.1 W/m2and a bias of −17.9 W/m2. Therefore, it is feasible and effective to obtain daily LE using instantaneous decoupling coefficient from remote sensing data.

Numerous works show the great potential of microwave remote sensing in assessing biophysical variables of rice plants. However, most studies are focusing on the backscattering character of rice canopy. Comparatively, much lesser attention has been devoted to modeling bistatic scattering behavior of rice canopy. Therefore, this study aims to improve the understanding of bistatic scattering response of rice canopy, and thus help design a bistatic radar system for better monitoring rice growth and yield estimation. Firstly, a bistatic scattering of a cluster of rice plant was modeled by solving the vector radiative transfer equations. Then, the concept of antenna array is applied to account for the inter-cluster wave interactions to obtain the total scattered field and bistatic scattering coefficient. The results show that the coherent scattering of clusters is found to be a function of vegetation growth stages. Scattering coefficient corrections need to be considered, especially at initial growth stages, and in the forward scattering direction.

Linking the terrestrial water cycles, carbon cycles and energy exchange, evapotranspiration (ET), which combines the surface evaporation and plant transpiration, is a key land surface parameter in water and heat balance of land, lake or river surface, and is central to earth system science. In this study, based on the MERRA reanalysis dataset and MODIS NDVI and LAI product, a support vector machine was used to estimate the land surface ET at sites and global scales. The results showed that, the support vector machine model probably could explain 60%–80% of the land surface ET change at 242 global FLUXnet sites when ten indicators while 56%–79% when five indicators were used to drive the model. For different vegetable cover sites, compared with EC observations, the results of evergreen broadleaf forest was worse than others.

Directional anisotropy limits the application of land surface temperature (LST) and a simplified parametric model to effectively estimate directional brightness temperature (DBT) in the thermal infrared is critical. This study used a widely validated four-stream scattering by arbitrarily inclined leaves (4SAIL) model as a benchmark to evaluate the performance of the kernel bidirectional reflectance distribution function (BRDF) model and the three-kernel-model. Results showed that the two kernel-driven models can fit the DBT well and the maximum root mean square error (RMSE) is 0.13°C. The kernel BRDF model has a wider application scope including canopies of uniform, spherical, plagiophile and planophile LIDF with low LAI and hotspot. When LIDF is planophile and plagiophile, two models can reach the best fitting effect and the worst effect is the canopy with erectrophile LIDF. Under a specified LIDF, the relationship between fitting accuracy and LAI is negative while hotspot parameter is positive.

Evapotranspiration (ET) is one of the most significant components in the water and heat transfer between land and atmosphere. This paper develops an improved constant evaporative fraction (EF) method through a theoretical derivation to improve the upscaling of remote sensing instantaneous latent heat flux (LE) to daily scale. Preliminary results show that our improved constant EF upscaling method can significantly reduce the underestimation of the daily LE upscaled using the conventional constant EF upscaling method. More validation work will be conducted to test the robustness of our improved EF method for the upscaling of remote sensing instantaneous LE estimates to daily scale.

Quantification of land surface evapotranspiration (ET) at daily or longer time scales is of great significance in agricultural ecosystem and hydrologic cycle. Temporal upscaling of instantaneous remote sensing-based ET to daily or longer time scales is generally only based on a single instantaneous estimate. A test is made to use two instantaneous ET estimates for the daily upscaling. The results show that the temporal upscaling using two instantaneous ET estimates is superior to that using only single instantaneous ET estimate for the constant extraterrestrial solar radiation ratio (Rp) method, the constant global solar radiation ratio (Rg) method, and the constant evaporative fraction (EF) method. The largest improvement of daily ET estimation occurs when instantaneous ET in the morning is combined with that in the afternoon for the Rpand Rgmethods, while the for EF method the optimal combination comprises of two moments in the afternoon.

Thin cirrus clouds can reduce land surface long-wave transmission and re-emit energy at a colder temperature and thus making it difficult to estimate downwelling surface longwave radiation (DSLR) from satellite data. In this study, a simulation database is established in terms of radiances observed at the top of the atmosphere (TOA), cloud optical thickness (COT), atmosphere water vapor content (WVC) and height of the cirrus bottom (HCB) and DSLR. And the back propagation (BP) artificial neural network (ANN) was used to estimate DSLR from remotely sensed data for cirrus cloudy skies. Results show that the BP model with TOA thermal radiance, COT, WVC and HCB as inputs provides a practical and efficient tool for remote sensing applications to estimate DSLR under thin cirrus clouds with root mean square error (RMSE) of 11.66 W/m2.

This paper aims to extend the generalized split-window (GSW) algorithm in land surface temperature (LST) retrieval to atmospheres with air temperature inversion (ATI) near the Earth surface boundary. Simulation analysis shows that the influence of ATI on the LST retrieval of the GSW algorithm becomes larger when the ATI intensity increases. To further analyze the influence, all ATI atmospheric profiles are extracted from the Thermodynamic Initial Guess Retrieval (TIGR) cloud-free database. Combining the ATI atmospheric profiles and the GSW coefficients, we find that the LST retrieval error caused by ATI is larger than 0.3 K. To reduce the LST retrieval error associated with the ATI in the GSW algorithm, a quadratic equation as a function of ATI intensity is proposed. To validate the proposed method, some in situ measurements observed at the Hailar site are used. The results show that the proposed method could improve the LST retrieval accuracy by 0.47 K for atmospheres under ATI conditions.

This paper attempts to retrieve leaf water content (LWC) by developing new vegetation indices from the combination of the near-infrared (NIR) and middle-infrared (MIR) spectral reflectances. The expanded vegetation leaf model PROSPECT-VISIR and the widely validated four-stream scattering by arbitrarily inclined leaves (4SAIL) model are employed to simulate canopy reflectance in 0.4–5.7 μm region with various leaf water content scenarios. Change of standard deviation of the canopy reflectance with respect to wavelength is used to analyze the sensitive of the spectral reflectance to the LWC. The results show that the spectral reflectances at 1.405μm, 1.875μm, 2.015μm, and 4.375μm are most sensitive to the change of LWC, and the difference vegetation index (DVI) combined by spectral reflectances in 1.405μm and 4.375μm is the best index to retrieve LWC with root mean square error (RMSE) of 0.0008 g/cm2.

Land surface temperature is an important driving force in the exchange of water, heat, and even CO2at the surface-atmosphere interface in the desert regions. The rapid and continuous measurements of land surface temperature are meaningful to the ecological and environmental researches. A physically based single-frequency and double-polarization algorithm for retrieving land surface temperature is developed in this study. The 18.7 GHz vertically polarized emissivities are firstly estimated from the Polarization Ratio (PR, defined as the ratio of the horizontal to vertical brightness temperature at the same frequency) at 18.7 GHz. And then the estimated emissivities can be directly used to retrieve land surface temperature without considering the atmospheric effect. A preliminary validation is done in the Taklimakan desert. The retrieved land surface temperatures are compared to the infrared land surface temperature products for all the year of 2007 with a Root Mean Square Error (RMSE) of 3.05 K.

In this paper, an attempt is made to investigate the angular signatures of bistatic scattering, in the azimuthal direction, from rough surfaces, with the aim of deepening our understanding of the bistatic scattering behaviors and exploring its potential applications. Three distinct angular features, dip angle, scattering strength, and angular width, as a function of the surface roughness and dielectric constant, are identified. Brewster's scattering, and its role in angular behavior, is examined at limited extent. Results reveal that the angular features strongly correlate with the surface parameters and scattering geometry. For small scattering angle, dip angle and width are independent of surface roughness. Comparatively, for larger incident and scattering angles, beyond 50°, the dip angle and scattering strength are sensitive, simultaneously, to rms height and dielectric constant, while the dip width only responses to rms height. Dips, induced by Brewster's scattering effect, not only shift in the polar direction, but also in the azimuthal direction, and are strongly dependent on surface parameters and bistatic geometry. Increasing the surface roughness or, equivalently, the incident angle tends to promote the disappearance of dips. The main contributions of this paper can be summarized as follows: 1) quantitative description of dip features, including angle, scattering strength, and angular width; 2) comprehensive characterization of the dip features and their dependence on surface parameters and bistatic geometries; and 3) limited investigation of the behavior of Brewster's scattering-induced dip.

Evapotranspiration (ET) is one of the key variables in the water and energy exchange between land surface and atmosphere. This paper develops an end-member-based two-source approach for estimating land surface ET (i.e., the ESVEP model) from remote sensing data, considering the differing responses of soil water content at the upper surface layer to soil evaporation and at the deeper root zone layer to vegetation transpiration. The ESVEP model first diverges the soil-vegetation system net radiation into soil and vegetation components by considering the transmission of direct and diffuse shortwave radiation separately from the transmission of longwave radiation through the canopy, then calculates the four dry/wet soil/vegetation end-members with the diverged soil and vegetation net radiations, and last separates soil evaporation from vegetation transpiration based on the two-phase ET dynamics and the four end-member temperatures. The model can overall produce reasonably good surface energy fluxes and is no more sensitive to meteorology, vegetation, and remote sensing inputs than other two-source energy balance models and surface temperature versus vegetation index ($T_{R}$ -VI) trapezoid models. A reasonable agreement could be found with a small bias of ±8 W/$\text{m}^{2}$ and a root-mean-square error within 60 W/$\text{m}^{2}$ (comparable to accuracies published in other studies) when both model-estimated sensible heat flux and latent heat flux from MODIS remote sensing data are validated with ground-based large aperture scintillometer measurements.

This paper presents bistatic scattering response of rice canopy over growth stages based on a three-layer microwave scattering model, which is developed using the iterative solution of the vector radiative transfer equations up to the second order, and the dense medium phase and amplitude correction theory (DM-PACT) is used to improve the phase matrix taking coherent effects into account. To validate the model, ground-based measurements of backscattering coefficients over an entire rice-growth stage is used. Then, the bistatic scattering response of rice canopy is analyzed in respect of canopy components, including plant height, structure, soil moisture, and surface roughness, as well as their interactions with sensor configurations, such as frequency, polarization, and incident angle. The sensitivity analysis was carried out to demonstrate how the model reacts to changes of each input. The results show that bistatic scattering coefficient varies greatly over different stages, and is strongly dependent on rice plants' structure, including the size, shape, and orientation, especially stems.

Evapotranspiration (ET) is the combination process of the surface evaporation and plant transpiration which occur simultaneously, and it links the terrestrial water cycles, carbon cycles and energy exchange. In this study, based on the observations from 242 global FLUXnet sites, with daily average temperature, relative humidity, wind speed, incident solar radiation, NDVI and observed ET as input data, we used a support vector machine to estimate the land surface daily ET at nine different vegetation type sites. The results show that, for all vegetation type sites, when the predicted ET was validated with the eddy covariance measurements, the support vector machine algorithm underestimates the ET and probably could explain 71%-86% of the land surface ET change.

In this paper, we investigated the bistatic scattering of soil moisture and surface roughness of bare soil surfaces. We generated database by using an advanced integral equation model (AIEM). For better understanding the bistatic scattering characteristics of bare soil surfaces, we adopted single polarized simulations and combination of dual angular simulations. To explore the sensitivity of bistatic scattering to soil moisture, we applied a defined sensitivity index. The results shown that the scattering coefficients of VV polarization are more sensitive to soil moisture compared with the HH polarized scattering coefficients. Moreover, the forward direction is the most sensitive to soil moisture in all cases. Besides, the dual angular observations show good sensitivity to soil moisture, especially when the differences of the two incident angles are large.

Ambient irradiance is pivotal to be considered for field measurements of soil emissivity with Portable Fourier Transform Infrared Spectro-radiometer (102F). Usually, a diffusely reflecting gold plate which has a near-Lambertian behavior was used to acquire the ambient irradiance. Because of the measurements of soil and ambient irradiance are not synchronized, It can generate errors on determination of soil emissivity, especially for the erratic cloud and instantaneous wind which can make the ambient irradiance a sharp change. In this study, four conditions about the ambient radiances were 30% underestimated, 50% underestimated, 30% overestimated and 50% overestimated to assess the impacts of ambient irradiance on determination of soil emissivity. Preliminary research shows that ambient irradiance has more impacts on determination of soil emissivity in 8-10um than it in 10-13um. In 8-10um, the relative difference of soil emissivity can be more than 0.005 when the ambient irradiance was 30% overestimated. And it can reach up to 0.01 when the ambient irradiance was 50% overestimated. The error magnitudes are related to soil types. By contrast, the impacts of ambient irradiance are not obviously in 10-13um. Similar results can be seen in the ambient irradiance were underestimated conditions.

This paper analyzes the influence of the anomalous temperature occurred at the near surface boundary layer of the atmosphere on the land surface temperature (LST) retrieval with the generalized split-window algorithm (GSW). The coefficients in the GSW algorithm corresponding to a series of overlapping ranging of the mean emissivity, the atmospheric water vapor content, and the LST are derived using a statistical regression method from the numerical values simulated with an accurate atmospheric radiative transfer model MODTRAN 4 over a wide range of atmospheric and surface conditions. The simulation analysis shows that the LST can be estimated by the GSW algorithm with the root mean square error (RMSE) increasing by larger than 0.2 K when atmospheric anomalous profiles are involved. Taking into account the angular dependence of the top of the atmosphere radiance, six different viewing zenith angles (VZAs) are used in the simulations. Results show that the RMSEs become larger when the VZAs change form 0°to 60°.

An algorithm has been developed for retrieving instantaneous microwave land surface emissivity using brightness temperature and precipitable water vapor data. Unlike previous algorithms, the new technique does not need infrared land surface temperature as the input data, and overcomes the limitation of previous algorithms under cloudy conditions. Compared with the values from physical retrieval algorithm, the result demonstrates that this new algorithm has a Root Mean Square Error of 0.038 and a bias of 0.012. Although the accuracy is worse than 1%, this new algorithm presents the potential to obtain the instantaneous microwave land surface emissivity under both cloud-free and cloudy conditions, which can be applied in some weather prediction models.

Land surface temperatures (LSTs) at high spatial resolution are crucial for hydrological, meteorological, and ecological studies. Downscaling LSTs from coarse resolution to finer resolution is an alternative way to obtain LSTs at high spatial resolution. In this paper, we proposed a new algorithm based on geographically weighted regression (GWR) to downscale Moderate Resolution Imaging Spectroradiometer LST data from 990 to 90 m. Unlike previous LST downscaling algorithms, this algorithm built the nonstationary relationship between LST and other environmental factors (including the normalized difference vegetation index and a digital elevation model) using geographically varying regression coefficients. The uncertainty in this algorithm was evaluated with a sensitivity analysis. The results show that the total uncertainty in this algorithm is less than 2 K. The performance of the GWR-based algorithm was assessed using concurrent ASTER LST data as a reference LST data set. Moreover, this algorithm was compared against the TsHARP algorithm, which was widely used for LST downscaling. The results indicate that the GWR-based algorithm outperforms the TsHARP algorithm in terms of statistical results. The root mean square error (mean absolute error) value decreases from 3.6 K (2.7 K) for the TsHARP algorithm to 3.1 K (2.3 K) for the GWR-based algorithm.

This paper presents an electromagnetic modeling of temporal variations of microwave backscatter from rice canopy based on radiative transfer theory to understand the complex microwave scattering mechanisms of rice crops at different growth stages. Model validation is made by a comparison with field measurements from four independent campaigns by ground-based scatterometers and spaceborne synthetic aperture radar (SAR). Then, the frequency responses and angular dependence are examined, followed by an analysis of parameter sensitivity and uncertainty to microwave backscatter. The validation shows promising results for the physically based model in assessing radar response of rice plant in its full-growth stage. It is found that radar scattering of rice canopy is highly dependent on the growing stages of rice plants, in addition to radar configurations, such as frequency, polarization, and incident angle. Moreover, backscattering of rice canopy is more dependent on stems than on leaves, and as rice plants grow, increasing stems and leaves tend to promote HH- and VH-polarized scattering, while first promoting and then reducing VV-polarized scattering. Moreover, the greatest uncertainty takes place at the late growth stage for copolarization and at the early stages for cross-polarization. The main contributions of this paper can be summarized as follows: 1) the establishment of a rice canopy microwave backscattering model using radiative transfer theory adapted to all the stages in the phenological cycle of rice; 2) the validation of the performance of the proposed model with numerous independent measurements from spaceborne SAR and ground-based scatterometers from different countries and regions; 3) the investigation of the interactions between microwave backscatter signatures and rice canopy growth variables over growth stages; and 4) the identification of the dominant influential parameters of the rice scattering model and determination model uncertainty.

Land surface temperature (LST) is a crucial parameter in analyzing and evaluating climate change at various scales, the surface energy balance, soil moisture, evapotranspiration and urban heat islands. Currently, methods for its estimation from space have continuously been developed, while most studies focus on the Split-Window (SW) algorithms. According to the published works, some approximations and assumptions were used to develop SW algorithms. This paper investigated and revised the error caused by these approximations and assumptions with the help of TIGR 2000 database and MODTRAN 4.0 software. Then a new SW method to estimate LST from FY-3A/VIRR was proposed in this paper. The primarily accuracy evaluation of the proposed method shows that the root mean square error (RMSE) of LST estimation using TIGR atmospheric profiles is 0.768 K, with the bias of −0.122 K.

Because of the complex influences of cirrus clouds on the estimation of Land Surface Temperature (LST), the traditional LST retrieval algorithms can only be used for clear-sky conditions and there is no LST when the pixel is identified as clouds by cloud mask algorithm. To retrieve LST under cirrus clouds, a three-channel algorithm what is dependent on cirrus optical depth (COD) and effective radius was proposed. The simulated data showed that the daytime LST could be retrieved using the three-channel algorithm with a root mean square error of less than 3.0 K when COD (at 12 μm) was less than 0.7 and viewing zenith angle was less than 60°. Compared with the results of the traditional clear-sky two-channel LST retrieval algorithm, where the maximum RMSE was 17.8 K, the algorithm proposed in this study could significantly improve the accuracy of the daytime LST retrieved using satellite thermal-infrared data.

Soil moisture plays an important role in the process of energy exchange and water cycle. Soil moisture also provides critical information in agriculture, including crop growth and drought. In this study, the comparison between NASA AMSR-E soil moisture product and ground-based measurement are performed in terms of (1) measurement depths of soil moisture, and (2) satellite overpass times. The results show that the NASA AMSR-E soil moisture product can be used to monitor time-series variation in soil moisture. Compared to the AMSR-E product from descending overpasses, the AMSR-E product from ascending overpasses has better ability in monitoring soil moisture variation. Also the AMSR-E product has better ability in monitoring soil moisture at the depth of 0-10 cm than 10-20 cm.

Evapotranspiration (ET) is one of the most significant components in the water and energy transfer between land surface and atmosphere at regional and global scales. this study aims to explore the underlying mechanism in the surface temperature versus fractional vegetation cover (Ts-Fr) space for regional ET and evaporative fraction (EF) estimation through a physically-based soil-vegetation-atmosphere transfer (SVAT) simulation. It also investigates the effect of vegetation type and physiology on the relationship between EF and Tsunder deep-layer water-saturated and water-stressed conditions. The preliminary results show that in the Ts-Frspace surface EF varies linearly with surface temperature when root zone layer is not water-stressed. However, the linear relationship may be different between one vegetation type and another. When root zone layer is water-stressed, the variation of root zone layer soil water content has a negligible effect on the canopy temperature but the EF can be significantly influenced.

This work estimated firstly net surface shortwave radiation (NSSR) from MODIS/Aqua data with six visible and near infrared channels by re-parameterizing the methodology proposed by Tang et al. (2006). Comparison of the estimated NSSR with those simulated actual one showed that the root mean square error (RMSE) is 34.1 W/m2. To validate the proposed parameterization scheme, some field measurements made at seven sites of the Surface Radiation Budget Network (SURFRAD) in October, 2008 were used. The result showed that the RMSE is 53.33 W/m2. To accurately capture the diurnal variation of NSSR for cloudy skies, a simple and practical linear regression model by combing the instantaneous NSSRs estimated from MODIS/Terra at local solar time 10:30 AM and MODIS/Aqua at 13:30 PM has been proposed to estimate the daily average net surface shortwave radiation (DANSSR). The results showed that the RMSE between the estimated DANSSR and those calculated from the seven SURFRAD measurements for cloudy days in 2008 is 42.59 W/m2.

This paper retrieves the Land surface temperature (LST) from MODIS mid-infrared data. Considering that the daytime mid-infrared satellite data contains both reflected radiance due to sun irradiance and emitted radiance from the surface and the atmosphere, this paper estimates the bidirectional reflectivity in mid-infrared channels firstly, and then derives the directional emissivity with the linear kernel-driven BRDF model. Finally based on the radiative transfer equations in mid-infrared channels, the LST is retrieved. The retrieved LSTs are preliminarily validated with the MODIS LST product MYD11B1. The results show that the root mean square error (RMSE) between the two estimated LST is below 1.9 K and the Bias is below 1.10 K. In addition, some in situ measurements are also used to validate the retrieved LST. The results show that the RMSE is 2.06 K and Bias is 0.73 K.

Based on analyzing the influence of atmospheric water vapor content (WVC) on coefficients determination in the generalized split-window (GSW) algorithm, it is found that the coefficients are relatively monotonic variable with the increasing of WVC, which were proposed to determine the coefficients as implicit linear functions. To improve the land surface temperature (LST) retrieval accuracy in the GSW algorithm, the WVC is proposed to determine the coefficients as an explicit parameter in this work. The results show that the proposed method can acquire relatively high accurate LST if WVC is known. The root mean square errors (RMSEs) between the actual LST and those estimated with the proposed method are lower than those retrieved with the coefficients in the GSW algorithm.

To compare and evaluate the performance of iterative spectrally smooth temperature and emissivity separation method (ISSTES) and linear emissivity constraint temperature and emissivity separation method (LECTES) on land surface temperature (LST) and land surface emissivity (LSE) estimation, the simulation data for hyperspectral infrared spectroradiometer are used. The results reveal that the LST can be retrieved within the accuracy of 1 K at various conditions for both methods. However, the 0.01 accuracy of LSE depends on the method selected and the noise level. The ISSTES method should be taken full consideration when it used to retrieve LSE for the warm and wet atmosphere. It is advised that the ISSTES method is used for cold and dry atmosphere and the LECTES method for warm and wet atmosphere. The noises in the ground measurements may be have more effects on the accuracies of LST and LSE than those in the atmospheric downwelling measurements.

Soil moisture is a key variable that influences the redistribution of the radiant energy and the runoff generation and percolation of water in soil. Knowledge of soil moisture temporal-spatial variations is important in a wide range of studies. This study aims to investigate the temporal-spatial variations of soil moisture using microwave polarization difference index (MPDI). The AMSR-E/Aqua Daily Global Quarter-Degree Gridded Brightness Temperature at 10.65 GHz channel was used to calculate the MPDI. In addition, the AMSR-E/Aqua Daily L3 Surface Soil Moisture was used in this study. The temporal and spatial patterns between the MPDI and soil moisture were analyzed. The results indicate that the temporal and spatial patterns of the MPDI are consistent with those of soil moisture. The MPDI reflects the temporal and spatial variations of soil moisture.

Land surface temperature (LST) is a critical parameter for numerical weather forecasting, drought monitoring, water resources management and global climate change studies. Because of the supercooled temperature, the cirrus cloud can significantly reduce the LST retrieved from thermal infrared data. This paper focused on analyzing and reducing the influence of thin cirrus cloud on the accuracy of LST retrieved using the generalized split-window (GSW) algorithm. A correction method was proposed with the LST retrieval error expressed as linear functions of cirrus optical depth (COD). The slopes of the linear functions were further written as the combination of the difference and mean of two used channels emissivities and cirrus cloud top height (CTH). The results showed that the LST retrieval accuracy could be significantly improved with root mean square error (RMSE) of LST changing from 14.4 K before LST error correction to 1.8 K after LST error correction for COD equivalent to 0.3.

The total atmospheric precipitable water vapor amount (TWV) is a key variable for the study of the Earth's climate. This paper develops an algorithm to estimate the TWV over clear skies from the Medium Resolution Spectral Imager (MERSI) data in the near-IR channels. The MODTRAN 4 code is used to simulate the top of the atmospheric radiances for the MERSI channels. The results show that the proposed algorithm is suitable to estimate TWV form the absorbing channel centered at 0.940 μm and the atmospheric window channels centered at 0.865 μm and centered at 1.030 μm by the radiances over the clear pixels, with relative differences in the range of 10%-15%.

Evapotranspiration (ET) controls the water and heat transfer between land surface and atmosphere at different temporal and spatial scales. Given the different structures of the spatial variability-based SEBAL and TS-VI triangle models but essentially the same definitions of the dry and wet pixels, this study aims to investigate through an analytical deduction and model applications how the SEBAL model and the TS-VI triangle method differ from each other in the regional evaporative fraction (EF) and ET estimation. Results show that the SEBAL model produces more satisfactory latent heat flux (LE) estimates than the Ts-VI triangle method when compared with ground-based large aperture scintillometer measurements at the Yucheng station. The SEBAL-derived EF and ET values for most pixels over the study area are larger than those derived by the TS-VI triangle method when the same group of dry and wet pixels is applied.

To evaluate the radiometric characteristics of the thermal infrared channels of Visible and InfraRed Radiometer (VIRR) aboard Chinese second generation polar-orbiting meteorological satellite FengYun-3B (FY-3B), the inter-calibration of those thermal infrared channels with high spectral resolution data acquired by the Atmospheric InfraRed Sounder (AIRS) aboard Aqua is carried out in this paper. Four steps, i.e. subsetting, collocating, transforming and regressing, were used to calculate the inter-calibration coefficients. The collocation data were picked out with a series of thresholds: the absolute viewing zenith angle differences less than 10°, the absolute viewing azimuth angle differences less than 20°, and absolute time differences less than 40 minute. The results on June 1st, 2012 reveal that the VIRR/FY-3B measurements are highly linearly related to the convolved AIRS/Aqua measurements. However, calibration discrepancies exist between VIRR and AIRS channels. When brightness temperatures in VIRR channels change from 270 K to 300 K under a normal condition, the AIRS-VIRR temperature adjustment linearly varies from -0.79 K to -2.32K for VIRR channel 4, from 0.14 K to -1.42 K for VIRR channel 5, respectively.

Soil moisture saturation index (SMSI) is an important indicator that demonstrates the status of the soil water content for drought monitoring. However, at present, most of the methods to calculate the SMSI from the in situ measurement data are inadequate or inaccurate. This paper proposed a simple method to determine the SMSI from the remotely sensed data. Combining the theory of thermal inertia and triangle method, the apparent thermal inertia and fractional vegetation cover can construct a triangular space. In this space, SMSI can be determined easily. Validation was performed with in situ measurements for 19 meteorological stations in the study area. Results indicated that the method can obtain the accurate soil water status that reflects the variation in soil moisture to some extent and is suitable for monitoring the regional surface soil moisture.

Land surface temperature and emissivity separation is a critical process for land surface temperature (LST) retrieval from hyperspectral thermal infrared data. This paper compared the iterative spectrally smooth temperature/emissivity separation (ISSTES) and the correlation based temperature/emissivity separation (CBTES) methods for land surface temperature and emissivities retrievals from simulated data under typical atmospheres and different land surface covers. The paper also compared both methods for retrieving low emissivities with simulated data. For typical land cover types, neglecting the instrumental noise, ISSTES is more accurate than CBTES with root mean square error (RMSE) of LSTs less than 0.0005K for the ISSTES and 0.1K for the CBTES. For low emissivity material, considering instrumental noise, both methods have large errors, but the CBTES performs much better.

Land surface microwave emission is mainly a function of soil moisture and surface roughness. However, the relationship between vertical and horizontal polarization land surface emissivities is not fully understood. This study attempts to develop a parameterized relationship to relate the emissivities at different polarizations for bare surfaces. A microwave emission database is simulated for bare surfaces with a wide range of surface roughness and dielectric properties using the Dobson model and the Advanced Integral Equation Model (AIEM) at 10.65 GHz under the configuration of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). By analyzing the factors that influence microwave emission, parameterized relationships between vertical and horizontal polarization emissivities are established. With the proposed relationships, the effects of soil moisture and surface roughness on the soil microwave emission signal can be separated. Simulated results using the proposed relationships are compared with those of the AIEM. These results show that the proposed relationships are accurate, with absolute root mean square errors (RMSEs) of 0.0025, and they can be used as a reliable boundary condition to retrieve other surface geophysical parameters. Combining this relationship with the calculated soil moisture, the RMSE of the estimated soil moisture is 0.44% using simulated data. As an example, observations of AMSR-E are used to estimate the variation in soil moisture in Saharan Africa in 2004. By comparing with independent soil moisture data, the result shows that the proposed relationship is promising for retrieving surface geophysical parameters from microwave observations.

This paper presents an improved algorithm for simultaneously retrieving both land surface emissivity (LSE) and land surface temperature (LST) using data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the MSG-2 satellite. First, the temperature-independent spectral index-based method for LSE retrieval is reviewed and improved in terms of three aspects: atmospheric correction, fitting of the bidirectional reflectivity model, and retrieval of the LSE in SEVIRI channel 10. Then, the generalized split-window method with seven unknown coefficients is used to derive the LST. Finally, this improved algorithm is applied to several MSG-2/SEVIRI data sets over a study area with geospatial coverage of latitude 30 ° N-45 ° N and longitude 15 ° W-15 ° E, and using detailed cases, the modifications to the original LSE/LST retrieval methods are shown to be effective and reasonable. In addition, the SEVIRI-derived LSTs are cross-validated primarily using the Moderate Resolution Imaging Spectroradiometer-derived validated LST data extracted from the MOD11B1 product on two clear-sky days (August 22, 2009 and July 3, 2008). The validation results indicate that more than 70% of the differences are within 2.5 K and that the LST differences tend to be lower at night than in the day, which may result from the homogeneous thermal conditions at night.

This paper aimed at the case of nonisothermal pixels and proposed a daytime temperature-independent spectral indices (TISI) method to retrieve directional emissivity and effective temperature from daytime multiangular observed images in both middle and thermal infrared (MIR and TIR) channels by combining the kernel-driven bidirectional reflectance distribution function (BRDF) model and the TISI method. Four groups of angular observations and two groups of MIR and TIR channels with narrow and broad bandwidths were used to investigate the influence of angular observations and bandwidth on the retrieval accuracy. Model sensitivity analysis indicated that the new method can generally obtain directional emissivity and temperature with an error less than 0.015 and 1.5 K if the noise included in the measured directional brightness temperature (DBT) and atmospheric data was no more than 1.0 K and 10%, respectively. The analysis also indicated that 1) large-angle intervals among the angular observations and a larger viewing zenith angle, with respect to nadir direction, can improve the retrieval accuracy because those angle conditions can result in significant difference for components' fractions and DBT under different viewing directions; 2) narrow channels can produce better results than broad channels. The new method was finally applied to a multiangular MIR and TIR data set acquired by an airborne system, and a modified kernel-driven BRDF model was used for angular normalization to the surface temperature for the first time. The difference of the retrieved emissivity and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) emissivity was found to be approximately 0.012 in the study area.

Land surface temperature (LST) is crucial for a wide range of applications such as meteorology, climatology, and hydrology. In this study, we develop a method to normalize the Terra-MODIS LST to the same local solar time. An empirical relationship is established to estimate the slope of LST versus local solar time from the MSG-SEVIRI brightness temperature at the top of the atmosphere during the period 10:00-12:00 and 21:00-23:00 local solar time. This relationship is then used to normalize the Terra-MODIS LST to the same local solar time. The results indicate that the spatial variations of the MODIS LST caused by different local solar time are removed after the temporal normalization. The temporal normalized LST may become more suitable for global climate studies.

In this paper, a generalized regression neural network (GRNN) is used for land surface temperature (LST) retrieval from advanced microwave scanning radiometer-earth (AMSR-E) passive microwave data. To make neural network method more representative of the real situations, the simulated data under various atmospheric and surface conditions is generated with the aid of monochromatic radiative transfer model and the advances integral equation model, and is used to train GRNN, combined with AMSR-E measurements and MODIS LST product on the same platform (Aqua satellite). Because of the lack of simultaneous ground LST measurements in large scale, MODIS LSTs are taken as actual ground LST measurements. Through detailed analysis, the datasets in AMSR-E channels 23.8 V, 36.5 V, 89.0 V and 89.0 H GHz with the smallest root mean square error (RMSE) are used for LST retrieval, and the results show that more than 70% of errors are within 3 K, and the RMSE is 4.66 K.

The crowning objective of this research are to analyze precipitation character of Cb for different dynamical characters in Huai river basin(HRB) with China's first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. Firstly, 5 cloud patch dynamic parameters with respect to life stage and moving parameters are derived based on the Cb tracking method the author has proposed by combing artificial neural network (ANN) cloud classification[1], and cross-correlation-based approach to track Cb patch motion. Secondly, Cb precipitation over different life cycles and motion characters are analyzed. The result shows that: 1) Rain probability has a similar variation to rain rate, and rain rate is generally not more than 6 mm/hour, and probability is randomly higher than 50%. 2) Both rain rate and probability of single Cb is lower than that of complicated Cb which involves cell-merger and cell-split of some minor Cb patches. 3) Motion features such as horizontal moving speed of cloud patch (HMSP), horizontal moving direction of cloud patch (HMDP), and vertical moving character of cloud patch (VMCP) have no obvious impact on rain.

To resolve uncertainties in evapotranspiration (ET) estimates caused by the retrieval error of remotely sensed data, this study develops an evaporative fraction (EF) parameterization based on surface energy balance and the assumption of generally invariant EF during the daytime. EF is deduced as a function of temporal change of surface temperatures, temporal change of air temperature, temporal change of net radiation, and fractional vegetation cover. The EF parameterization is evaluated by the simulated data from a soil-vegetation-atmosphere transfer model with a coefficient of determination (R2) of 0.786 and a root mean square error (RMSE) of 0.117. When the EF parameterization is used to estimate the daily ET of the Yucheng station in North China by in situ measurements, the estimated results are acceptable with an RMSE of 0.7 mm (relative RMSE of 25%) and an R2of 0.837.

Land surface temperature and emissivity separation (TES) is a key problem in thermal infrared remote sensing. Current TES methods were proposed and succeeded to apply for the retrieval of land surface temperature and emissivity for the materials with emissivity close to 1. This work addressed the performance of linear spectral emissivity constraint (LSEC) method proposed by wang et al. (2011) for the TES of hyperspectral TIR data for contrast samples (high- and low- emissivity materials). The simulated hyperspectral TIR data are used for analysis and generated with six MODTRAN standard atmospheric profiles by hyperspectral atmospheric radiative transfer model (4A/OP). The influence of initial emissivity estimation is considered in this paper. The results show that initial emissivity estimation has a great impact on the performance of LSEC. LSEC method performs a fairly good result when the initial emissivity is close to the true value, and the RMSEs of temperature and emissivity are smaller than 0.5K and 0.01 when initial emissivity is good. However, the performance is worst when the initial emissivity has a great deviation.

Surface evaopotranspiration (ET) is one of the significant water and energy components in the land and atmosphere system. Remote sensing technology provides opportunity to map surface ET at large heterogeneous area. However, this ET is generally produced at the instantaneous scale. This paper investigated in the constant reference evaporative fraction upscaling method whether the use of a variable canopy resistance in the reference ET estimation from the Penman-Monteith equation could improve the daily ET estimate. Near-surface meteorological variables and eddy covariance system measurements used as the model inputs and ground-truth were collected from late April 2009 to late October 2011 at the Yucheng station in Northern China. Preliminary results showed that it was not an imperative step to use a more complex parameterization of canopy resistance to estimate the reference ET when the constant evaporative fraction method was applied to upscale the instantaneous ET to daily value.

In this paper, great efforts are focused on the temperature and emissivity separation (TES) from hyperspectral thermal infrared data. However, instead of proposing new method, the performances of several published TES methods, including iterative spectrally smooth temperature emissivity separation method (ISSTES), automatic retrieval of temperature and emissivity using spectral smoothness method (ARTEMISS), spectral smoothness method (SpSm), downwelling radiance residual index method (DRRI) and linear spectral emissivity constraint method (LSEC) are analyzed under different instrument characteristics, including the shifting of spectral and the broadening of the full-width half-maximum (FWHM), with the simulated data. The results shows that LSEC has the most robust and accurate performance. DRRI also has a good performance, but a channel selection procedure is required before the use of this method. ISSTES, ARTEMISS and SpSm are more sensitive to the instrument characteristics with some larger errors than other two methods.

This paper proposed two methods to estimate the instantaneous downwelling surface longwave radiation (RL, D) using the MODIS measurements observed at the top of the atmosphere (TOA) over the Tibetan plateau region for clear-sky conditions. One is the method proposed by [2] and refined in this work, and the other is an artificial neural network (ANN) method. The upwelling surface longwave radiation (RL, U) was estimated using the Stefan-Boltzmann law with MODIS surface temperature/emissivity products (MOD11_L2). The two methods are all based on the atmospheric transfer simulation. The net surface longwave radiation (Rn, l) can then be obtained by differing the RL, Dand the RL, U. The results showed that the RMSEs of the estimated RL, Dand measured RL, Dwith the first method are smaller than those of with the ANN method for the sites over the Tibetan plateau region.

This letter presents a method to calculate the width ω over the half-period of the cosine term in a diurnal temperature cycle (DTC) model. ω deduced from the thermal diffusion equation (TDE) is compared with ω obtained from solar geometry. The results demonstrate that ω deduced from the TDE describes the shape of the DTC model more adequately around sunrise and the time of maximum temperature than ω obtained from solar geometry. Additionally, taking into account the physical continuity of land surface temperature (LST) variation, a day-to-day temporal progression (DDTP) model of LST is developed to model several days of DTCs. The results indicate that the DDTP model fits in situ [or Spinning Enhanced Visible and Infrared Imager (SEVIRI)] LST well with a root-mean-square error (RMSE) less than 1 K. Compared with the DTC model, the DDTP model slightly increases the quality of LST fits around sunrise. Assuming that only six LST measurements corresponding to the NOAA/AVHRR and MODIS overpass times for each day are available, several days of DTCs can be predicted by the DDTP model with an RMSE less than 1.5 K.

Soil moisture (SM) is a major concern in the earth science. In previous studies, in the process of retrieval of SM from remote sensing data, surface roughness effects on the retrieval of SM is obvious and a priori knowledge of surface roughness is necessary. In this paper, a simple method to retrieve SM from passive microwave data is proposed. Using the proposed method, surface roughness effects on soil moisture retrieval can be reduced. Result of sensitivity analysis shows it can be a promising method to retrieve SM. Both simulated data and actual data have been used to retrieve SM with the proposed method in this work. The result shows that the SM can be obtained with a RMSE of 1.14% from the simulated data and 1.7% from actual data.

This study firstly analyses the spatial representation of LAS (Large Aperture Scintillometer)-observed heat fluxes for a complex surface; and then evaluates the performance of SEBS model applied to a complex surface in comparison with in situ measurements. The results showed that LAS observation is indeed more stable than EC measurements even for complex surfaces, and the sensible heat flux from LAS is less than that from EC observations because of some land types with more evapotranspiration included into LAS footprint. SEBS overestimated latent heat flux at QYZ station in southern China because of the underestimation of H, but SEBS-estimated turbulent fluxes are more consistent with the LAS measurement.

This work addressed the estimation of the directional reflectance in Middle Infra-Red (MIR) channel from the data acquired by the Stretched Visible and Infrared Spin Scan Radiometer (SVISSR) onboard Chinese geostationary Meteorological satellite FengYun 2C (FY-2C). SVISSR/FY-2C sensor acquires image covering the whole disk with a temporal resolution of 30 minutes. The MIR directional reflectance retrieval procedure can be seen as follows. Firstly, the atmospheric profiles data provided by European Centre for Medium-Range Weather Forecasts (ECMWF) were used to correct atmospheric influence with the radiative transfer code (MODTRAN 4.0). Secondly, the bi-directional reflectance in SVISSR/FY-2C MIR channel 4 (3.8 micron) was estimated from the combined MIR and TIR channel with day-night SVISSR/FY-2C data. Finally, a BRDF model referred to as the RossThick-LiSparse-R model was used to estimate the directional reflectance in MIR channel from the time-series bi-directional reflectance data. The results have been demonstrated that the method can be applied well to estimate the directional reflectance in MIR channel of SVISSR/FY-2C sensor.

In this paper, an improved method is proposed to simultaneously retrieve land surface temperature (LST), emissivity (LSE) and atmospheric profiles. This method employed the linear spectral emissivity constraint to efficiently reduce the number of retrieved variables. The proposed method was validated with some simulations. The initial guesses were derived from a neural network model. This method could greatly improve the accuracies of LST, LSE and atmospheric profiles. The RMSE of LST was decreased from 5.12 K (the initial guesses) to 1.59 K (the physical retrieved). The retrieved emissivity spectrum was in good agreement with the actual spectrum. An improvement of 1K in the tropospheric temperature was also been found. Those results showed that the proposed method is capable of improving the retrieval accuracies of land surface and atmospheric parameters with the remotely sensed thermal infrared data.

An operational statistical method suitable for nearly real-time estimate of land surface and atmospheric parameters was developed and applied to the Infrared Atmospheric Sounding Interferometer (IASI) observations. The proposed method utilized three steps to solve the ill-posed problems and to stabilize the solution in a fast speed regression manner: 1) the atmospheric profiles and land surface emissivity spectra were expressed by their eigenvectors to reduce the number of unknowns; 2) a ridge regression procedure was introduced to improve the conditioning of the problem and to lessen the influence of noises; 3) a set of optimal channels was selected to decrease the effect of forward model errors or uncertainties of trace gases, and to increase computational efficiency. The retrieval results using the independent simulated data indicate the proposed method is promising. The root mean squared error (RMSE) of land surface temperature is 3.5 K, the RMSE of land surface emissivity at the selected channels is 0.01, and the RMSE of atmospheric temperature and moisture profile are about 2.0K and 0.001g/g, respectively.

MODIS land surface temperature (LST) products have been widely used in numerous applications. Each pixel within the MODIS LST products is acquired at different local solar time even though they are in the same granule. A temporal consistency and spatial comprehensiveness data set will benefit us in the utilization of the LST products in related applications and researches. In this study, a diurnal temperature cycle (DTC) model was employed to normalize the MODIS LSTs to the same local solar time. The MODIS LSTs were derived from the Terra/MODIS and Aqua/MODIS LST products (MOD11_L2 and MYD11_L2, respectively). The results at daytime only are presented because the larger LSTs heterogeneity makes the comparison of LSTs before and after the temporal normalization much clearer. The preliminary results indicate that the spatial variations of the MODIS LSTs caused by different local solar time are removed after the temporal normalization. The temporal normalized LSTs may become more suitable for the analysis of land surface processes.

Total precipitable water (TPW) is an important atmospheric parameter in many applications. A method was proposed to estimate TPW from thermal infrared hyperspectral data. First, 21 channel groups were selected to retrieve TPW. Then, two indices, namely, the differenceand the ratio-depth in each channel group, were used as the measurement of the water vapor absorption. By multivariate regression, the relationship between the TPW and the indices was established. Finally, this relationship was applied to the simulated thermal infrared hyperspectral data. Results showed that the root mean square error (RMSE) of the model is 0.102 g·cm-2, and the relative error is 8.1%. The proposed method needs to be further refined in the future work, including the complete elimination of the Earth's emission in the retrieval.

Owing to the ill-posed problem of radiometric equations, the separation of land surface temperature (LST) and land surface emissivity (LSE) from observed data has always been a troublesome problem. On the basis of the assumption that the LSE spectrum can be described by a piecewise linear function, a new method has been proposed to retrieve LST and LSE from atmospherically corrected hyperspectral thermal infrared data using linear spectral emissivity constraint. Comparisons with the existing methods found in literature show that our proposed method is more noise immune than the existing methods. Even with a NEΔT of 0.5 K, the rmse of LST is observed to be only 0.16 K, and that of LSE is 0.006. In addition, our proposed method is simple and efficient and does not encounter the problem of singular values unlike the existing methods. As for the impact of the atmosphere, the results show that our proposed method performs well with the uncertainty of the atmospheric downwelling radiance but suffers from the inaccuracy of the atmospheric upwelling radiance and atmospheric transmittance, which implies that an accurate atmospheric correction is still needed to convert the radiance measured at the satellite level to the at-ground radiance. To validate the proposed method, a field experiment was conducted, and the results show that 80% of the samples have an accuracy of LST within 1 K and that the mean values of LSE are accurate to 0.01.

Land Surface Temperature (LST) is a major concern in the earth science. Accurately retrieving LST from passive microwave data will promote work in many other research fields. In this paper, a simple linear relationship is developed to relate the microwave surface emissivities at vertical and horizontal polarizations for the channels of Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) instrument. On the basis of this relationship and the radiative transfer equation, a method is also proposed to derive directly LST from AMSR-E data, provided that the volumetric soil moisture and atmospheric quantities are known or can be estimated a prior. The preliminary validation results indicate that LST can be obtained with a RMSE of 1.4 K from the simulated data with NEΔT=1.0 K, as for the actual AMSR-E data over the desert region, compared with MODIS LST product, the proposed method can give an estimation of LST with a RMSE of 4.9 K.

This work aimed to refine the Moderate Resolution Imaging Spectroradiometer (MODIS) based snow cover algorithm for the Hindu Kush-Himalayan (HKH) region. Taking into account the effect of the atmosphere and terrain on the satellite observations at the top of the atmosphere (TOA), particularly in heavily rugged Tibet plateau region, the surface reflectances were retrieved from the TOA reflectances after atmospheric and topographic corrections. To reduce the effects of the snow/cloud confusion, a normalized difference cloud index (NDCI) model was proposed to discriminate snow/cloud pixels, apart from use of the MODIS cloud mask product MOD35. Furthermore, MODIS land surface temperature (LST) product MOD11_L2 have been used to ensure better accuracy of the snow cover pixels. Comparisons of the resultant MODIS snow cover with those obtained respectively from high resolution Landsat ETM+ data and the MODIS snow cover product MOD10_L2 for the Mount Everest region at different seasons, showed overestimation of the MOD10_L2 snow cover with the differences of 50%, whereas the improved algorithm can estimate the snow cover for HKH region more precisely with absolute accuracy of 90%.

Significance of Evapotranspiration (ET) has been realized in disciplines of hydrology, meteorology and agriculture from a number of studies. A parameterization based on the spatially contextual information of surface temperature-vegetation index, namely Ts-VI triangle method, is applied to estimate regional ET from remotely sensed data acquired at the Changwu agro-ecological experiment station. Surface net radiation (Rn) is estimated also from MODIS/Terra products. Ratio of soil heat flux (G) to Rnis determined using a linear combination of G/Rnat bare soil and fully vegetated surface. Reasonably good agreement between estimated and measured sensible heart flux from Large Aperture Scintillometer is observed with RMSD about 48 W/m2.

This paper makes an attempt to establish a generalized neural network for simultaneously retrieving atmospheric profiles and surface temperature from hyperspectral thermal infrared data. To generate the simulated data covering the whole actual situations, the distributions of surface material, temperature and atmospheric profiles are elaborated carefully. The simulated at-sensor radiances are divided into two sub-ranges, one in atmospheric window and another in water absorption band. The simulated data are transformed in the eigen-domain in both sub-ranges and used as the network inputs. The atmospheric profiles, surface temperature and emissivity are used as the outputs after the eigen-domain transformation. The validation of the trained network indicates that a RMSE of surface temperature around 1.6K, a RMSE of temperature profiles around 2K in troposphere and a RMSE of total water content around 0.3g/cm2can be obtained. The results from the net can be used as initial guess of the physical retrieval model.

This paper makes an attempt to address the scaling problem of Leaf Area Index (LAI) and to analyze the propagation of scaling effect of LAI. On the basis of the Taylor series expansion and following the general scaling procedure, it is demonstrated that the magnitude of the scaling effect is the product of the degree of the non-linearity of the retrieval model and the spatial heterogeneity of input variables involved in this model. Finally, a scaling correction model is proposed to correct for the scaling effect of LAI. The validation using the simulated data indicates that the proposed scaling correction model of LAI gives promising accuracy if the spatial heterogeneity is well characterized by its wavelet variance. The RMSE and relative error of retrieved LAI induced by the scale effect can be greatly reduced after scaling correction. The scaling propagation analysis of LAI reveals that the scaling effects caused by several non-linear components may compensate for each other, which would enhance our confidence in using LAI product over heterogeneity areas.

Land surface temperature (LST) and vegetation index or Fraction of Vegetation Cover (FVC) triangle space in regional scale has been demonstrated to be an effective way to monitor surface soil moisture condition. In this study, LST mid-morning rising rate from geostationary satellite data is applied instead of LST in the triangle space. A new soil water dryness index (Temperature Rate Vegetation Dryness Index, TRVDI) is presented from the LST mid-morning rising rate - FVC space to reflect surface soil moisture condition. Regional TRVDI is calculated over a region of the Iberian Peninsula using MSG SEVIRI data recorded on July 2006. The validation was performed with AMSR-E soil moisture product and Anticipant Precipitation Index (API) for two meteorological stations in the area. Results indicate that TRVDI reflects the variation in soil moisture to some extent and is suitable to monitor regional surface soil moisture and temporal variation.

Land surface temperature (LST) and emissivity are required for many applications. Several methods have been proposed to retrieve these two parameters from hyperspectral data, some of which are based on the spectral smoothness of emissivity. To analyze the sensitivity of those methods to various measurement errors, hyperspectral TIR data are first simulated using radiative transfer model 4A/OP (Operational Release for Automatized Atmospheric Absorption Atlas) with different atmospheric profiles and surface parameters, and then the sensitivity of the Downwelling Radiance Residual Index method to different sources of error is analyzed. In terms of resulting errors in LST, results show that: 1) the method is not very sensitive to the uncertainties of atmosphere. An error of 1.47 g/cm2on water vapor content for a sub-arctic summer atmosphere (2.1 g/cm2) only leads to an error of 1.8 K for rock2 (the worst case). 2) Satisfactory results are obtained by this method over heterogeneous land surface. LST retrieval error is less than 0.3 K for all atmospheres.

Atmospheric correction plays an important role in the retrieval of land surface temperatures and emissivities from remotely sensed thermal infrared images. When imaging technology upgrades from multispectral to hyperspectral, an opportunity appears that atmospheric compensation can be resolved only according to hyperspectral thermal infrared data itself. A set of methods is now proposed to carry out atmospheric correction for the purpose of land surface temperature/emissivity separation: A segmental linear model is proposed to retrieve water vapor line absorption transmittance, a ¿H2O-CO2two channel groups¿ method is designed to retrieve water vapor continuum absorption transmittance, and a procedure to extract atmospheric upwelling radiance is presented. Tests with the simulated hyperspectral thermal infrared (TIR) data demonstrate that these techniques can provide good results for atmospheric compensation.

Land surface temperature, land surface emissivity and atmospheric profiles are all of great importance in many applications. As the at-sensor radiances are dependent on both the land surface parameters (temperature and emissivity) and atmospheric conditions, it is difficult to simultaneously retrieve these parameters with a high accuracy from multi-spectral radiances measured at satellite level. However, some studies have recently shown that hyperspectral thermal infrared data could be used to derive these parameters simultaneously from space. This paper tries to explore the possibilities to recover with an acceptable accuracy both the geophysical properties and the atmospheric parameters from the hyperspectral thermal infrared data using the neural network technique. The results show that the land surface temperature can be obtained with a RMSE=0.24 K and the atmospheric profiles can also be retrieved with relatively high accuracy. However, further work has to be performed to improve the retrieval accuracy in the near future.

Atmospheric corrected Landsat Enhanced Thematic Mapper Plus (ETM+) near-infrared (NIR) and shortwave infrared (SWIR) band reflectances are used to develop a new index to monitor vegetation water content (VWC) in terms of equivalent water thickness (EWT, cm). This paper outlines the first part of a research program to investigate the potential and physical basis of wavelengths in the optical domain to assess the VWC. Then, a method called vegetation water content index (VWCI) were developed using SWIR, and NIR wavelengths of ETM+ data. The relationship between the EWT at canopy level is explored through linking leaf reflectance data obtained from PROSPECT with canopy reflectance from SailH and in-situ measurements. Significant correlations are found between canopy EWT and the developed index for both modeled and ground measured data.

HJ-1B satellite is one of the small satellites in the constellation for disaster prediction and monitoring which will be launched in 2008. The infrared sensor, which is one of the payloads of HJ-1B satellite, contains the MIR and TIR channels. The improved capabilities of HJ-1B data offer an opportunity for the computation of subpixel fire temperature and fire area. The simulated HJ-1B MIR and TIR channel images are used in this paper for the algorithm test. Fires with various sizes and temperatures are simulated in a wide range of terrestrial biomes and climates conditions by MODTRAN 4. A bispectral method developed by L. Giglio and J. D. Kendall is adopted to retrieve the temperature and area of a subpixel fire within an otherwise homogeneous pixel. It is evident that HJ-1B satellite data are more sensitive to the smaller and the cooler fires than that of MODIS or AVHRR Data. For the HJ-1B data, if the fire area is about 450m2and fire temperature is about 1000K, it can also offer a capability of retrieving the fire temperature and area in a relatively high accuracy. It has been demonstrated that the accuracy will increase with the growing fire area or temperature. By sensitivity analysis it has been found that the uncertainties of the retrieved fire temperature and area using HJ-1B data are about 10.0% and 30% at the given simulation condition.

Among the key parameters acquired by remote sensing inversion, vegetation fraction cover is one of the crucial variables. Component temperatures inversion and leaf area index (LAI) inversion all have close relations with the vegetation fraction cover. The objective of this study is to develop a method to estimate the vegetation cover fraction from satellite observation. Traditional methods of inferring vegetation fraction cover from satellite remote sensing include spectral mixture analysis (SMA) and scaled normalized difference vegetation index (NDVI). Those methods often rely on a series of steps in the processing chain, including atmospheric correction, surface angular correction and so on. Generally, those procedures are very computationally demanding. In addition, the errors associated with each procedure may be accumulated and significantly affect to the accuracy of the final products. In this study, a new retrieval methodology is proposed to calculate vegetation fraction cover over mixed pixels directly from the AATSR spectral reflectance data at top-of-atmosphere (TOA). The method consists of extensive radiative transfer simulations under a wide variety of solar illumination and sensor view conditions, atmospheric profiles, aerosol types and conditions and vegetation canopy leaf angle distributions. The derivation of vegetation fraction cover from TOA observations requires several steps of processing.Important steps include, (1) Preparing for the model input variables: foliage and soil spectral data on red, green and near-infrared bands which measured from two kinds of vegetation and three kinds of soil in the field experiment; (2) Generating a database based on a canopy radiative transfer model, Scattering by Arbitrarily Inclined Leaves (SAIL), and a hybrid linear model with the spectral data combined with vegetation geometric construction data and observation geometric data; (3)Atmospheric correction that converts surface ensemble reflectance to TOA ensemble reflectance based on a radiative transfer model, Second Simulation of the Satellite Signal in the Solar (6S); (4) Mapping the relationships between spectral directional ensemble reflectance and vegetation fraction cover through a nonlinear regress method. The correction coefficients of the surface vegetation fraction cover computed with AATSR are provided.vegetation fraction cover retrieval from TOA data of AATSR does not exceed by 6% at nadir view and 9.7% at forward view, respectively. The performances of input parameters on estimates of vegetation fraction cover are given compared with the "true" surface vegetation fraction cover. The aim of estimating vegetation fraction cover is to prepare for inversing component temperatures using AATSR data, in which process vegetation fraction cover is an important parameter.

The central problem of temperature and emissivity separation (TES) is, as Realmuto had pointed out, that we obtain N spectral measurements of radiance and need to find N+1 unknowns (N emissivities and one temperature), if the atmospheric perturbations are well corrected for. Thus, one constraint must be found in the retrieval to obtain the realistic solution for the temperature/emissivity separation. A new index called `Downwelling Radiance Residual Index' (DRRI) is proposed to provide this type of constraint. Tests with the simulated hyperspectral thermal infrared (TIR) data sets demonstrate that this new index can provide an accurate and fast Temperature/Emissivity separation.

In this paper, agricultural and hydrological drought definitions are adopted to estimate the severity of drought in northern China in recent years. Particularly crop transpiration as important parameter is added in the drought index algorithm. Land surface model Noah are used and driven by a combination of meteorological reanalysis dataset (NCEP GDAS) and high resolution precipitation (CMORPH) and surface parameters from satellites (MODIS). The seasonal or yearly surface parameters (such as Albedo and LAI) from climatology are replaced by monthly data derived from MODIS, in order to represent the vegetation dynamics more accurately. Products for Crop transpiration and soil evaporation are derived at passing time of MODIS satellites. Temperature products of MODIS are adopted and are validated by simultaneous observation data of Dongping lake in Shandong province of China. Using vegetation transpiration (LEv) and latent evaporation (LE0= Rn-G) with high every 3 hours time resolution and 1km space resolution in north China, plant water stress index (PWSI) can be got. It is feasible that a combination of the land surface models and the two sources ET remote sensing model to monitoring drought using PWSI drought index according to the application of the method in North China.

Four radiative transfer equations for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) bands 11, 12, 13, and 14 are built involving six unknowns (average atmospheric temperature, land surface temperature, and four band emissivities), which is a typical ill-posed problem. The extra equations can be built by using linear or nonlinear relationship between neighbor band emissivities because the emissivity of every land surface type is almost constant for bands 11, 12, 13, and 14. The neural network (NN) can make full use of potential information between band emissivities through training data because the NN simultaneously owns function approximation, classification, optimization computation, and self-study ability. The training database can be built through simulation by MODTRAN4 or can be obtained from the reliable measured data. The average accuracy of the land surface temperature is about 0.24 K, and the average accuracy of emissivity in bands 11, 12, 13, and 14 is under 0.005 for test data. The retrieval result by the NN is, on average, higher by about 0.7 K than the ASTER standard product (AST08), and the application and comparison indicated that the retrieval result is better than the ASTER standard data product. To further evaluate self-study of the NN, the ASTER standard products are assumed as measured data. After using AST09, AST08, and AST05 (ASTER Standard Data Product) as the compensating training data, the average relative error of the land surface temperature is under 0.1 K relative to the AST08 product, and the average relative error of the emissivity in bands 11, 12, 13, and 14 is under 0.001 relative to AST05, which indicates that the NN owns a powerful self-study ability and is capable of suiting more conditions if more reliable and high-accuracy ASTER standard products can be compensated.

The water vapor is the main absorb substance of the atmosphere in thermal infrared bands, but the distribution of water vapor amount in the ground varied greatly, so one radio sounding couldn’t represent the water vapor distribution of different pixel in one image. AATSR (Advanced Along-Track Scanning Radiometer) sensor could provide radiance measurements of two channels at two observation angles. So it is possible to retrieve the atmosphere parameters and SST (Sea Surface temperature) synchronously. In order to make the system of equations determined, the two atmosphere parameters that could express whole atmosphere profiles must be confirmed. As the retrieval accuracy is requested, the surface reflected atmospheric downward radiance effect couldn’t be ignored. A climatologically data set of 1761 different radio soundings, the SATIGR database, have been used to develop a new atmospheric correction method, the two atmosphere parameters have been proved that could represent the transmittance, atmospheric upward radiance and downward radiance of different wave band and different view angle, they are the transmittance and atmospheric upward radiance of 11μm at the nadir view angle. Introducing the appropriate sea surface BRDF model, the emissivity and the reflected downward radiance can be calculated precisely. So the SST, wind speed, and two atmosphere parameters are retrieved by Broyden iteration method, when the initial values are found using LUT (look up table) method. Simulations showed :(1) the physics-based algorithm described in this paper could provide more accurate calculation of atmospheric effect. Compared with split window method, the retrieval error of SST is little. (2) The new downwelling atmospheric radiance correct method described in this paper could provide more precise calculation of atmospheric effects. The field test using ATSR-2 (Along-Track Scanning Radiometer- 2) DATA proved this new algorithm can improve the retrieval accuracy significantly

A radiative transfer model to compute brightness temperatures in the microwave region for the soil-vegetation- atmosphere system has been developed in this study. Considering the various atmospheric conditions, a microwave brightness temperature database is generated for a wide range of surface dielectric constant and roughness properties under AMSR-E sensor configurations by using the soil-vegetation-atmosphere radiative transfer model. From the land surface emissivities in the simulated database, the linear relationships of surface emissivities between different channels for the advanced microwave scanning radiometer-earth observing system (AMSR-E) are established in this study. The analysis of the stability and the root-mean-square error (RMSE) for these linear relations indicates that the relationship using three channels is the best of all. The cloud-free AMSR-E actual satellite microwave data combining with MODIS land surface temperature product are used to validate these relationships. The linear relationship derived from the simulated database is in agreement with that from AMSR-E data. This emissivity model as a constrain condition of the radiative transfer equation is applied to retrieval LST and emissivity, and the RMSE of the results is lower than 1 K for LST, 0.0025 for emissivity. So the relationships of emissivity between different channels are very useful to derive land surface parameters directly from AMSR-E data, which will promote the application of AMSR-E data in many fields, such as climate, hydrology, and ecology.

This paper proposed to study a Vegetation Index (VI) with surface bi-directional reflectivities in MODIS near-IR and mid-IR channels. Considering the fact that the observations in mid-IR at satellite altitude during daytime consist of a combination of both reflected radiance due to sun irradiance and emitted radiance from both the surface and the atmosphere, a brief description of estimating the land surface bi-directional reflectivity in mid-IR channels from MODIS data was given. Sensitivity analysis of the vegetation index with regard to the variations of horizontal visibility was performed. The results showed that the proposed vegetation index is much less sensitive to haze in the atmosphere than the Normalized Difference Vegetation Index (NDVI). In addition, in order to compare the traditional NDVI with the new VI, NDVI and VI were calculated using MODIS data acquired over a region of Western Europe (Spain), for cloud-free days, covering a period of one month, July, 2006. The result of this comparison indicated that the proposed new vegetation index is feasible to descript the properties of vegetation and to determine the classification of vegetation, especially in the areas covered by dense smoke or industrial pollution.

Surface soil moisture is a key variable in computing several important variables of the land energy and water budget (albedo,hydraulic conductivity etc). At the same time,surface soil moisture affects the diurnal change of surface temperature. Meteorological satellite data have great potential for providing estimation of surface soil moisture with high temporal resolution on a daily basis. This paper compares some relationship between the parameters derived by fitting land surface temperature (LST) with its diurnal cycle model and surface soil moisture,The results showed that lag time (the difference of the time corresponding to maximum surface temperature and that to maximum solar net short-wave radiation) is most correlated to surface soil moisture.

We intend to propose a multiple-band algorithm which can simultaneously retrieve land surface temperature and emissivity from ASTER data. We build four radiance transfer equations for ASTER band 11, 12, 13, 14, which involve six unknown parameters (average atmosphere temperature, land surface temperature and four bands emissivity). We also analyze the emissivity characteristics of common objects about 160 kinds provided by JPL spectral database between thermal band 11, 12, 13, 14 and find that there is approximate linear relationship between them. For common 80 kinds terrors, the average emissivities error of band 11 and 14 are all under 0.01, the max emissivity error is under 0.0097 for band 11 and 14. So we can obtain six equations and six unknown parameters. In order to improve the accuracy, we can make some classification before retrieving land surface temperature. We can use three methods to resolve the equations. The first is that we make classification for image and get different equation, then resolve the equation. The second is Least-squares. The third is that, we can simulate database according to the characteristics of objects and utilize the neural network to resolve equations. The analysis indicates that the neural network can improve the practical and accuracy of algorithm.

As well known, surface emissivity is one of the key parameters in the retrieval of surface temperature. The algorithms and the measurements about it are always the focus in the field of thermal remote sensing study. Among these methods for acquiring emissivity on pixel scale, day/night algorithm is used widely, for example, MODIS LST product; on the other hand, sub-pixel weighted method is an operational algorithm. Because the emissivities of the soil vary with different water contents, it is necessary to adopt the concept of relative thermal inertia to account for this effect. In addition, other influencing factors, such as type of soil, structure of soil and vegetation cover can also lead to different emissivity. In order to optimize this algorithm further, we did the experiments using an automatic field observation system to retrieve the component emissivity of mixed ground objects in November, 2005 developed by our group. In the experiment, the observed objects were composed of four sub-pixel components which have different combinations of soil content, soil type and vegetation cover. Then, the revised algorithm and the day/night algorithm to MODIS data are compared. Similar results were found in the two experimental sites. Since the day/night method requires day and night remote sensing data in a same day, it is difficult to be applied to TM and NOAA-AVHRR data, while the new sub-pixel weighted method will be a good choice.

Although directional measurements of brightness temperature has made by a number of satellite- based and airborne sensors for a long time, interpretation of such measurements is a difficult task due to the fact that surface radiometric temperatures are resulted from energy balances in multi-scales from leaf and soil to the top of canopy or pixel. In this paper, a non-isothermal Monte-Carlo method based algorithm was proposed to model the angular emission from vegetative canopy and scale radiometric temperatures of foliage and soil to the top of canopy. This approach allows an accurate simulation of multi-reflection between foliage layers, foliage layer and soil surface, and foliage layers and sky, as well as the non-isothermal condition within canopy and between canopy layers and soil surface. Field measurements of directional thermal infrared radiation, which were collected in the Shunyi remote sensing campaign in Shunyi, Beijing in 2001 was used to preliminarily validate this non-isothermal Monte-Carlo algorithm. A reasonable agreement was archived from this validation.

According to our experiment, emissivity at pixel scale is not equal to the average value of each sub-pixel emissivities. There exist scaling rule between mixed and single emissivities, which has important significance to understand models and to improve precision for quantitative inversion of land surface temperature and fluxes. In order to search and validate the rules, an automatic field observed device for component emissivity of mixed ground objects is developed by us. The device can directly measure emissivities and their distribution for each ground object in 300 mmtimes400 mm area. It is convenient to deploy and measure. The device is suitable to study scaling effect from sub-pixel to pixel for any thermal infrared remote sensing data. So far we still do not find similar observation device like above mentioned. Observed results using the device show that the device is effective and convenient; measurement principle is correct; the emissivity data are credible. For tow-dimension objects, we obtained a good agreement between measurements and prediction. However for three-dimension objects, it is difficult to predict multi-reflection and scaling. The device shows you advantage for studying multi-reflection and scaling. More experiments will be carried out in the future.

A regression analysis between brightness of all AMSR bands and MODIS land surface temperature product provided by NASA indicated good correlation, so retrieving land surface temperature from AMSR passive data is available without ground truth data (soil moisture and land surface type). The analysis results (North-Africa, North-East China, Tibet) indicate that the radiation mechanism of surface covered snow is different from others. In order to retrieve land surface temperature more accurately, the land surface at least are classified into two groups. For non-snow covered land surface, The average land surface temperature error is about2- 3℃ relative to the MODIS LST product. For snow covered land surface, The average land surface temperature error is about 3-4℃ relative to the MODIS LST product. On the other hand, the emissivity of passive microwave is very important parameter for retrieving soil moisture. We compute the emissivity through land surface temperature retrieved by statistical regression method and make some analysis.

In the article, one universal formula presented by RH Zhang (2004) is validated based on the TM image assimilation and the surface simultaneous experiment data which was measured in remote sensing experimental field of Xiaotangshan of Beijing. Several scales was used to study: the first is the experimental field (2 m /spl times/ 2 m), another is a 30 m /spl times/ 30 m TM image validated by the experiment data, the others are images degraded using the Landsat TM image. The scaling rule of independent variables defined as those surface parameters whose values are not affected by its neighboring pixels but depend on the characteristics of the pixel itself, such as NDVI, albedo, LAI, surface emissivity and apparent thermal inertia (ATI). They are investigated using the areas composed by farmland, the areas of the same size but different water area fractions, within which the difference of the surface characteristics are obvious. Generally, the independent variables can be expressed as the form of x/y. Therefore, when degrading these parameters, the scaling rule have close relationship with the numerator x and the denominator y. In terms of our analysis, when the denominator y/sub i/ is the same for the aggregated pixels, there is no scaling difference. However, when y/sub i/ is variable, the scaling difference is affected by the magnitude of x and y, and the variance of x/sub i/ (varx) and y/sub i/ (vary) divided by the average x/sub i/ (meanx) and y/sub i/ (meany) can reduce this effect; for a moderate heterogeneous surface, the scaling difference of NDVI, LAI, emissivity and ATI can be ignored, however, for obvious contrasting surface, the scaling difference can lead to large errors; when the meanx, meany, varx and vary are in the same magnitude, it can be considered that there is the same scaling difference regardless of resolution.

First the paper discussed the importance of daily total evapotranspiration in hydrology, agriculture, forestry and environmental studies. Then the function relation between evaporative fraction and Bowen ratio was discussed based on energy balance without advection. Variation of Bowen ratio with time front China Ecosystem Research Network was classified into four types and the influential factors were analyzed. And daily total evapotranspiration was estimated based on the flux observations of the stations. The results showed that average error of about twelve percent was probably produced without considering variation of Bowen ratio with nine when retrieving daily total evapotranspiration. Finally the paper retrieved total evapotranspiration of North China Plain in DOY78, 2002 based on the hypothesis that Bowen ratio was unchanged in a day, a two-layer model and,MODIS data in combination with the weather data. The results showed that it is necessary to take variation of Bowen ratio with time into account to further improve the retrieval precision of daily total evapotranspiration based on remote sensing.

In many two-layer models, Beer's Law was adopted to separate net radiation of mixed pixel. However in many cases, the difference of albedo and surface temperature between vegetation and soil is often very large. Soil's temperature can be m ore than 10 degrees C than vegetation's. Therefore, the error is large for the separation of net radiation using Beer's Law. Net radiation is composed of short wave net radiation in visible and near-infrared band and long wave net radiation in thermal-infrared band. Therefore, in order to achieve it, besides the separation of radiometric temperature of mixed pixel, albedo of mixed pixel must be separated. Theoretical basis of the layered energy-separating algorithm of net radiation was analyzed. Instantaneous soil evaporation and transpiration and carbon dioxide assimilation flux of vegetation in North China were retrieved based on MODIS data and simultaneous measurements of Dongping Lake. The obtained results showed that the layered energy-separating algorithm of net radiation is reasonable and feasible. In addition, uncertainty of the algorithm was discussed.

This Two-layer surface fluxes model is suitable to inhomogenous soil-vegetation surface. The key step is to separate mixed pixel surface temperature. ATSR's sensor can provide two view angles for separating the temperature. However, the areas are different between the pixel at nadir view and the pixel at forward view, which would result in some errors. Furthermore, re-sampling will reduce the spatial resolution of remote sensing data. On the other hand, remote sensing data source used widely now do not provide multi-angular data, which also limits the wide use of this method. An alternative method for separating mixed pixel temperature is proposed in this paper. A different T-m2 similar to f(2) equation is established using the derivative of pixel mixed temperature to vegetation fractional cover (dT(m)/df). Combining with original T-m1 similar to f(1) equation the mixed pixel temperature can he separated. A pixel component arranging comparing method will be used to obtain (dTm/df). The essence of the method is to obtain (dTm/df) using slopes of a trapezium. The principle of this method is similar to the multi-angular method. And the method overcomes the uncertainty due to the different area measured in two view angles. Theoretical positioning algorithm of four extreme pixel temperatures in the trapezium is presented to overcome uncertainty of previous method. We did a series of experimentsin Beijing and Yucheng remote sensing experimental stations, and the results indicated that this algorithm is suitable for the purpose. The above methods were adopted to separate mixed pixel temperature in two-layer surface flux model using MODIS data and simultaneous measurements in Dongping Lake, Shandong province of China. Distribution images of soil evaporation and vegetation transpiration were obtained in North China. The method and algorithm are feasible and reasonable according to good agreements' between predicted and measured values.

In the micrometeorolotrical and microclimatic research fields, surface roughness length is one of the very important surface parameters in estimating surface fluxes. With the development of remote sensing models for retrieving surface fluxes, the concept and estimating method for surface roughness length has been developed correspondingly. This paper analyzed researcher's cognition process of the surface roughness length. An experiment was further carried out in 2004 to improve and validate Zhang's model, which was proposed in 2004. In this paper, roughness elements' geometric roughness length was retrieved on TM pixel scale, and aerodynamic surface roughness length under conditions of atmospheric stability was iterated out with field measured data. By choosing eight typical wind directions, we established a relationship between roughness elements' geometric roughness length and effective surface roughness length, which made quite an improvement for Zhang's model. The parameters of the model were optimized and more universal, and the model could be used universally. The result indicates further that effective roughness length of a certain g fetch is related closely to equivalent geometric roughness length of each pixel unit within the fetch area. The model for retrieving surface roughness length would help to improve the surface fluxes retrieval precision greatly.

The inversion of physical models in remote sensing is difficult due to its ill-posed essence. Though scientists have been realizing that the inversion result is much concerned with sensitivities of parameters, how to define the sensitivity of a parameter in inversion is still under discussion. In this paper, an "S Index" is proposed to derive S Ratio, a ratio of one input parameter's S Index to the sum of all other S Indices, as a useful sensitivity criterion. Moreover, we analyzed S index and S Ratio based on the information transfer theory. It is shown that S Ratio is related with information distribution ratio in inversion. The value of S Ratio may vary with different ground covers, soil types, moisture, geometries and bands. We took SAIL model as an example to illustrate the use of S Ratio under several typical scenes. Multi-angular datasets were generated for these scenes and further been used to retrieve 7 parameters of the model. The results suggest that the inversion accuracy is strongly correlated to S Ratio. Another two sensitivity indices are also demonstrated as a comparison. As a result, we could use it to estimate the sensitivity of parameters in a certain inversion step and which type of datasets is better for inversion under various cases. Such a priori information could be important before data selection

This work proposes a basic equation of thermal radiation interaction between surface objects on the basis of the principle of heat balance in the interface. The solution of this equation takes account of the contribution of sensible heat flux and latent heat flux more completely, compared with traditional solution for surface cooling and heating processes. By the aid of the experimental data conducted in Xiaotangshan experimental site, Beijing, both the nonapplicability of Kirchoff's law and the measurability of surface emissivity in a nonisothermal system have been highlighted. Two methods called ventilation and time-delay compensations have been proposed to reduce the error induced by change of surface temperatures of nonisothermal objects during the measurement of emissivity.

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.

Principal Component Analysis (PCA) is a classical multivariate data analysis method that is useful in linear feature extraction and data compression. It can compress the most information in the original data space into a few features. Generally, remote sensing image contains (is composed of) many different objects such as land cover classes, but for a specific purpose of remote sensing application, only a few classes may be relevant. In this paper, a new method called Modified Principal Component Analysis (MPCA) is proposed and applied to a DAIS (Digital Airborne Imaging Spectrometer) image acquired in Venice, Italy. The results show that the features form MPCA is more effective in information compression, classes separablity and classification accuracy than those form PCA.

A method called visual computing was used to solve angular four-components' proportions and directional vegetation cover fraction for discrete canopies and continuous row-planted plants. It proved effective to predict the angular characteristics on pixel scale. The method could be an alternative way to a classical solution of geometrical optical models. A visual computing method would be effective especially when the canopies' spatial distribution and shapes were irregular and could not be described statistically.

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.

The exploration of the full potential of multi-angular thermal infrared measurements is hampered by the scarcity of accurate and representative data sets. Acquisition of field data has several constraints, including lack of a suitable airborne sensor system. Development and evaluation of algorithms requires detailed and accurate radiometric data, which have to be collected in a very short time to limit noise due to the temporal variability of surface temperature controlled by the local heat balance within the canopy. Several canopy properties have to be determined, e.g. leaf area index (LAI), leaf inclination distribution, in addition to soil and foliage temperature. A data set for validation studies should comprise a range of canopies and canopy conditions. We have coped with these difficulties by using a detailed radiative transfer model (CUPID) of the soil-plant--atmosphere system to produce synthetic data sets over a wide range of canopies and hydro-meteorological conditions. These data have been first used to assess the sensitivity of the anisotropy in TIR radiance to LAI and to the soil water content. It was shown that a dry top soil and a wet root zone imply that the brightness temperature is highest at nadir viewing, opposite to the condition with a wet top soil and a drier root zone. The relative magnitude of the directional change in surface temperature compared with the difference between soil and foliage temperature was evaluated in detail over a range of LAI values. Next, the radiance data base created by RT modeling was used to produce realistic complex scenes. Land cover of a complex, irrigated agriculture scene was mapped beforehand using a TM image.

A mixture of foliage and soil is thermally heterogeneous, so the radiometric temperature of the mixture depends on view direction. A simple linear mixture model was applied to estimate the component surface temperatures of foliage and soil temperatures. The potential of directional observations in the thermal infrared region for land surface studies is a largely uncharted area of research. The availability of the dual-view Along Track Scanning Radiometer (ATSR) observations led to explore new opportunities in this direction. In the context of studies on heat transfer at heterogeneous land surfaces, multiangular thermal infrared (TIR) observations offer the opportunity of overcoming fundamental difficulties in modeling sparse canopies. Three case studies were performed on the estimation of the component temperatures of foliage and soil. The first one included the use of multi-angular field measurements at view angles of 0/spl deg/, 23/spl deg/ and 52/spl deg/. The second and third one were done with directional ATSR observations at view angles of 0/spl deg/ and 53/spl deg/ only. Different models have been proposed in literature to interpret observations of directional exitance: (1) simple geometric (deterministic) models of the system, (2) radiative transfer within a complete canopy, and (3) radiative transfer in an inhomogeneous thick layer of vegetation. Our approach is based on the third modeling concept. A target comprising a mixture of foliage and soil is characterized by the gap fraction, and observed radiance is described as a weighted sum of foliage radiance and soil radiance, with the weights being the gap fraction and its complement, respectively.

A simple and feasible method to retrieve directional fraction of vegetation cover is presented. A digital camera is adopted to capture multi-angle images for a wheat field and trees. The viewing angles are controlled by an automatic multi-angle observation device. After image processing, directional fraction cover and gap probability can be obtained.

This paper presents the status of MODIS land-surface temperature (LST) standard products. The accuracy of daily MODIS LST products has been validated in eighteen clear-sky cases with in-situ measurement data collected in field campaigns in 2000 and 2001, being better than 1 K in the range from 263 K to 322 K. Techniques to remove the LSTs contaminated with cloud effects are discussed in order to make the MODIS LST products suitable for regional and global change studies.

A key point is to separate mixed temperature into soil and crop surface temperatures for inverting transpiration and CO/sub 2/ fluxes. We here present a new way of using two-temporal phase information to separate mixed surface temperature in instead of tow-angle data. Soil and canopy surface radiometric temperatures are very close at the time when net radiation is equal to zero. We can summarize that the radiometric temperature difference between soil and mixed pixels are equal to the diurnal amplitude difference between the radiometric temperature of soil and canopy divided by the percent vegetation cover. In practice, the relationship between the diurnal amplitude of the radiometric temperature of the soil and mixed pixels can be found by experiments. Through simultaneous thermal infrared images with high spatial resolution we can also find out the radiometric temperature of the bare soil in mixed pixels as well as its diurnal amplitude for lower resolution images. The method was validated in the experiment of monitoring radiometric temperature of a wheat field in spring of 2000. We also found out this rule by thermal camera in the Shunyi and Yucheng experiments in 2001. Thus this method is feasible based on theory and experiments.

The authors have developed a physics-based land-surface temperature (LST) algorithm for simultaneously retrieving surface band-averaged emissivities and temperatures from day/night pairs of MODIS (Moderate Resolution Imaging Spectroradiometer) data in seven thermal infrared bands. The set of 14 nonlinear equations in the algorithm is solved with the statistical recession method and the least-squares fit method. This new LST algorithm was tested with simulated MODIS data for 80 sets of hand-averaged emissivities calculated from published spectral data of terrestrial materials in wide ranges of atmospheric and surface temperature conditions. Comprehensive sensitivity and error analysis has been made to evaluate the performance of the new LST algorithm and its dependence on variations in surface emissivity and temperature, upon atmospheric conditions, as well as the noise-equivalent temperature difference (NE/spl Delta/T) and calibration accuracy specifications of the MODIS Instrument. In cases with a systematic calibration error of 0.5%, the standard deviations of errors in retrieved surface daytime and nighttime temperatures fall between 0.4-0.5 K over a wide range of surface temperatures for mid-latitude summer conditions. The standard deviations of errors in retrieved emissivities in bands 31 and 32 (in the 10-12.5 /spl mu/m IR spectral window region) are 0.009, and the maximum error in retrieved LST values falls between 2-3 K.

Land surface temperature (LST) retrievals obtained from NOAA Advanced Very High Resolution Radiometer (AVHRR) are of considerable importance for climatic research. However, the accurate evaluation of LST from space has been severely limited because of the difficulty in separating atmospheric from surface effects as the surface cannot be modeled as a black-body radiator. With this goal in mind, a novel extension of the split-window technique is presented in which the atmospheric contribution to the radiance measured by the satellite is investigated by the ratioing of covariance and variance of the brightness temperatures measured in channels 4 and 5 of AVHRR/2. Furthermore, the contribution of emissivity is evaluated from coefficients that depend on the spectral emissivities in both thermal channels. Using a wide range of simulations from an atmospheric radiative transfer model it is shown that the proposed algorithm provides an estimate of LST, to within 0.4 K if the spectral surface emissivity is known, which is better than that given by the currently used split-window algorithms for LST determination. Also the limitations on algorithm accuracy are discussed considering different values of noise equivalent temperature. Finally the authors present the preliminary results obtained using the proposed method from AVHRR data over a semi-arid region-of Northwestern Victoria in Australia provided by CSIRO, and a mountainous region of Northeast of France acquired in the frame of Regio Klimat Projekt.>

Sea surface temperature (SST) algorithms for NOAA AVHRR data can determine SST with rms values of 0.7 K on a global basis. However, this figure is not compatible with the high accuracy of 0.3 K required by climate studies. Biases in the SST product, arising when the factors that increase the optical path-length (absorbents concentration in the atmosphere or viewing angles) are large, cause problems in the use of the split-window formulation for climate monitoring. The reason is that the split-window coefficients currently used are not adequate to cover for all the atmospheric variability. To show this, simulations of channels 4 and 5 of AVHRR/2 of NOAA-11 using a radiative transfer model have been made. The range of atmospheric conditions and surface temperatures introduced in the simulation covers the variability of these parameters on a worldwide scale. From these data, the authors present new split-window coefficients that take into account the atmospheric variability through the ratio of the channel transmittances, or else through the total water vapor content along the path. They also show, using simulated and actual data, that the proposed split-window algorithm has a real global character and represents an improvement over the conventional algorithms.>