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

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

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

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

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

Stripe noise on the remote sensing image is a general phenomenon that not only degrades the image quality but also severely limits its application. While the classical statistical method is effective in correcting common stripes caused by inaccurate calibration of relative gains and offsets between detectors, it falls short in correcting other nonlinear stripe noises resulting from minor nonlinear changes or random contaminations that occur within the same detector. Therefore, this paper proposes a novel trend repair method based on adjacent normal columns to rectify the trend of the defective column by considering the geospatial structure of the contaminated pixels to remove residual stripe noises after histogram matching. The proposed trend repair method is compared with the piece-wise method using the GF5-02 VIMI (Visual and Infrared Multispectral Imager) thermal Band 9 image to evaluate its effectiveness. Streaking (streaking metrics), SSIM (structural similarity), and PSNR (peak signal-to-noise ratio) are employed to assess the performance of the new method. Experimental results indicate that the proposed trend repair method removes residual stripe noises effectively after histogram matching.

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

Land surface temperature (LST) is an important variable in Earth science research and can be measured using various thermal infrared (TIR) observations. Due to variations in data sources and inversion algorithms, LST products yield inconsistent results, further affecting subsequent applications (e.g., drought and vegetation monitoring). Although many evaluation studies have been conducted, most of them have focused on product validation or differences between inversion algorithms. There is a lack of analysis of the data sources, which is important for the data fusion. Therefore, a consistency analysis was conducted herein. Mainstream polar-orbiting satellite data were selected, including data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Sea and Land Surface Temperature Radiometer (SLSTR), and Visible Infrared Imaging Radiometer Suite (VIIRS). The same inversion algorithm (split-window) for LST was employed across all datasets, thereby ensuring that differences in satellite data were the primary factor. Following validation based on in situ measurements, the polar-orbiting LST results were intercompared and analyzed with the LST results derived from the Himawari-8 Advanced Himawari Imager (AHI) observations. The results indicated that 1) similar conclusions were obtained from the intercomparison results and the ground-based validation results, with root mean square errors (RMSEs) for intercomparison results ranging from 2.903 K to 3.353 K; 2) based on the intercomparison results, regression analysis revealed that surface temperature status, land cover and vegetation information, and angular factors had a significant impact on the evaluation results, with t tests yielding p values less than 0.05 for all of these factors; and 3) based on a decision tree analysis, the contributions of angular factor, surface temperature status, and land surface structure were 50.9%, 31.3%, and 17.8%, respectively. These findings enhance knowledge regarding the impact of different satellite data on LST inversion results and emphasize the necessity of preprocessing before the joint application of satellite data, such as angle normalization and radiometric calibration.

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

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

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

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

High-resolution land surface temperature (LST) retrieval is a hot research topic in recent ten years, and the development of various high-resolution satellite sensors provides a data basis for this study. The Visual and Infrared Multispectral Imager (VIMI) on Gaofen5-02 (GF5-02) satellite provides 40m spatial resolution thermal infrared data ranging from 8µm to 12.5µm, including four thermal infrared channels. In this paper, we developed a split-window algorithm for retrieving LST from VIMI data. First, the two thermal infrared channels 11 and 12 of VIMI are cross-calibrated using MODIS bands 31 and 32, and then high-resolution LST was derived using the generalized split-window algorithm. The GF5-02 LST was cross-validated with the MODIS MOD21 LST products, the preliminary results indicate that GF5-02 LST shows a reasonable accuracy, with a mean bias of 0.05 K and a mean RMSE of 3.29 K.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The polynomial expression is a new and powerful model to interpret the light scattering process inside leaf/soil system and decipher the nonlinear relationship between component spectra and canopy reflectance. In our previous work, we have outlined the forward model and analyzed its feature. For models with large number of parameters, their inversion is a challenging problem. This paper presents the algorithm and strategy that makes the complex multivariant inversion problem efficient and stable

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