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

The optimized sampling is very important for obtaining representative observations over heterogeneous surfaces. However, spatial heterogeneity (SH) is influenced by both randomness and structure factors and varies with scale. A comprehensive understanding of how the contribution of these factors to SH varies with scale is crucial for optimizing sampling. This study quantified the scale dependence of SH caused by structure and randomness factors based on the geostatistical attributes of semivariogram and explored the relationship between the optimal deployment of ground samples and SH dominated by randomness or structure factors. The results showed that as the plot size increased, the proportion of SH caused by spatial structure factors (${P} _{\text {SSF}}$) increased. When the plot size was larger than 20 m, the${P} _{\text {SSF}}$gradually approached 80%–100%. As the plot size increased, the optimal sample plots were distributed on the typical structural features in the area. However, when the plot size was small, the optimal samples were not necessarily located on the predominant surface types. Optimizing sampling can characterize the SH, and the optimal deployment of ground samples should comprehensively consider the plot size and the number of sample plots.

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

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

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

Land surface temperature (LST) is a vital parameter for energy budget and land surface process models. With the development of high spatial resolution remote sensing technologies, especially the wide application of unmanned aerial vehicle (UAV) in land surface observations, the acquisition of high-resolution thermal infrared (TIR) data has made the accurate extraction of varied LST products possible. However, the complex heterogeneous structure of land surface may cause adjacency effect, i.e., multiple scattering of radiation and geometric occlusion, which is inevitable in high-resolution UAV-based observations. The existing TIR temperature retrieval methods mainly focus on the one-dimensional (1D) scenarios, lacking consideration for the three-dimensional (3D) structure of actual ground objects, resulting in significant errors in extracting LST from high-resolution data. To address these limitations, we propose a novel retrieval method to eliminate the influence of multiple scattering caused by finer spatial resolution on the accuracy of LST. This method introduces a 3D radiative transfer model to account for the radiative transfer in a heterogeneous scenario, based on remotely sensed imagery, corresponding 3D structure of the ground surface and component attributes. An optimization strategy is adopted to iteratively converge toward physically consistent LST results. The proposed retrieval method is validated using the UAV-based TIR images and in-situ surface temperature measurement data obtained from the Huailai remote sensing test site in Hebei province, China. Simulated TIR image datasets of typical vegetation and urban scenes were additionally employed to validate the applicability at different values of key parameters. The factors influencing the adjacency effect were extensively analyzed. Results show that 1) the effects of adjacent objects on LST can result in an overestimate exceeding 0.9 K in cases of typical vegetation scene for TIR observations when the spatial resolution was finer than 1 m; 2) the proposed LST retrieval method based on a 3D radiative transfer model can significantly reduce the influence of adjacency effect on the accuracy of LST; 3) in addition to the sky view factor (SVF), the irradiance from the adjacent objects can also have a significant impact on the accuracy of the temperature retrieval, especially when the emissivity is relatively low. This retrieval method provides a solution for high-resolution near-surface UAV/airborne TIR data and a promising framework for enhancing the LST accuracy using multi-source geographic information assistance.

Satellite products have served as the foundation for subsequent analysis, modeling, and decision-making. However, the errors or inconsistencies of satellite products may bias or even mislead the conclusions and decisions based on them. Using ground-based observation data to directly correct the errors in satellite products provides a more relaxed and direct method for constraining the errors of satellite products. However, it is challenged by the sparsity of ground station distribution and the spatial scale mismatch between ground observations and satellite pixels. To address this issue, this study pioneers an integrated and comprehensive methodological framework for pixel-by-pixel error correction based on sparsein situsite observation data across the globe. This methodological framework comprises several core components: the error correction models over the regions within situsites based on the pixel scale ground "truth", the spatial extension model to allocate optimal error correction model for regions withoutin situsites, and finally the pixel-by-pixel error correction of satellite products. MCD43A3 v061 was taken as an example to illustrate the methodology as well as its effectiveness. The RMSE of error-corrected MCD43A3 based on the optimal correction model was reduced from 0.05 to approximately 0.02. To conclude, the results and comparative analysis shown in this study suggested that the proposed framework for pixel-by-pixel error correction of satellite products based on ground observations from sparse networks has the potential to further improve the quality of satellite products across the globe.

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.

Sea Surface Temperature (SST) is a pivotal parameter in studying the energy balance and material exchange between the ocean and the atmosphere. Obtaining high-precision SST is of significant importance for a deep understanding of the dynamic changes in the ocean and atmospheric systems. This study utilized a nonlinear split-window algorithm for deriving 40m SST from Chinese Gaofen5-02 (GF5-02) Visible and Infrared Multispectral Imager (VIMI). The coefficients of the algorithm were simulated utilizing the MODTRAN 5.2 atmospheric radiative transfer model and the global atmospheric profile library of SeeBor V5.0. The retrieved VIMI SST was validated using iQuam SST measurements (in-situ measurements) and the MODIS SST products. The results of the cross-validation demonstrate a reasonable accuracy of the produced VIMI SST products, exhibiting a bias of 1.34 K and an RMSE of 2.68 K.

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

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.

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

Land surface component temperature (LSCT) is a vital parameter in many remote sensing applied fields such as drought monitoring and evapotranspiration. Previous large-scale two-source (vegetation and soil) LSCT retrieval method seldom considered the hot-spot effect of soil, thereby failing to account for the observed higher temperatures when the view direction is closer to the sun. Thus, an algorithm using both Sea and Land Surface Temperature Radiometer (SLSTR) and the Advanced Himawari Imager (AHI) dataset was performed and validated. The validation dataset was collected from Daman, China with underlying of forest and crop. The validation results demonstrated that the proposed method, which considers the hot-spot effect, significantly improves the accuracy of LSCT estimation, as indicated by root mean square error (RMSE) values of 2.58K and 3.34K for crops and trees, respectively. This study paving the way for improved understanding and applications of LSCT estimation account for the hot-spot effect in real-world scenarios.

Land Surface Temperature (LST) is crucial for studying various surface processes. Previous validations of remotely sensed LST products gained mostly the combined results of remotely sensed observations overlaid with inversion algorithms, failing to account for the impact of multi-source data on inversion results. To address this, data from six polar-orbiting satellites within China region in 2019 were collected using a uniform inversion algorithm. The derived LST values were then validated against in-situ measurements from 13 ground sites in China. Results showed minor disparities in LST validation results among satellites, with an root mean square error (RMSE) ranging from 2.6K to 3.0K. Variation analysis revealed higher errors in extreme temperature levels and a decrease in RMSE with increasing angular distance. These findings enhance understanding of multi-source data's influence on LST inversion quality and promote collaborative utilization of satellite data.

Validation of satellite albedo products relies on reference value on the coarse pixel scale which is acquired by independent means. In previous researches, reference value was generally obtained within the nominal spatial extent of the validation pixel fromin situobservations or high spatial resolution airborne/spaceborne albedo references. Nevertheless, the signal of the validation pixel may correspond to different areas due to geometric errors. This geolocation mismatch will introduce large uncertainty into validation results, particularly for pixels covering heterogeneous areas. Therefore, this study first proposed a geometric location matching method on the coarse pixel level to establish the actual position of the validation pixel. The results show that geolocation error of the validation pixels of the high-order satellite products occurs widely. And they are not systematically shifted. The errors of reference values resulting from geolocation errors range from -10% to 25%, which are very likely to be greater than the accuracy requirement of satellite albedo products. Such errors caused by geometric shifts of validation pixels significantly amplify the errors in satellite albedo products. With this geometric location matching method, the reported relative RMSE of MCD43A3 V061 reduced from 9.8% to 3.2%, and the reported correlation coefficient increased from 0.204 to 0.611. This method is very helpful to reduce the uncertainty of validation results and identify the real accuracy of satellite albedo products. Moreover, it has the generalization ability for other numerical variables over other types of land surfaces.

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

The spatial scale mismatch between satellite andin-situ-based measurements can be reduced by deploying multiplein-situsites within the coarse pixel. However, upscalingin-situmeasurements from the ground-support scale to the coarse pixel scale is still necessary due to their “point” measurement characteristics. The previous upscaling methods were generally developed merely for thein-situmeasurements. Nevertheless, the uncertainty ofin-situmeasurements such as measurement errors and spatial representativeness errors was not dealt with. Consequently, the upscaling results inevitably suffer from errors, which will finally propagate into the pixel scale ground “truth”. For the first time, this study presents an improved upscaling method with the consideration of the uncertainty ofin-situmeasurements based on the error theory and measurement adjustment theory. The effectiveness of the corrected upscaling coefficients was evaluated by comparing the accuracy of the corrected upscaling results with those based on the upscaling coefficients without considering the uncertainty ofin-situmeasurements. The results indicate that the accuracy of the upscaling results can be enhanced by 11.06% in the condition in whichin-situmeasurements suffer from large uncertainty. However, if the uncertainty ofin-situmeasurements is negligible, the corrected upscaling model is not necessary because it does not bring many benefits. Although the effectiveness of this method was only tested on a limited study area, it makes an important first step toward a higher precision pixel-scale ground “truth”, especially when the uncertainty ofin-situmeasurements is non-negligible.

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.

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

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

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

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

Uncertainty quantification is an important part of validation, because the pixel scale reference generally suffers from uncertainty caused by different factors, lowering the accuracy of validation results. In order to take a step forward to characterize the uncertainty of validation results, this study proposed a simulated shift-based pixel matching (SSPM) method with the aim of quantifying the uncertainty caused by geometric mismatch in the multiscale validation. Furthermore, its relationships with spatial heterogeneity and subpixel size were also explored. It was found that the uncertainty caused by the geometric mismatch is nonnegligible in multiscale validation, which would obscure the true accuracy of satellite products. Spatial heterogeneity makes a positive contribution to the uncertainty caused by geometric mismatch, but the magnitude depends on subpixel size, being weaker with small subpixel size and stronger with larger subpixel size. Subpixel size is generally positively related to geometric uncertainty. But in the case of very large spatial heterogeneity, their correlation is very weak. This study is an important step toward quantitatively characterizing the uncertainties of pixel scale reference in order to increase the confidence of validation results.

Moderate-high resolution satellite missions provide an opportunity to capture subtle spatial variability in lakes; however, the sparsity of time series for individual satellite instruments cannot monitor temporal variation in the lake environment. To date, studies on the joint observations of chlorophyll-a (Chl-a) in inland lakes from multiple missions have been poorly reported. Here, we generated a harmonized Chl-a dataset for the lakes in the Yunnan–Guizhou Plateau in China from 2013 to 2022 by the Landsat 8/9 and Sentinel-2A/B virtual constellation. This study first examined the performance of four atmospheric correction processors to derive remote sensing reflectance (Rrs) from Landsat 8/9 Operational Land Imager (OLI) and Sentinel-2A/B multispectral instrument (MSI) images. We determined that the dark spectral fitting algorithm generated better Rrsthan the other processors, e.g., Rrs(561) mean absolute percentage error (MAPE)=15.2%, Rrs(665) MAPE=27.5%, and Rrs(704) MAPE=25.7%. OLI-derived Rrsat five visible and near-infrared bands showed satisfactory agreement with MSI (slope=0.94, MAPE=11.8%). The mixed density network outperformed the six state-of-the-art algorithms and other two machine learning models in retrieving Chl-a [MSI: MAPE=31.4% (N=109), OLI: MAPE=38.0% (N=74)]. The satisfactory agreement of Chl-a retrievals between the synchronous MSI and OLI images (N=2,293,821, MAPE=34.6%) supported the establishment of the virtual constellation. MSI- and OLI- derived Chl-a in nine major lakes in the studied area exhibited apparent seasonal variability from 2013 to 2022, particularly after 2017. Results highlight a solution to establish the Landsat/Sentinel-2 virtual constellation for improving the spatial and temporal resolutions of a database of lake water quality.

Validation of satellite albedo products is an essential step because their quantitative application lie in their ability to record the real state of the earth surface. Upscalingin situmeasurements to the corresponding pixel scale is necessary due to the spatial scale mismatch betweenin situand satellite measurements. Machine learning-based models have been increasingly used for upscaling because they can yield more reliable results than traditional methods. Nevertheless, the main controlling factors on upscaled results have rarely been discussed. This article explores the control factors that bring uncertainties to the upscaled results based on machine learning models. Three machine learning models, including random forest (RF),$k$-nearest neighbor (KNN), and Cubist models, were selected to upscale single sitein situ-based albedo to the coarse pixel scale. The upscaled results were carefully assessed through comparison with pixel scale albedo reference. The results indicate that the accuracy of upscaled results depends on the machine learning models, the inclusion of key variables related to albedo, the dataset selection of these variables, the amount of training data, and the sensitivity of machine learning models to these factors. Despite the dependence on control factors, the machine learning-based upscaling methods generally have excellent applicability across different spatial scales and over other untrained areas. Therefore, they open the door to generating a time series of globally, spatially continuous distributed reference datasets with sufficient length, consistency, and continuity to adequately fulfill the requirement of a comprehensive validation.

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

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

Characterizing the subpixel heterogeneity within satellite pixels is a key issue in validation. Nevertheless, it is challenging due to multi-scale problems in the geological description based on remote sensing. Based on an airborne platform, the multi-scale variation laws of several key indicators in validation including spatial heterogeneity (SH), representativeness errors, and representative area with subpixel size were analyzed and discussed. Furthermore, this article discussed the optimal subpixel size to assess SH within a coarse pixel and the optimal footprint ofin situmeasurements for building dense and sparse validation networks. SH decreases with the increase of subpixel size. And a reduction of about 10% can be obtained from 5 m$\times 5$m to 150 m$\times150$m subpixel size, depending on the degree of SH within the typical satellite pixels. And the sensitiveness of SH to subpixel size decreases gradually with the increasing of subpixel size. Ideally, SH should be assessed using maps with pixel sizes corresponding to the footprint ofin situmeasurements. Regarding the deployment of future validation networks, the footprint ofin situsites should be designed at least larger than 25 m for dense networks. And much larger footprints (e.g., 100 m) are preferred in designing sparse networks. The representativeness error is not fully related to subpixel sizes because it is affected by many factors. The findings are also transferable to model evaluation when comparing model grid values to local observations.

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.

The semiempirical kernel-driven model is commonly used for global surface reflectance characterization because of its simplicity and underlying physical meaning. However, the current kernel-driven reflectance models assume that the terrain is flat and homogeneous, and can induce significant errors in the surface reflectance estimation and subsequent parameter retrievals over rugged terrain. In this study, an improved topography-coupled kernel-driven (TCKD) reflectance model with the correction of diffuse skylight effects was proposed based on the diffused-equivalent slope model (dESM) and RossThick-LiTransit (RTLT) kernel-driven model. The TCKD model's accuracy and effectiveness were evaluated using surface reflectance simulated by the radiosity approach and the Moderate Resolution Imaging Spectroradiometer (MODIS) data. Against simulated data, the results show that the TCKD model can accurately capture the distortion of the reflectance shape and hemispherical distribution caused by the topographic effects. Compared to MODIS data, the TCKD model has an overall better performance than the RTLT model across different spatial scales and land cover types. When the mean slope is larger than 35° at the 500-m resolution, the TCKD model's near-infrared (NIR) root-mean-square error (RMSE) and the regression slope of the fitting line are 0.037 and 0.752, respectively, whereas those of the RTLT model are 0.049 and 0.645. Neglecting the diffuse skylight in the TCKD model can also lead to great bias in the reflectance retrievals. When the mean slope is 31°, as the ratio of diffuse skylight varies from 0 to 1, the NIR RMSE of the TCKD model decreases from 0.012 to 0.005, whereas that increases from 0.012 to around 0.02 if the diffuse skylight effects are neglected. These preliminary results demonstrate that the TCKD model is capable of improving the fitting ability of the kernel-driven model over rugged terrain and provides potentials for better retrieving and interpreting land surface parameters such as land surface albedo in mountainous areas.

The in situ measurements from globally distributed sparse networks provide a valuable data source for the validation of satellite products. However, the representativeness errors resulting from the spatial scale mismatch between in situ-satellite measurements and surface heterogeneity have generally limited past validation to only very few spatially representative sites, which cannot meet the requirement of a comprehensive validation. In response to this challenge, this article offers a strategy for upscaling sparse in situ measurements and removing the impact of representativeness errors on the evaluation of coarse-pixel albedo products. The main idea of the upscaling method is to establish the correspondence relationship between each subpixel albedo time series within a coarse pixel and in situ albedo time series by using high-resolution albedo maps as the prior knowledge. Furthermore, the performance of the upscaling method is carefully evaluated over the sites featured by different degrees of spatial representativeness. The results indicate that the upscaling method improves the representativeness of single-site measurements with respect to a coarse pixel, and the improvement is most significant over the sites with relatively low representativeness. Therefore, the upscaling method is particularly useful for the validation at heterogeneous sites in strengthening the reliability of validation results. It is expected to open the door to maximizing the use of existing sparse networks and generating a time series of globally distributed reference data sets with sufficient length, consistency, and continuity.

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.

Rugged terrain significantly complicates the land surface albedo modeling and retrievals in remote sensing. Neglecting the topographic effects may lead to large uncertainties when estimating land surface albedo over rugged terrain. In this study' the sensitivities of snow-free vegetation shortwave albedo to topography are quantitatively investigated and analyzed based on the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface albedo data and the global sensitivity analysis of a mechanistically-based land surface albedo parameterized model over rugged terrain. The results reveal that the topography can account for more than 30% of the total variabilities of the land surface albedo and the topographic effects on land surface albedo cannot be neglected even for the terrain with the mean slope of 10°.

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.

Accurately estimating the spatial–temporal distribution of downward surface shortwave radiation (DSSR) is essential for terrestrial ecological modeling and climate change research. The accurate georegistration of digital elevation model (DEM) has become one of the significant bottlenecks for improving the DSSR accuracy over rugged terrain. To clearly understand and quantitatively evaluate the impact of geolocation bias on the DSSR estimation under clear sky, this letter conducts a systematical simulation research in Dayekou Basin of China based on a developed remote sensing satellite-based DSSR estimation scheme over rugged terrain. The results demonstrate that the proposed approach can accurately capture the high temporal and spatial heterogeneities of DSSR, and the DSSR estimations are sensitive to geolocation bias. When the horizontal bias is lower than half a pixel, the deviations of the direct radiation could lead to above 600 W/m2due to the illumination angle effects and shadow effects. The consequence of the bias on the diffuse and reflected radiation from adjacent terrains is little because of their relatively small values and low-spatial heterogeneities under clear sky in general except for the deep valley areas. The trends of the total radiation errors with the geolocation bias are identical in different days (scenes), and the error is related to the solar zenith angle. In addition, the more rugged the terrain, the greater the influence of geolocation bias on the radiation accuracy.

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

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

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

Validation of remote sensing albedo products involves comparisons between point-scale in situ observations and footprint-scale satellite retrievals. However, the observed differences between product and in situ observations are not only attributable to intrinsic errors of satellite products but also to inadequate spatial representativeness of in situ observations. Here, representativeness errors of in situ observations and their effects on validation results were quantitatively explored. Furthermore, the contributors and their influences on representativeness errors were quantified. In the case of large representativeness errors, validation result errors are mainly controlled by representativeness errors. When representativeness errors are small, validation result errors are likely affected by other factors and can be so large that cannot be ignored. Surface heterogeneity is most positively related to representativeness errors, followed by the deviation distance of in situ site from the pixel center. The representative area surrounding in situ sites only shows a weak negative correlation with representativeness errors. The range seems to be not a good indicator of spatial representativeness of in situ sites since there is almost no relationship between them. When these factors are combined, surface heterogeneity contributes more to representativeness errors on the 500-m pixel scale, while quantitative impacts of the representative area and location deviation of in situ sites are not fully understood because magnitudes of these effects are dependent on the choice of high-resolution data set. These findings enhance our understanding about spatial representativeness of in situ observations and improve the quality of validation results based on single in situ observations.

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.

Rugged terrain complicates the BRDF modeling mainly by the modulation of sun-target-sensor geometry and shadowing effects. An improved kernel-driven BRDF model coupled with topography (KDCT) is put forward by combining the RTLSR model used in the algorithm for MODIS bidirectional reflectance anisotropies of land surface (AMBRALS) and the anisotropic reflectance model for rugged terrain (dESM). The improved model was compared with the original RTLSR model by using the simulated data based on the radiosity approach and the MODIS reflectance data. The validation results revealed that the improved KDCT model outperforms the RTLSR model without topographic consideration and can significantly improve the ability of the kernel-driven model to process the multi-angular reflectance measurements over rugged terrain.

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

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

Heterogeneous terrain significantly complicates signals received by airborne or satellite sensors. It has been demonstrated that both solar direct beam and diffuse skylight illumination conditions are significant factors influencing the anisotropy of reflectance over mountainous areas. Several models and methods have been developed to account for topographic effects on surface reflectance at the pixel level in remote sensing. However, subtopographic effects are generally neglected for low-spatial-resolution pixels due to the complex law of radiative transfer and the limitations of higher spatial resolution digital elevation models, which can lead to deviations in reflectance estimation. Accurately estimating the subtopographic effects on anisotropic reflectance over composite sloping terrain under different illumination conditions presents a challenge for remote sensing models and applications. In this paper, the diffused equivalent slope model (dESM) was developed, which is an anisotropic reflectance simulation model coupled with diffuse skylight over composite sloping terrain. The corresponding subtopographic impact factor was also proposed to exhibit how microslope topography affects reflectance over composite sloping terrain under different illumination conditions. Simulated reflectance data sets simulated by the radiosity method and Moderate Resolution Imaging Spectroradiometer reflectance data were used to evaluate the performance of the dESM model. The results reveal that the dESM model can accurately capture the reflectance anisotropy over composite sloping terrain under different illumination conditions, and the subtopographic impact factor can account for the effects of microslope topography, shadow, and illumination conditions.

Ground-based microwave radiometric measurements were carried out in 2016 by using a multi-frequency microwave radiometer at L, C and X bands (1.4, 6.925 and 10.65 GHz). The aim of the experiments was to explore multi-frequency microwave emission characteristics of the soil freeze-thaw process for model and algorithm development for the future Water Cycle Observation Mission (WCOM). Measurements were carried out on pastureland in Chengde, Hebei Province, which belongs to seasonally frozen ground of China. Soil temperature and soil moisture profiles, the frost depth, and meteorological observations were synchronously collected. It has been found that microwave radiation has different responses to soil freezing and thawing process at different frequencies.

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.

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

Surface albedo is a critical variable in the Earth's energy balance. To evaluate the quality of albedo retrieved from satellite data is crucial. In this study, the direct comparison was carried out to assess the 5-day albedo product retrieved from the Multi-sensor Combined BRDF Inversion (MCBI) model. Different land covers, such as, grassland, deciduous and coniferous forests are considered in the validation. The results show that MCBI albedos agree well with ground-based albedo measurements.

Well design and coordinated implementation of validation activity is necessary to evaluate the accuracy of remote sensing product. However, validation is not a straightforward task and remain many challenges. A generally recognized difficult issue is the inconsistence between sparse observations and remote sensing pixels, strong spatial and temporal variations of surface variables, and the intrinsic heterogeneity of land surfaces. Thus, to develop, design and conduct reasonable validation schemes and activities to acquire ground truth at pixel scale over heterogeneous land surfaces is urgently needed. This contains, from the perspective of measurement, integrating various ground observations collected at multi-scale, in order to validate different types of RSPs from site to network, especially for those land surface variables with strong spatial-temporal variations. To this end, a dedicated validation initiative has been launched in China since 2011. The main scientific objectives and research contents are to develop mathematical approaches for spatial sampling optimization to acquire the ground truth at pixel scale over heterogeneous land surfaces, to form a series of recognized and practicable technical specifications to guide validation of various RSPs, and to establish a prototype of national validation network for long term operation. Specific validation activities, such as HiWATER, were conducted from site to network, through multi-scale observations collected from multi-platform and multi-source sensors, to experimentally examine those proposed methodologies and guidelines. Following the experience of these validation exercises, we are coordinating a Chinese validation network to use standardized and recognized technical specifications in implementing future validation attempts, aiming to extend validation exercises from point scale to regional scale and to national scale across different zones.

This study describes the use of ground-based albedometer measurement based on the automatic weather stations (AWS) for validating MCD43A3 and GLASS albedo products over heterogeneous landscapes in Heihe river Basin, China. Because the footprint of ground observed albedo was far less than the spatial resolution of albedo products, high-resolution albedo imageries were used as an upscaling bridge to reduce the scale discrepancy. Based on this scheme, we present the results from an accuracy assessment of MODIS and GLASS. The validation results show that MODIS and GLASS have RMSEs less than 0.05 over large areas and over a full year of measurements.

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.

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.

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

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

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

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

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

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

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

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

Foliage and soil temperatures are key variables for assessing the exchanges of turbulent heat fluxes between vegetated land and the atmosphere. Using multiple-view-angle thermal-infrared (TIR) observations, the temperatures of soil and vegetation may be retrieved. However, particularly for sparsely vegetated areas, the soil and vegetation component temperatures in the sun and in the shade may be very different depending on the solar radiation, the physical properties of the surface, and the meteorological conditions. This may interfere with a correct retrieval of component temperatures, but it might also yield extra information related to canopy structure. Both are strong reasons to investigate this phenomenon in some more detail. To this end, the relationship between the TIR radiance directionality and the component temperatures has been analyzed. In this paper, we extend the four-stream radiative transfer (RT) formalism of the Scattering by Arbitrarily Inclined Leaves model family to the TIR domain. This new approach enables us to simulate the multiple scattering and emission inside a geometrically homogenous but thermodynamically heterogeneous canopy for optical as well as thermal radiation using the same modeling framework. In this way top-of-canopy thermal radiances observed under multiple viewing angles can be related to the temperatures of sunlit and shaded soil and sunlit and shaded leaves. In this paper, we describe the development of this unified optical-thermal RT theory and demonstrate its capabilities. A preliminary validation using an experimental data set collected in the Shunyi remote sensing field campaign in China is briefly addressed

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

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

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