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

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

Land surface temperature (LST) 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.

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

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

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

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

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

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.

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

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

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

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

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

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.

The operation mode of traditional remote sensing satellite is passive. The satellite passively carries out environmental awareness and task implementation after receiving ground instructions, and lacks the ability of autonomous operation and decision-making. In order to improve the intelligent level of remote sensing satellites, a fire detection method for forest and grassland fires is proposed, and a set of fire detection system through infrared imaging is designed. Infrared spectrum imaging results are processed in orbit, and fire information, such as location and area, is obtained in real time. Then the fire information is transmitted to the ground user through the BeiDou transimission channel in China, for users of forestry, natural resources, emergency management and other relevant departments to respond quickly and provide decision support.The fire detection system scheme has a high timeliness of disaster emergency response capability and can solve the time delay problem of disaster emergency response. The system can provide accurate forest fire location, area, shape and other information, and achieve real-time, autonomous, multifunctional and intelligent detection of fire targets in orbit. The fire detection system was launched into orbit on August 4, 2022, with China's first terrestrial ecosystem carbon monitoring satellite, "Goumang". Compared with the traditional way of remote sensing satellite returning images to the ground station, this system can obtain disaster information 3 to 4 hours in advance, leaving more ample response time for the users.

HJ-1B Infrared Scanner (IRS) has accumulated long-term data, but there are no comparative studies on land surface temperature (LST) inversion algorithms for IRS data. This study compared the radiative transfer equation (RTE) and two generalized single-channel algorithms (GSCwand GSCwT) for retrieving LST from IRS data. Firstly, the coefficients of the GSCwand the GSCwTalgorithms were simulated using MODTRAN 5.2 and the TIGR atmospheric profiles. ERA5 atmospheric profiles were used for the RTE algorithm. Second, land surface emissivity was calculated using the ASTER global emissivity dataset and vegetation/snow cover products based on the vegetation cover method. Finally, the LST retrievals were evaluated using ground measurements of twelve sites during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment from June 2012 to June 2014. The results showed that the accuracy of the RTE with bias (RMSE) of 0.74 K (2.47 K) is superior to the accuracy of the GSCwT with bias (RMSE) of -1.18 K (2.50 K), followed by the GSCwwith bias (RMSE) of 1.60 K (2.77 K). The results can support the production of HJ-1B/IRS LST products.

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.

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

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

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

Three operational long-term land surface temperature (LST) products from Landsat series are available to the community until now, i.e., U.S. Geological Survey (USGS) LST, Instituto Português do Mar e da Atmosfera (IPMA) LST, and China University of Geosciences (CUG) LST. A comprehensive assessment of these LST products is essential for their subsequent applications (APPs) in energy, water, and carbon cycle modeling. In this study, an evaluation of these three Landsat LST products was performed using in situ LST measurements from five networks [surface radiation budget (SURFRAD), atmospheric radiation measurement (ARM), Heihe watershed allied telemetry experimental research (HiWATER), baseline surface radiation network (BSRN), and National Data Buoy Center (NDBC)] for the period of 2009–2019. Results reveal that the overall accuracies of CUG LST with bias [root-mean-square error (RMSE)] of 0.54 K (2.19 K) and IPMA LST with bias (RMSE) of 0.59 K (2.34 K) are marginally superior to USGS LST with bias (RMSE) of 0.96 K (2.51 K). The RMSE of USGS LST is about 0.3 K less than IPMA/CUG LST at water surface sites and is about 0.4 K higher than IPMA/CUG LST at cropland and shrubland sites. As for tundra, grassland, and forest sites, the RMSEs of three Landsat LST products are similar, and the RMSE difference among three Landsat LST products is < 0.18 K. Considering the close emissivity estimates over water surface in these three LST data, USGS LST has a better performance in atmospheric correction over water surface compared with IPMA/CUG LST. For land surface sites, the RMSE of LST increases initially and then decreases with land surface emissivity (LSE) for three Landsat LST products. This indicates that the emissivity correction has a large uncertainty for moderately vegetated surface with emissivity ranging from 0.970 to 0.980. Underestimated emissivity for USGS LST at vegetated sites leads to overestimation of LST, which could have led to the higher bias and RMSE compared with IPMA/CUG LST. For the LST retrievals for the three different sensors [i.e., Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and thermal infrared sensor (TIRS)] onboard the Landsat satellite series, the accuracies are consistent and comparable, which is beneficial for providing long-term and coherent LST.

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

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

In this study, a split-window(SW) algorithm is tested to retrieve the land surface temperature (LST) from nighttime mid-infrared MODIS data. At first, the SW algorithm's coefficients were derived using MODTRAN simulations with the TIGR atmospheric profile database. Then, the input emissivities of the SW algorithm were directly calculated using the MODIS MYD11B1 products. At last, the LST was retrieved using the Aqua MODIS bands 22 and 23 data. The retrieved LSTs were compared with the MODIS MYD11A1 products and validated using ground measurements collected from four ground sites in northwest China. The validation results indicate that the developed SW algorithm provides better accuracy than the MYD11A1 LST products, with a mean bias of −0.76K and a mean root-mean-square error (RMSE) of 1.3K. Experiments show that the split-window algorithm is also applicable in the mid-infrared channel at night, and can produce accurate land surface temperature results.

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

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

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

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

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

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

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

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

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

According to the current main application mode of remote sensing satellites, users obtain the original image data, carries on the complex data processing, and get the required substantive content by filtering results after processing. The poor data timeliness, the large amount of data transmitted from satellite to ground, and the heavy workload of the ground processing system are main drawbacks of such modes. This article presents an intelligent onboard processing and multichannel transmission concept for infrared remote sensing satellites to solve above problems. Through applying cloud detection, fire spot detection, water pollution detection and other algorithms, infrared remote sensing data of the satellite payload is processed onboard, so that typical user-focused events with exact location information are discovered. The event information can be broadcast to the ground immediately through the short message channel of Chinese Beidou Navigation System, which is a real-time data transmission channel, so application users can quickly respond. In addition, there is a mid-speed data transmission channel for slicing images of areas of interest (AOI), as well as a high-speed data transmission channel for complete image data, to meet the needs of different users. In general, this article provides an overview of onboard information extraction and multichannel distribution for infrared remote sensing satellites, which can be used as a reference for satellite applications and engineering development.

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.

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

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

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

The fine spatial resolution (~100 m) land surface temperature (LST) is a key variable of great concern in various environmental studies over urban heterogeneous regions. An improvement in the spatial resolution of the coarse spatial resolution LST is an effective way to extend its potential uses in applications that have strict requests on both the spatial and temporal resolutions. However, previous statistical downscaling algorithms were proposed mainly by addressing the spatial variability in the LST while neglecting the temporal variability. In this paper, we propose a new algorithm based on a geographically and temporally weighted regression (GTWR) model for spatial downscaling of the Moderate Resolution Imaging Spectroradiometer LST data from 1000 to 100 m. The GTWR-based algorithm with temporally and geographically varying regression coefficients can capture both the spatial and temporal variabilities in the LST from the time series data at a coarse spatial resolution for effectively reconstructing the subpixel variability in the LST at fine spatial resolution. In addition, because of a better ability to explain the LST variability over urban heterogeneous regions, a normalized difference built-up index and a digital elevation model were selected as auxiliary variables. Taking Beijing and Lanzhou as examples, the performance of the GTWR-based algorithm was assessed by comparing the results with the TsHARP and GWR-based algorithms and the Landsat-8 LST. The results indicate that the GTWR-based algorithm outperforms the above-mentioned algorithms with lower mean root mean square error (1.62 °C) and mean absolute error (1.28 °C) and better agreement between the GTWR downscaled LST and the Landsat-8 LST.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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