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.

Land surface temperature (LST) is a crucial parameter influencing Earth-atmosphere interactions and energy balance processes. The Sustainable Development Goals Science Satellite 1 (SDGSAT-1) was recently launched to support the realization of the United Nations Sustainable Development Goals (SDGs), which provides worldwide three-spectrum wide-swath, high-resolution, and high-sensitivity thermal infrared (TIR) images. The objective of this study is to develop a modified three-channel split-window algorithm incorporating atmospheric water vapor content (W-TCSW) for LST retrieval from SDGSAT-1 images. This algorithm was developed from the existing split-window (SW) form. The parameters of the algorithm were determined based on the MODerate resolution atmospheric TRANsmission (MODTRAN) simulation results of 946 Thermodynamic Initial-Guess Retrieval (TIGR) atmospheric profiles. The W-TCSW algorithm was comprehensively compared with the SW and three-channel SW (TCSW) algorithms. The retrieval results of the three algorithms were validated with simulated datasets and in situ measurements from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites in China and the Surface Radiation Budget Network (SURFRAD) sites in USA. The SDGSAT-1 data retrieved by the W-TCSW algorithm was also intercompared with Landsat and ECOSTRESS LST products. The W-TCSW algorithm demonstrated the highest accuracy among the three retrieval algorithms (SW, TCSW, and W-TCSW). The influences of atmospheric water vapor content (AWVC) and land surface emissivity (LSE) as well as land use and land cover (LULC) on retrieval algorithms were discussed in a long-term time series. This study introduces a novel LST retrieval algorithm considering AWVC for SDGSAT-1 images and elucidates comprehensive validation and comparative assessment, expanding the application of high-spatial resolution TIR remote sensing data.

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.

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.

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

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