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A Study on Simulating Directional Land Surface Emissivity Based on Kernel-Driven Models and Its Application to the Generalized Split-Window Algorithm
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DOI：10.1109/TGRS.2025.3642323.png)
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Abstract
En 中文
In radiometric measurements, the emissivity of natural objects exhibits a dependence on the viewing angle. Ignoring the angular effect of surface emissivity can increase the uncertainty of land surface temperature (LST) retrievals. To mitigate this issue, we evaluated the simulation performance of 11 parametric kernel-driven models (KDMs) and developed directional emissivity models using MYD21 and MYD03 products. Afterward, the directional and classification-based emissivities were input into the refined generalized split-window (GSW) algorithm to retrieve LSTs with and without considering angular effects (LST_GSW_DE and LST_GSW_CE, respectively). Coupled with the MYD21 LST product (LST_TES), three LSTs were evaluated via SURFRAD in situ data and ERA5-Land products. The main findings were as follows. First, the RMSEs of directional emissivity simulated by different KDMs ranged from ~0.0003 to ~0.001, and their performance differences were generally slight, indicating that parameterized KDMs demonstrate reliable simulation performance in satellite-based directional emissivity modeling. Second, the directional emissivity simulation performances of different KDMs were ranked as follows: dual-kernel model (with both hotspot and base shape kernels) $\ge $ multikernel model > single-kernel model. The USEA and GUTA-sparse models exhibited advantages over the other KDMs when simulating impervious surfaces during the daytime. Third, we evaluated the three types of retrieved LSTs via SURFRAD in situ data. The rankings of the RMSE and MBE values were consistent: LST_TES was optimal, followed by LST_GSW_DE and LST_GSW_CE, with average RMSEs of 2.47, 2.62, and 2.80 K. Furthermore, we evaluated the three types of retrieved LSTs against the ERA5-Land data, and the rankings of the RMSE and MBE values were also consistent: LST_TES was comparable to (slightly better than) LST_GSW_DE in some seasons and consistently better than LST_GSW_CE. The average RMSEs were 2.45, 2.52, and 2.60 K. In addition, the RMSE and MBE values at different viewing zenith angles (VZAs) for the three LSTs increased with increasing VZA, especially when the VZA was greater than 40°. The results demonstrated that it is feasible to use KDMs to simulate directional emissivity from satellite data, offering theoretical interpretability and addressing the issues of discrete and missing emissivity data. Future studies could be devoted to establishing new KDMs or kernels that conform to different land surface and solar illumination conditions to improve the LST retrieval accuracy.
Keywords:
Angular effects
directional emissivity
generalized split-window (GSW) algorithm
kernel-driven model (KDM)
land surface temperature (LST)
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W
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