Modeling Diurnal Variation of Land Surface Albedo Over Rugged Terrain

The diurnal variation of land surface albedo (DVLSA) is crucial for understanding energy budgets and climate change. As topography complicates the radiative transfer processes, the estimation of DVLSA over rugged terrain becomes challenging. In this study, the topography-coupled DVLSA model (DVLSA_T...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-13
Hauptverfasser: Han, Yuan, Wen, Jianguang, You, Dongqin, Xiao, Qing, Hao, Dalei, Tang, Yong, Piao, Sen, Liu, Guokai, Liu, Qinhuo
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Sprache:eng
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Zusammenfassung:The diurnal variation of land surface albedo (DVLSA) is crucial for understanding energy budgets and climate change. As topography complicates the radiative transfer processes, the estimation of DVLSA over rugged terrain becomes challenging. In this study, the topography-coupled DVLSA model (DVLSA_T) is developed to estimate DVLSA over rugged terrain. DVLSA_T represents DVLSA as a multiplication between the basic albedo and a diurnal variation factor. The basic albedo is the albedo at local noon with topographic effects removed, while the diurnal variation factor extends the albedo from local noon to different times of the day, accounting for topographic effects. Specifically, the diurnal variation factor of black-sky albedo (BSA) changes with the illumination geometry, integrating the topographic effects and U-shaped pattern of DVLSA. In contrast, the diurnal variation factor of white-sky albedo (WSA) is independent of illumination geometry and is solely influenced by topography. DVLSA_T shows good performance when compared with the 3-D radiative transfer simulations by the large-scale remote sensing data and image simulation framework (LESS) (BSA: coefficient of determination ( {R}^{2} ) = 0.977; root-mean-square (RMSE) = 0.013; WSA: {R}^{2} =0.982 ; and RMSE = 0.012) and sandbox measurements (blue-sky albedo: {R}^{2} = 0.904 and RMSE = 0.012). DVLSA_T also has a good agreement with in situ measurements, with an RMSE of 0.024 and an {R}^{2} of 0.738. Our results demonstrate that DVLSA_T can effectively characterize DVLSA over rugged terrain.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3466951