Modeling Diurnal Variation of Land Surface Albedo Over Rugged Terrain

被引:0
|
作者
Han, Yuan [1 ,2 ]
Wen, Jianguang [1 ,2 ]
You, Dongqin [3 ]
Xiao, Qing [1 ,2 ]
Hao, Dalei [3 ]
Tang, Yong [3 ]
Piao, Sen [3 ]
Liu, Guokai [1 ,2 ]
Liu, Qinhuo [1 ,2 ]
机构
[1] Chinese Acad Sci, Aerosp Informat Res Inst, Natl Engn Lab Remote Sensing Satellite Applicat, Beijing 100101, Peoples R China
[2] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
[3] Chinese Acad Sci, Aerosp Informat Res Inst, Stake Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2024年 / 62卷
关键词
Climate change; Land surface; Terrain mapping; Energy management; Costs; Measurement techniques; Diurnal variation; land surface albedo; rugged terrain; sandbox; U-shaped pattern; AVERAGE NET-RADIATION; REFLECTANCE; DERIVATION; RETRIEVAL; BUDGET;
D O I
10.1109/TGRS.2024.3466951
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
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.
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页数:13
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