Paper Info
Title
Sloping Surface Reflectance: The Best Option for Satellite-Based Albedo Retrieval Over Mountainous Areas
Abstract
The estimation of satellite-based albedo highly depends on the surface reflectance (SR). In mountainous areas, three types of SRs I i.e., the virtual SR (VSR) that is retrieved from the atmospheric correction model, the topographically corrected SR (TCSR) that is retrieved from the atmospheric and topographic correction model, and the sloping SR (SSR) that is retrieved from the physically bidirectional reflectance distribution function (BRDF)-based mountain-radiative-transfer (MRT) model] are commonly used to retrieve land surface albedo (SA). However, which type of SR is the best option for SA retrieval has not yet been quantitatively addressed. This letter assessed the performance of these three types of SRs on driving SA by comparison with in situ albedo measurements over field sites in the Heihe River Basin, China. Our results show that these three types of albedos have consistent accuracy over flat sites with a root mean squared error (RMSE) smaller than 0.0320. Moreover, the sloping SA (SSA) retrieved from SSR shows the best agreement with in situ albedo measurements over rugged sites with a bias of 0.0008, RMSE of 0.0338, relative RMSE (RMSER) of 12.92%, and correlation coefficient (r) of 0.89, followed by the topographically corrected SA (TCSA) from TCSR with a lager bias of 0.0208, RMSE of 0.0470, RMSER of 20.24%, and r of 0.69. The virtual SA (VSA) retrieved from VSR shows the largest uncertainty than the other two types of albedos, with an RMSE of 0.0516. These results illustrate that SSR is the best option of reflectance for satellite-based albedo retrieval over mountainous areas.
Year
DOI
Venue
2022
10.1109/LGRS.2021.3069637
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Keywords
DocType
Volume
Albedo, land surface reflectance (SR), sloping SR (SSR), topographic correction
Journal
19
ISSN
Citations 
PageRank 
1545-598X
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Xingwen Lin102.70
Shengbiao Wu200.34
Dalei Hao364.49
Jianguang Wen46721.30
Qing Xiao589.06
Qinhuo Liu628085.97