王宁等:Estimation of subpixel snow sublimation from multispectral satellite observations
被阅读 43 次
2018-01-30
Estimation of subpixel snow sublimation from multispectral satellite observations
作者:Wang, N (Wang, Ning)[ 1,2 ] ; Jia, L (Jia, Li)[ 1,3 ] ; Zheng, CL (Zheng, Chaolei)[ 1 ] ; Menenti, M (Menenti, Massimo)[ 1,4 ]
JOURNAL OF APPLIED REMOTE SENSING
卷: 11
文献号: 046017
DOI: 10.1117/1.JRS.11.046017
出版年: DEC 8 2017
 
摘要
Snow sublimation is an important hydrological process and its spatial and temporal variation remains largely unknown; however, few studies have been conducted to quantify its spatial variability. Our study focuses on the evaluation of two algorithms, Penman-Monteith (P-M) equation and the bulk aerodynamic (BA) parameterization of snow sublimation. The two methods were first evaluated against eddy covariance (EC) measurements of latent heat flux at towers located in the upper reaches of the Heihe River Basin (China). Both methods were in good agreement with the ground observations with high coefficient of determination (R-2) and low root mean squared error (RMSE). Next, we estimated subpixel snow sublimation using remote sensing data at a 1-km x 1-km spatial resolution. The results based on satellite data were evaluated against ground measurements at the two experimental sites. The P-M equation gave R-2 =0.75, RMSE = 8.4 W m(-2) for Dashalong site and R-2 = 0.36, RMSE = 9.1 W m(-2) for the Dadongshu site and performed better than the BA parameterization, which gave R-2 = 0.65, RMSE = 17.5 W m(-2) for the Dashalong site and R-2 = 0.06, RMSE 1/4 21.2 W m(-2) for the Dadongshu site. Overall, the results indicate that P-M is promising for estimating snow sublimation at the regional scale using satellite observations. (c) The Authors.
 
通讯作者地址: Jia, L (通讯作者)
      State Key Lab Remote Sensing Sci, Beijing, Peoples R China.
通讯作者地址: Jia, L (通讯作者)
      Joint Ctr Global Change Studies, Beijing, Peoples R China.
地址:
[ 1 ] State Key Lab Remote Sensing Sci, Beijing, Peoples R China
[ 2 ] Univ Chinese Acad Sci, Beijing, Peoples R China
[ 3 ] Joint Ctr Global Change Studies, Beijing, Peoples R China
[ 4 ] Delft Univ Technol, Dept Geosci & Remote Sensing, Delft, Netherlands