作者:Xu, SG (Xu, Shiguang)[ 1 ] ; Wu, CY (Wu, Chaoyang)[ 1 ] ; Wang, L (Wang, Li)[ 1 ] ; Gonsamo, A (Gonsamo, Alemu)[ 2,3 ] ; Shen, Y (Shen, Yan)[ 4 ] ; Niu, Z (Niu, Zheng)[ 1 ]

REMOTE SENSING OF ENVIRONMENT

卷: 162  页: 119-140

DOI: 10.1016/j.rse.2015.02.024

出版年: JUN 1 2015

摘要

Remote sensing is an important tool to monitor precipitation over regions with sparse rain gauge networks. To provide high-resolution precipitation estimates over un-gauged areas, great efforts have been taken to downscale low-resolution satellite precipitation datasets using the Normalized Difference Vegetation Index (NDVI) and the Digital Elevation Model (DEM) based on the assumption that precipitation can be simulated by vegetation and topography proxies at various spatial scales. However, the non-stationarity of the relationship between precipitation and vegetation or topography has not been appropriately considered when low-resolution satellite precipitation datasets are downscaled using NDVI and DEM in previous studies. To overcome this limitation, a new downscaling algorithm was proposed in this study by introducing a regional regression model termed as geographically weighted regression (GWR) to explore the spatial heterogeneity of the precipitation-ND VI and precipitation-DEM relationships. The performance of this new downscaling algorithm was assessed by downscaling the latest version of monthly TRMM precipitation datasets (referred to TRMM 3B43 V7) over the eastern Tibetan Plateau and the TianShan Mountains from 025 (about 25 km) to 1 km spatial resolution, and the downscaled precipitation datasets were validated against ground observations measured by rain gauges. The validation results indicate that the high-resolution precipitation datasets obtained through the new algorithm not only performed better than the traditional downscaling algorithms, but also had higher accuracy than the original TRMM 3B43 V7 dataset Besides, we found that the performance of this new algorithm was largely dependent on the accuracy of the original TRMM 3B43 V7 data. We therefore recommend considering the non-stationarity of the precipitation-NDVI and precipitation-DEM relationships in the downscaling process, and demonstrate the possibility of downscaling satellite precipitation with NDVI and DEM at monthly temporal scale. (C) 2015 Elsevier Inc. All rights reserved.

通讯作者地址: Wu, CY (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] Univ Toronto, Dept Geog, Toronto, ON M5S 3G3, Canada

[ 3 ] Univ Toronto, Program Planning, Toronto, ON M5S 3G3, Canada

[ 4 ] China Meteorol Adm, Natl Meteorol Informat Ctr, Beijing 100081, Peoples R China