中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
An Improved Spatial-Temporal Downscaling Method for TRMM Precipitation Datasets in Alpine Regions: A Case Study in Northwestern China's Qilian Mountains

文献类型:期刊论文

作者Wang, Lei1,2; Chen, Rensheng1; Han, Chuntan1,2; Yang, Yong1; Liu, Junfeng1; Liu, Zhangwen1; Wang, Xiqiang1; Liu, Guohua1,2; Guo, Shuhai1,2
刊名REMOTE SENSING
出版日期2019-04-01
卷号11期号:7页码:23
ISSN号2072-4292
关键词improved downscaling method TRMM precipitation datasets processed NDVI DEM alpine mountains
DOI10.3390/rs11070870
通讯作者Chen, Rensheng(crs2008@lzb.ac.cn)
英文摘要Remote sensing techniques provide data on the spatial-temporal distribution of environmental parameters over regions with sparse ground observations. However, the resolution of satellite precipitation data is too coarse to be applied to hydrological and meteorological research at basin scales. Downscaling research using coarse remote sensing data to obtain high-resolution precipitation data is significant for the development of basin-scale research. Here, we propose improvements to a spatial-temporal method for downscaling satellite precipitation. The improved method uses a nonlinear regression model and introduces longitude and latitude based on processed normalized difference vegetation index (NDVI) and a digital elevation model (DEM) to stimulate precipitation in the Qilian Mountains during 2006-2015. The final downscaled annual precipitation (FDAP) results are corrected by observed data to obtain corrected final downscaled annual precipitation (CFDAP) datasets. For temporal downscaling, monthly downscaled data are the corrected monthly ratio multiplied by the corresponding downscaled annual datasets. The results indicated that processed NDVI (PNDVI) reflected spatial precipitation patterns more accurately than the original NDVI. The accuracy was significantly improved when the final downscaled annual precipitation data were corrected by observed data. The average annual root mean square error (RMSE) from 2006 to 2015 of CFDAP was 66.48 and 83.07 mm less than that of FDAP and original Tropical Rainfall Measuring Mission (TRMM) data, respectively. Compared with previous methods, which use NDVI and/or DEM to downscale TRMM, the accuracy of FDAP and CFDAP from the improved method was higher, and the RMSE decreased on average by 13.63 and 80.11 mm. The RMSE of monthly data from corrected monthly ratio (CMR) decreased on average by 4.93 mm over monthly data from previous monthly ratio (PMR). In addition, the accuracy of the original satellite data affected the initial downscaling results but had no significant effects on the corrected downscaling results.
收录类别SCI
WOS关键词VEGETATION INDEX ; NDVI ; ELEVATION ; VARIABILITY ; CATCHMENT ; ALGORITHM ; RAINFALL ; ALTITUDE ; IMPACTS
WOS研究方向Remote Sensing
WOS类目Remote Sensing
语种英语
出版者MDPI
WOS记录号WOS:000465549300135
URI标识http://www.irgrid.ac.cn/handle/1471x/2555466
专题寒区旱区环境与工程研究所
通讯作者Chen, Rensheng
作者单位1.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Ecohydrol Inland River Basin, Qilian Alpine Ecol & Hydrol Res Stn, Lanzhou 730000, Gansu, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714
Wang, Lei,Chen, Rensheng,Han, Chuntan,et al. An Improved Spatial-Temporal Downscaling Method for TRMM Precipitation Datasets in Alpine Regions: A Case Study in Northwestern China's Qilian Mountains[J]. REMOTE SENSING,2019,11(7):23.
APA Wang, Lei.,Chen, Rensheng.,Han, Chuntan.,Yang, Yong.,Liu, Junfeng.,...&Guo, Shuhai.(2019).An Improved Spatial-Temporal Downscaling Method for TRMM Precipitation Datasets in Alpine Regions: A Case Study in Northwestern China's Qilian Mountains.REMOTE SENSING,11(7),23.
MLA Wang, Lei,et al."An Improved Spatial-Temporal Downscaling Method for TRMM Precipitation Datasets in Alpine Regions: A Case Study in Northwestern China's Qilian Mountains".REMOTE SENSING 11.7(2019):23.

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来源:寒区旱区环境与工程研究所

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