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 |
DOI | 10.3390/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. |
入库方式: iSwitch采集
来源:寒区旱区环境与工程研究所
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