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A Temporal Disaggregation Approach for TRMM Monthly Precipitation Products Using AMSR2 Soil Moisture Data

文献类型：期刊论文

作者Fan, Dong1,2; Wu, Hua1,2,3; Dong, Guotao4; Jiang, Xiaoguang2,5; Xue, Huazhu6
刊名REMOTE SENSING
出版日期2019-12-02
卷号11期号:24页码:19
关键词downscaling precipitation TRMM AMSR2
DOI10.3390/rs11242962
通讯作者Xue, Huazhu(xhz@hpu.edu.cn)
英文摘要Accurate and spatially-distributed precipitation information is vital to the study of the regional hydrological cycle and water resources, as well as for environmental management. To provide high spatio-temporal resolution precipitation estimates over insufficient rain-gauge areas, great efforts have been taken in using the Normalized Difference Vegetation Index (NDVI) and other land surface variables to improve the spatial resolution of satellite precipitation datasets. However, the strong spatio-temporal heterogeneity of precipitation and the "hysteresis phenomenon" of the relationship between precipitation and vegetation has limited the application of these downscaling methods to high temporal resolutions. To overcome this limitation, a new temporal downscaling method was proposed in this study by introducing daily soil moisture data to explore the relationship between precipitation and the soil moisture increment index. The performance of this proposed temporal downscaling was assessed by downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from a monthly scale to a daily scale over the Hekouzhen to Tongguan of the Yellow River in 2013, and the downscaled daily precipitation datasets were validated with in-situ measurement from 23 rainfall observation stations. The validation results indicate that the downscaled daily precipitation agrees with the rain gauge observations, with a correlation coefficient of 0.59, a mean error range of 1.70 mm, and a root mean square error of 5.93 mm. In general, the monthly precipitation decomposition method proposed in this paper has combined the advantage of both microwave remote sensing products. It has acceptable precision and can generate precipitation on a diurnal scale. It is an important development in the field of using auxiliary data to perform temporal downscaling. Furthermore, this method also provides a reference example for the temporal downscaling of other low temporal resolution datasets.
WOS关键词DOWNSCALING ALGORITHM ; REGRESSION ; ELEVATION ; MICROWAVE ; RAINFALL
资助项目Strategic Priority Research Program of Chinese Academy of Sciences[XDA19040403] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA20030302] ; National Natural Science Foundation of China[41871267] ; National Natural Science Foundation of China[41971319] ; Key Scientific and Technological Project of Henan Province[172102110268] ; Fundamental Research Funds for the Universities of Henan Province[NSFRF170907]
WOS研究方向Remote Sensing
语种英语
出版者MDPI
WOS记录号WOS:000507333400078
资助机构Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; Key Scientific and Technological Project of Henan Province ; Fundamental Research Funds for the Universities of Henan Province
源URL[http://ir.igsnrr.ac.cn/handle/311030/131262]  
专题中国科学院地理科学与资源研究所
通讯作者Xue, Huazhu
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China
4.Heihe Water Resources & Ecol Protect Res Ctr, Lanzhou 730030, Peoples R China
5.Chinese Acad Sci, Acad Optoelectron, Key Lab Quantitat Remote Sensing Informat Technol, Beijing 100094, Peoples R China
6.Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454000, Henan, Peoples R China
推荐引用方式
GB/T 7714		 Fan, Dong,Wu, Hua,Dong, Guotao,et al. A Temporal Disaggregation Approach for TRMM Monthly Precipitation Products Using AMSR2 Soil Moisture Data[J]. REMOTE SENSING,2019,11(24):19.
APA Fan, Dong,Wu, Hua,Dong, Guotao,Jiang, Xiaoguang,&Xue, Huazhu.(2019).A Temporal Disaggregation Approach for TRMM Monthly Precipitation Products Using AMSR2 Soil Moisture Data.REMOTE SENSING,11(24),19.
MLA Fan, Dong,et al."A Temporal Disaggregation Approach for TRMM Monthly Precipitation Products Using AMSR2 Soil Moisture Data".REMOTE SENSING 11.24(2019):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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