Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution

doi:10.1016/j.rse.2019.111364

	Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution
	Long, Di 2; Bai, Liangliang 2; Yan, La 2; Zhang, Caijin 2; Yang, Wenting 2; Lei, Huimin 2; Quan, Jinling 3; Meng, Xianyong 4; Shi, Chunxiang 1
刊名	REMOTE SENSING OF ENVIRONMENT
	2019-11-01
卷号	233 页码:19
关键词	Microwave soil moisture Land surface temperature Downscaling Random forest Water resources management
ISSN号	0034-4257
DOI	10.1016/j.rse.2019.111364
通讯作者	Long, Di(dlong@tsinghua.edu.cn) ; Quan, Jinling(quanjl@lreis.ac.cn)
英文摘要	Surface soil moisture (SSM), as a vital variable for water and heat exchanges between the land surface and the atmosphere, is essential for agricultural production and drought monitoring, and serves as an important boundary condition for atmospheric models. The spatial resolution of soil moisture products from microwave remote sensing is relatively coarse (e.g., similar to 40 km x 40 km), whereas SSM of higher spatiotemporal resolutions (e.g., 1 km x 1 km and daily continuous) is more useful in water resources management. In this study, first, to improve the spatiotemporal completeness of SSM estimates, we downscaled land surface temperature (LST) output from the China Meteorological Administration Land Data Assimilation System (CLDAS, 0.0625 degrees x 0.0625 degrees) using a data fusion approach and MODIS LST acquired on clear-sky days to generate spatially complete and temporally continuous LST maps across the North China Plain. Second, spatially complete and daily continuous 1 km x 1 km SSM was generated based on random forest models combined with quality LST maps, normalized difference vegetation index (NDVI), surface albedo, precipitation, soil texture, SSM background fields from the European Space Agency Soil Moisture Climate Change Initiative (CCI, 0.25 degrees x 0.25 degrees) and CLDAS land surface model (LSM) SSM output (0.0625 degrees x 0.0625 degrees) to be downscaled, and in situ SSM measurements. Third, the importance of different input variables to the downscaled SSM was quantified. Compared with the original CCI and CLDAS SSM, both the accuracy and spatial resolution of the downscaled SSM were largely improved, in terms of a bias (root mean square error) of -0.001 cm(3)/cm(3) (0.041 cm(3)/cm(3)) and a correlation coefficient of 0.72. These results are generally comparable and even better than those in published studies, with our SSM maps featuring spatiotemporal completeness and relatively high spatial resolution. The downscaled SSM maps are valuable for monitoring agricultural drought and optimizing irrigation scheduling, bridging the gaps between microwave-based and LSM-based SSM estimates of coarse spatial resolution and thermal infrared-based LST at a 1 km x 1 km resolution.
资助项目	National Natural Science Foundation of China[51579128] ; National Natural Science Foundation of China[91547210] ; National Key Research and Development Program of China[2017YFC0405801]
WOS关键词	AMSR-E ; SATELLITE DATA ; LOESS PLATEAU ; DATA FUSION ; SMAP ; ASSIMILATION ; SMOS ; TIME ; DISAGGREGATION ; RETRIEVAL
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000497601000006
资助机构	National Natural Science Foundation of China ; National Key Research and Development Program of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/130289]
专题	中国科学院地理科学与资源研究所
通讯作者	Long, Di; Quan, Jinling
作者单位	1.China Meteorol Adm, Natl Meteorol Informat Ctr, Beijing 100081, Peoples R China 2.Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.China Agr Univ, Coll Resources & Environm Sci, Beijing 100094, Peoples R China
推荐引用方式 GB/T 7714	Long, Di,Bai, Liangliang,Yan, La,et al. Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution[J]. REMOTE SENSING OF ENVIRONMENT,2019,233:19.
APA	Long, Di.,Bai, Liangliang.,Yan, La.,Zhang, Caijin.,Yang, Wenting.,...&Shi, Chunxiang.(2019).Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution.REMOTE SENSING OF ENVIRONMENT,233,19.
MLA	Long, Di,et al."Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution".REMOTE SENSING OF ENVIRONMENT 233(2019):19.