干旱区生态环境知识资源中心(Arid): Daily soil moisture mapping at 1 km resolution based on SIVIAP data for desertification areas in northern China

Arid

DOI	10.5194/essd-14-3053-2022
	Daily soil moisture mapping at 1 km resolution based on SIVIAP data for desertification areas in northern China
	Rao, Pinzeng; Wang, Yicheng; Wang, Fang; Liu, Yang; Wang, Xiaoya; Wang, Zhu
通讯作者	Wang, F
来源期刊	EARTH SYSTEM SCIENCE DATA
ISSN	1866-3508
EISSN	1866-3516
出版年	2022
卷号	14 期号:7 页码:3053-3073
英文摘要	Land surface soil moisture (SM) plays a critical role in hydrological processes and terrestrial ecosystems in desertification areas. Passive microwave remote-sensing products such as the Soil Moisture Active Passive (SMAP) satellite have been shown to monitor surface soil water well. However, the coarse spatial resolution and lack of full coverage of these products greatly limit their application in areas undergoing desertification. In order to overcome these limitations, a combination of multiple machine learning methods, including multiple linear regression (MLR), support vector regression (SVR), artificial neural networks (ANNs), random forest (RF) and extreme gradient boosting (XGB), have been applied to downscale the 36 km SMAP SM products and produce higher-spatial-resolution SM data based on related surface variables, such as vegetation index and surface temperature. Desertification areas in northern China, which are sensitive to SM, were selected as the study area, and the downscaled SM with a resolution of 1 km on a daily scale from 2015 to 2020 was produced. The results showed a good performance compared with in situ observed SM data, with an average unbiased root mean square error value of 0.057 m(3) m(-3). In addition, their time series were consistent with precipitation and performed better than common gridded SM products. The data can be used to assess soil drought and provide a reference for reversing desertification in the study area. This dataset is freely available at https://doi.org/10.6084/m9.figshare.16430478.v6 (Rao et al., 2022).
类型	Article
语种	英语
开放获取类型	gold, Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000821143300001
WOS关键词	NEURAL-NETWORKS ; PRODUCT
WOS类目	Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS研究方向	Geology ; Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392270
推荐引用方式 GB/T 7714	Rao, Pinzeng,Wang, Yicheng,Wang, Fang,et al. Daily soil moisture mapping at 1 km resolution based on SIVIAP data for desertification areas in northern China[J],2022,14(7):3053-3073.
APA	Rao, Pinzeng,Wang, Yicheng,Wang, Fang,Liu, Yang,Wang, Xiaoya,&Wang, Zhu.(2022).Daily soil moisture mapping at 1 km resolution based on SIVIAP data for desertification areas in northern China.EARTH SYSTEM SCIENCE DATA,14(7),3053-3073.
MLA	Rao, Pinzeng,et al."Daily soil moisture mapping at 1 km resolution based on SIVIAP data for desertification areas in northern China".EARTH SYSTEM SCIENCE DATA 14.7(2022):3053-3073.