干旱区生态环境知识资源中心(Arid): Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and Mapping

Arid

DOI	10.3390/rs14143373
	Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and Mapping
	Zhao, Zebin; Jin, Rui; Kang, Jian; Ma, Chunfeng; Wang, Weizhen
通讯作者	Jin, R
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:14
英文摘要	Soil moisture is one of the core hydrological and climate variables that crucially influences water and energy budgets. The spatial resolution of available soil moisture products is generally coarser than 25 km, which limits their hydro-meteorological and eco-hydrological applications and the management of water resources at watershed and agricultural scales. A feasible solution to overcome these limitations is to downscale coarse soil moisture products with the support of higher-resolution spatial information. Although many auxiliary variables have been used for this purpose, few studies have analyzed their applicability and effectiveness in arid regions. To this end, we comprehensively evaluated four commonly used auxiliary variables, including NDVI (Normalized Difference Vegetation Index), LST (Land Surface Temperature), TVDI (Temperature Vegetation Dryness Index), and SEE (Soil Evaporative Efficiency), against ground-based soil moisture observations during the vegetation growing season in the Heihe River Basin, China. Performance metrics indicated that SEE is most sensitive (R-2 >= 0.67) to soil moisture because it is controlled by soil evaporation limited by the available soil moisture. The similarity of spatial patterns also showed that SEE best captures soil moisture changes, with the STD (standard deviation) of the HD (Hausdorff Distance) less than 0.058 when compared with PLMR (Polarimetric L-band Multi-beam Radiometer) soil moisture products. In addition, soil moisture was mapped by RF (Random Forests) using both single auxiliary variables and 11 types of multiple auxiliary variable combinations. SEE was found to be the best auxiliary variable for scaling and mapping soil moisture with accuracy of 0.035 cm(3)/cm(3). Among the multiple auxiliary variables, the combination of LST, NDVI, and SEE was found to best enhance the scaling and mapping accuracy of soil moisture with 0.034 cm(3)/cm(3).
英文关键词	soil moisture auxiliary variable Hausdorff Distance Random Forests scaling mapping
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000832194300001
WOS关键词	WIRELESS SENSOR NETWORK ; HEIHE RIVER-BASIN ; SURFACE-TEMPERATURE ; VEGETATION TEMPERATURES ; CONDITION INDEX ; HIGH-RESOLUTION ; RANDOM FORESTS ; LAND-COVER ; AMSR-E ; MODIS
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394168
推荐引用方式 GB/T 7714	Zhao, Zebin,Jin, Rui,Kang, Jian,et al. Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and Mapping[J],2022,14(14).
APA	Zhao, Zebin,Jin, Rui,Kang, Jian,Ma, Chunfeng,&Wang, Weizhen.(2022).Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and Mapping.REMOTE SENSING,14(14).
MLA	Zhao, Zebin,et al."Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and Mapping".REMOTE SENSING 14.14(2022).