干旱区生态环境知识资源中心(Arid): Estimation of Root-Zone Soil Moisture in Semi-Arid Areas Based on Remotely Sensed Data

Arid

DOI	10.3390/rs15082003
	Estimation of Root-Zone Soil Moisture in Semi-Arid Areas Based on Remotely Sensed Data
	Guo, Xiaomeng; Fang, Xiuqin; Zhu, Qiuan; Jiang, Shanhu; Tian, Jia; Tian, Qingjiu; Jin, Jiaxin
通讯作者	Fang, XQ
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:8
英文摘要	Soil moisture (SM) is a bridge between the atmosphere, vegetation and soil, and its dynamics reflect the energy exchange and transformation between the three. Among SM at different soil profiles, root zone soil moisture (RZSM) plays a significant role in vegetation growth. Therefore, reliable estimation of RZSM at the regional scale is of great importance for drought warning, agricultural yield estimation, forest fire monitoring, etc. Many satellite products provide surface soil moisture (SSM) at the thin top layer of the soil, approximately 2 cm from the surface. However, the acquisition of RZSM at the regional scale is still a tough issue to solve, especially in the semi-arid areas with a lack of in situ observations. Linking the dynamics of SSM and RZSM is promising to solve this issue. The soil moisture analytical relationship (SMAR) model can relate RZSM to SSM based on a simplified soil water balance equation, which is suitable for the simulation of soil moisture mechanisms in semi-arid areas. In this study, the Xiliaohe River Basin is the study area. The SMAR model at the pixels where in situ sites were located is established, and parameters (a, b, s(w2), s(c1)) at these pixels are calibrated by a genetic algorithm (GA). Then the spatial parameters are estimated by the random forest (RF) regression method with the soil, meteorological and vegetation characteristics of the study area as explanatory variables. In addition, the importance of soil, climatic and vegetation characteristics for predicting SMAR parameters is analyzed. Finally, the spatial RZSM in the Xiliaohe River Basin is estimated by the SMAR model at the regional scale with the predicted spatial parameters, and the variation of the regional SMAR model performance is discussed. A comparison of estimated RZSM and in-situ RZSM showed that the SMAR model at the point and regional scales can both meet the RMSE benchmark from NASA of 0.06 cm(3)center dot cm(-3), indicating that the method this study proposed could effectively estimate RZSM in semi-arid areas based on remotely sensed SSM data.
英文关键词	surface soil moisture root-zone soil moisture remote sensing SMAR random forest
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:000977322400001
WOS关键词	WATER ; SCALE
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/2XILL650/398259
推荐引用方式 GB/T 7714	Guo, Xiaomeng,Fang, Xiuqin,Zhu, Qiuan,et al. Estimation of Root-Zone Soil Moisture in Semi-Arid Areas Based on Remotely Sensed Data[J],2023,15(8).
APA	Guo, Xiaomeng.,Fang, Xiuqin.,Zhu, Qiuan.,Jiang, Shanhu.,Tian, Jia.,...&Jin, Jiaxin.(2023).Estimation of Root-Zone Soil Moisture in Semi-Arid Areas Based on Remotely Sensed Data.REMOTE SENSING,15(8).
MLA	Guo, Xiaomeng,et al."Estimation of Root-Zone Soil Moisture in Semi-Arid Areas Based on Remotely Sensed Data".REMOTE SENSING 15.8(2023).