干旱区生态环境知识资源中心(Arid): Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands

Arid

DOI	10.3390/rs15153789
	Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands
	Zhu, Yi; Zhang, Lanhui; Li, Feng; Xu, Jiaxin; He, Chansheng
通讯作者	Zhang, LH
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:15
英文摘要	In arid and semi-arid areas, soil moisture (SM) plays a crucial role in land-atmosphere interactions, hydrological processes, and ecosystem sustainability. SM data at large scales are critical for related climatic, hydrological, and ecohydrological research. Data fusion based on satellite products and model simulations is an important way to obtain SM data at large scales; however, little has been reported on the comparison of the data fusion methods in different categories. Here, we compared the performance of two widely used data fusion methods, the Ensemble Kalman Filter (EnKF) and the Back-Propagation Artificial Neural Network (BPANN), in the degraded grassland site (DGS) and the alpine grassland site (AGS). The SM data from the Community Land Model 5.0 (CLM5.0) and the Soil Moisture Active and Passive (SMAP) were fused and validated against the observations of the Cosmic-Ray Neutron Sensor (CRNS) to avoid the impacts of scale-mismatch. Results show that compared with the original data sets at both sites, the RMSE of the fused data by BPANN (FD-BPANN) and EnKF (FD-EnKF) had improved by more than 50% and 31%, respectively. Overall, the FD-BPANN performs better than the FD-EnKF because the BPANN method assigned higher weights to input data with better performance and the EnKF method is affected by the strong variabilities of both the fused CLM5.0 and SMAP data and the CRNS data. However, in terms of the percentile range, the FD-BPANN showed the worst performance, with overestimations in the low SM range of 25th percentile (
英文关键词	soil moisture data fusion Back-Propagation Artificial Neural Network (BPANN) Ensemble Kalman Filter (EnKF) semi-arid grasslands Soil Moisture Active and Passive (SMAP) Community Land Model 5 0 (CLM5 0) Cosmic-Ray Neutron Sensor (CRNS)
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:001046336800001
WOS关键词	ENSEMBLE KALMAN FILTER ; ARTIFICIAL NEURAL-NETWORK ; DIFFERENT LAND COVERS ; DATA ASSIMILATION ; IN-SITU ; MOUNTAINOUS AREA ; AMSR-E ; NORTHWEST ; COMMUNITY ; SMOS
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/2XILL650/398304
推荐引用方式 GB/T 7714	Zhu, Yi,Zhang, Lanhui,Li, Feng,et al. Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands[J],2023,15(15).
APA	Zhu, Yi,Zhang, Lanhui,Li, Feng,Xu, Jiaxin,&He, Chansheng.(2023).Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands.REMOTE SENSING,15(15).
MLA	Zhu, Yi,et al."Comparison of Data Fusion Methods in Fusing Satellite Products and Model Simulations for Estimating Soil Moisture on Semi-Arid Grasslands".REMOTE SENSING 15.15(2023).