干旱区生态环境知识资源中心(Arid): Inter-comparison and integration of different soil moisture downscaling methods over the Qinghai-Tibet Plateau

Arid

DOI	10.1016/j.jhydrol.2022.129014
	Inter-comparison and integration of different soil moisture downscaling methods over the Qinghai-Tibet Plateau
	Shangguan, Yulin; Min, Xiaoxiao; Shi, Zhou
通讯作者	Shi, Z
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2023
卷号	617
英文摘要	Soil moisture (SM) is a key state variable in the water, energy cycle between atmosphere and land surface but existing passive microwave soil moisture products typically have spatial resolutions of tens of kilometers, which fails to meet the requirements of regional applications. Even though numerous machine/deep learning methods have been applied to downscale SM, few studies have investigated the performance differences of diverse approaches and attempted to integrate various methods to improve downscaling accuracy. Therefore, this study firstly evaluated and inter-compared the downscaling performances of six machine/deep learning approaches and further proposed a hybrid downscaling method based on Bayesian three-cornered hat merging (MATCH). The daily 1 km seamless soil moisture product during 2015-2019 over the Qinghai-Tibet Plateau was then obtained. Evaluation and inter-comparison results revealed that there was obvious performance discrepancy among different downscaling approaches and the gradient boosting decision tree (GBDT) and random forest (RF) were the best two methods, which performed best in the southern and eastern of the plateau, respectively. While the artificial neural network (ANN) outperformed other approaches in the northwestern areas. Validation against in-situ measurements showed that compared with SMAP SM, the MATCH SM exhibited comparable accuracy and lower error with mean R and ubRMSE values of 0.55 and 0.047 m3/m3. The mean R and ubRMSE values for SMAP SM were 0.67 and 0.056 m3/m3, respectively. In addition, the MATCH SM presented great improvement compared with any single downscaled SM data, having the highest correlation and the lowest ubRMSE scores. While, among different methods, the highest R was 0.50 (GBDT) and the lowest ubRMSE was 0.052 m3/m3 (residual network (ResNet)). Besides, the downscaled SM could accurately reflect the temporal variations of soil moisture and precipitation, and effectively represent the spatial patterns of soil moisture. Satisfactory downscaling results were achieved in arid and semi-arid areas whereas a certain degree of overestimation still existed in the eastern and southeastern regions with dense vegetation and high moisture conditions. Such overestimation was inherent from original SMAP SM but was mitigated after downscaling. In conclusion, performance differences existed among diverse downscaling approaches and the developed MATCH method could maximize the potentials of each method and show encouraging downscaling performances.
英文关键词	Soil moisture Downscaling Machine learning Integration Inter-comparison The Qinghai-Tibet Plateau
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000914075300001
WOS关键词	LAND-SURFACE TEMPERATURE ; AMSR-E ; SMAP ; PRODUCTS ; SMOS ; RETRIEVALS ; SCALE ; REFLECTANCE ; PERFORMANCE ; VALIDATION
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397383
推荐引用方式 GB/T 7714	Shangguan, Yulin,Min, Xiaoxiao,Shi, Zhou. Inter-comparison and integration of different soil moisture downscaling methods over the Qinghai-Tibet Plateau[J],2023,617.
APA	Shangguan, Yulin,Min, Xiaoxiao,&Shi, Zhou.(2023).Inter-comparison and integration of different soil moisture downscaling methods over the Qinghai-Tibet Plateau.JOURNAL OF HYDROLOGY,617.
MLA	Shangguan, Yulin,et al."Inter-comparison and integration of different soil moisture downscaling methods over the Qinghai-Tibet Plateau".JOURNAL OF HYDROLOGY 617(2023).