干旱区生态环境知识资源中心(Arid): Downscaling SMAP soil moisture product in cold and arid region: Incorporating NDSI and BSI into the random forest algorithm

Arid

DOI	10.1002/vzj2.20323
	Downscaling SMAP soil moisture product in cold and arid region: Incorporating NDSI and BSI into the random forest algorithm
	Gao, Mingxing; Zhu, Kui; Guo, Yanjun; Han, Xuhang; Li, Dongsheng; Zhang, Shujian
通讯作者	Zhu, K
来源期刊	VADOSE ZONE JOURNAL
EISSN	1539-1663
出版年	2024
卷号	23 期号:3
英文摘要	Soil moisture (SM) is a critical element of the hydrological cycle, land surface processes, and surface energy balance. However, the low spatial resolution of commonly used SM products limits the application of SM in agriculture and eco-hydrology in cold and arid regions. In this study, the normalized difference soil index (NDSI) and bare soil index (BSI) were added to traditional downscaling factors including land surface temperature, normalized difference vegetation index, digital elevation mode, apparent thermal inertia, Albedo, and temperature vegetation dryness index, as they are more strongly correlated with surface SM in the bare soil-vegetation alternation zone of such region. Using the random forest algorithm, a downscaling model of SM was constructed for such region. The accuracy of the downscaled SM estimates was validated by comparing them with the original SM data collected from May to September 2021, which is the non-freezing period of the soil. The findings indicate that the newly added NDSI and BSI have good correlation with SM. Incorporating NDSI and BSI to construct the downscaled model enhances the accuracy by over 19% compared to excluding them, while also providing a more comprehensive representation of SM information. NDSI and BSI can be well applied to the downscaled research of SM in the bare soil-vegetation alternation zone, which is of great value for the study of eco-hydrology and agricultural drought monitoring in cold and arid regions. Adding soil factors improves the downscaling of soil moisture in cold and arid regions. Compared with the traditional model, the improved model showed a significant increase in accuracy. This improved model better characterized soil moisture spatial and temporal variability.
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:001187790700001
WOS关键词	VEGETATION ; INDEX ; TEMPERATURE ; VARIABILITY ; SIGNATURE ; URBAN
WOS类目	Environmental Sciences ; Soil Science ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405832
推荐引用方式 GB/T 7714	Gao, Mingxing,Zhu, Kui,Guo, Yanjun,et al. Downscaling SMAP soil moisture product in cold and arid region: Incorporating NDSI and BSI into the random forest algorithm[J],2024,23(3).
APA	Gao, Mingxing,Zhu, Kui,Guo, Yanjun,Han, Xuhang,Li, Dongsheng,&Zhang, Shujian.(2024).Downscaling SMAP soil moisture product in cold and arid region: Incorporating NDSI and BSI into the random forest algorithm.VADOSE ZONE JOURNAL,23(3).
MLA	Gao, Mingxing,et al."Downscaling SMAP soil moisture product in cold and arid region: Incorporating NDSI and BSI into the random forest algorithm".VADOSE ZONE JOURNAL 23.3(2024).