干旱区生态环境知识资源中心(Arid): An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

Arid

DOI	10.3390/rs11232736
	An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data
	Bai, Jueying 1; Cui, Qian 2; Zhang, Wen 1; Meng, Lingkui 1
通讯作者	Meng, Lingkui
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2019
卷号	11 期号:23
英文摘要	A method is proposed for the production of downscaled soil moisture active passive (SMAP) soil moisture (SM) data by combining optical/infrared data with synthetic aperture radar (SAR) data based on the random forest (RF) model. The method leverages the sensitivity of active microwaves to surface SM and the triangle/trapezium feature space among vegetation indexes (VIs), land surface temperature (LST), and SM. First, five RF architectures (RF1-RF5) were trained and tested at 9 km. Second, a comparison was performed for RF1-RF5, and were evaluated against in situ SM measurements. Third, two SMAP-Sentinel active-passive SM products were compared at 3 km and 1 km using in situ SM measurements. Fourth, the RF5 model simulations were compared with the SMAP L2_SM_SP product based on the optional algorithm at 3 km and 1 km resolutions. The results showed that the downscaled SM based on the synergistic use of optical/infrared data and the backscatter at vertical-vertical (VV) polarization was feasible in semi-arid areas with relatively low vegetation cover. The RF5 model with backscatter and more parameters from optical/infrared data performed best among the five RF models and was satisfactory at both 3 km and 1 km. Compared with L2_SM_SP, RF5 was more superior at 1 km. The input variables in decreasing order of importance were backscatter, LST, VIs, and topographic factors over the entire study area. The low vegetation cover conditions probably amplified the importance of the backscatter and LST. A sufficient number of VIs can enhance the adaptability of RF models to different vegetation conditions.
英文关键词	soil moisture downscaling random forest SMAP SAR data optical/infrared data
类型	Article
语种	英语
国家	Peoples R China
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000508382100017
WOS关键词	SYNTHETIC-APERTURE RADAR ; AMSR-E ; SPATIAL-RESOLUTION ; SMOS ; ALGORITHM ; VEGETATION ; PRODUCTS ; EVAPOTRANSPIRATION ; DISAGGREGATION ; INTEGRATION
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
EI主题词	2019-12-01
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/311244
作者单位	1.Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China; 2.Minist Water Resources, Hydrol Monitor & Forecast Ctr, Informat Ctr, Beijing 100053, Peoples R China
推荐引用方式 GB/T 7714	Bai, Jueying,Cui, Qian,Zhang, Wen,et al. An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data[J],2019,11(23).
APA	Bai, Jueying,Cui, Qian,Zhang, Wen,&Meng, Lingkui.(2019).An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data.REMOTE SENSING,11(23).
MLA	Bai, Jueying,et al."An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data".REMOTE SENSING 11.23(2019).