气候变化领域集成服务门户(CCPortal): Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data

CCPortal

DOI	10.3390/rs12142303
	Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data
	Ma, Chunfeng; Li, Xin; McCabe, Matthew F.
通讯作者	Ma, CF (通讯作者)
发表日期	2020
EISSN	2072-4292
卷号	12 期号:14
英文摘要	Estimating soil moisture based on synthetic aperture radar (SAR) data remains challenging due to the influences of vegetation and surface roughness. Here we present an algorithm that simultaneously retrieves soil moisture, surface roughness and vegetation water content by jointly using high-resolution Sentinel-1 SAR and Sentinel-2 multispectral imagery, with an application directed towards the provision of information at the precision agricultural scale. Sentinel-2-derived vegetation water indices are investigated and used to quantify the backscatter resulting from the vegetation canopy. The proposed algorithm then inverts the water cloud model to simultaneously estimate soil moisture and surface roughness by minimizing a cost function constructed by model simulations and SAR observations. To examine the performance of VV- and VH-polarized backscatters on soil moisture retrievals, three retrieval schemes are explored: a single channel algorithm using VV (SCA-VV) and VH (SCA-VH) polarizations and a dual channel algorithm using both VV and VH polarizations (DCA-VVVH). An evaluation of the approach using a combination of a cosmic-ray soil moisture observing system (COSMOS) and Soil Climate Analysis Network measurements over Nebraska shows that the SCA-VV scheme yields good agreement at both the COSMOS footprint and single-site scales. The features of the algorithms that have the most impact on the retrieval accuracy include the vegetation water content estimation scheme, parameters of the water cloud model and the specification of initial ranges of soil moisture and roughness, all of which are comprehensively analyzed and discussed. Through careful consideration and selection of these factors, we demonstrate that the proposed SCA-VV approach can provide reasonable soil moisture retrievals, with RMSE ranging from 0.039 to 0.078 m(3)/m(3)and R(2)ranging from 0.472 to 0.665, highlighting the utility of SAR for application at the precision agricultural scale.
关键词	VEGETATION WATER-CONTENT SYNTHETIC-APERTURE RADAR GLOBAL OPTIMIZATION SURFACE-ROUGHNESS SAR BACKSCATTERING ALGORITHM MODEL VALIDATION EVOLUTION
英文关键词	synthetic aperture radar; precision agriculture; microwave remote sensing; soil moisture
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000554242300001
来源期刊	REMOTE SENSING
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259683
推荐引用方式 GB/T 7714	Ma, Chunfeng,Li, Xin,McCabe, Matthew F.. Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data[J]. 中国科学院青藏高原研究所,2020,12(14).
APA	Ma, Chunfeng,Li, Xin,&McCabe, Matthew F..(2020).Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data.REMOTE SENSING,12(14).
MLA	Ma, Chunfeng,et al."Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data".REMOTE SENSING 12.14(2020).