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TitleUsing SAR-derived vegetation descriptors in a water cloud model to improve soil moisture retrieval
AuthorLi, J; Wang, SORCID logo
SourceRemote Sensing vol. 10, no. 9, 1370, 2018 p. 1-17, Open Access logo Open Access
Alt SeriesNatural Resources Canada, Contribution Series 20170259
Mediapaper; on-line; digital
File formatpdf; html
ProgramGroundwater Geoscience Aquifer Assessment & support to mapping
Released2018 08 29
AbstractThe water cloud model (WCM) is a widely used radar backscatter model applied to SAR images to retrieve soil moisture over vegetated areas. The WCM needs vegetation descriptors to account for the impact of vegetation on SAR backscatter. The commonly used vegetation descriptors in WCM, such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI), are sometimes difficult to obtain due to the constraints in data availability in in-situ measurements or weather dependency in optical remote sensing. To improve soil moisture retrieval, this study investigates the feasibility of using all-weather SAR derived vegetation descriptors in WCM. The in-situ data observed at an agricultural crop region south of Winnipeg in Canada, RapidEye optical images and dual-polarized Radarsat-2 SAR images acquired in growing season were used for WCM model calibration and test. Vegetation descriptors studied include HV polarization backscattering coefficient (sigmaHV°) and Radar Vegetation Index (RVI) derived from SAR imagery, and NDVI derived from optical imagery. The results show that sigmaHV° achieved similar results as NDVI but slightly better than RVI, with a root mean square error of 0.069 m3/m3 and a correlation coefficient of 0.59 between the retrieved and observed soil moisture. The use of sigmaHV° can overcome the constraints of the commonly used vegetation descriptors and reduce additional data requirements (e.g., NDVI from optical sensors) in WCM, thus improving soil moisture retrieval and making WCM feasible for operational use.
Summary(Plain Language Summary, not published)
Continuous observations of soil moisture over large areas are important in many earth sciences applications. Current soil moisture products derived from passive radar data of SMOS/ SMAP satellites can provide global coverage with 2-3 days cycle but have coarse resolutions (~40km), which limits them in those applications. SAR is available at high resolution and is sensitive to soil moisture change. Therefore, the combination of SMOS/SMAP data with SAR data offers a way to downscale global soil moisture products to the higher resolution. This study explores the capability of time series of Radarsat2 SAR imagery for soil moisture retrieval over a vegetated area. The results indicate that the backscatter of Radarsat2 HV polarization instead of Normalized Difference Vegetation Index and Radar Vegetation Index for accounting vegetation's effect improves soil moisture retrieval. This study prepares the theoretical base for the downscaling of SMOS/SMAP soil moisture data using SAR data.

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