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TitleRetrieving soil moisture in a vegetated area using water-cloud model and time series dual-polarized Radarsat-2 imagery
AuthorLi, J; Wang, S
SourceRemote Sensing of Environment .
Alt SeriesNatural Resources Canada, Contribution Series 20170259
ProgramGroundwater Geoscience, Aquifer Assessment & support to mapping
AbstractThis study examines the potential of using time series C-band dual-polarized SAR imagery and a modified Water-Cloud Model (WCM) for soil moisture retrieval in a vegetated area. The parameters HV polarization backscattering coefficient (s°HV) and Radar Vegetation Index (RVI) derived from SAR imagery are tested in the WCM for characterizing vegetation and compared to the Normalized Difference Vegetation Index (NDVI) derived from optical imagery. The time series of SAR imagery was used to eliminate the dependence of backscattering on the soil surface roughness. The study is carried out at the SMAPEX12 experiment site, an agricultural region south of Winnipeg in Manitoba, Canada, using dual-polarized Radarsat-2 data acquired in early June to middle July of 2012. The results indicate that the accuracy of retrieved soil moisture depends on the saturation level of the vegetation descriptor in the WCM. The relationships between the three vegetation descriptors and in-situ LAI show that NDVI and RVI saturate at LAI of around 2.0, and s°HV saturates at higher LAI with specific value depending on crops. This study demonstrates that s°HV can be used as the vegetation descriptor in the WCM to improve soil moisture retrieval in vegetated areas. With the Radarsat Constellation Mission (RCM) data becoming available in 2018 which will provide 4 days revisit time, this study presents a potential for using RCM data in continuously mapping soil moisture over large areas under all weather conditions.
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.