Abstract
Surface soil moisture (SSM) retrieval over agricultural fields using synthetic aperture radar (SAR) data is often obstructed by the vegetation effects on the backscattering during the growing season. This paper reports the retrieval of SSM from RADARSAT-2 SAR data that were acquired over wheat and soybean fields throughout the 2015 (April to October) growing season. The developed SSM retrieval algorithm includes a vegetation-effect correction. A method that can adequately represent the scattering behavior of vegetation-covered area was developed by defining the backscattering from vegetation and the underlying soil individually to remove the effect of vegetation on the total SAR backscattering. The Dubois model was employed to describe the backscattering from the underlying soil. A modified Water Cloud Model (MWCM) was used to remove the effect of backscattering that is caused by vegetation canopy. SSM was derived from an inversion scheme while using the dual co-polarizations (HH and VV) from the quad polarization RADARSAT-2 SAR data. Validation against ground measurements showed a high correlation between the measured and estimated SSM (R2 = 0.71, RMSE = 4.43 vol.%, p < 0.01), which suggested an operational potential of RADARSAT-2 SAR data on SSM estimation over wheat and soybean fields during the growing season.
Highlights
Surface soil moisture (SSM) is a key state variable that influences various hydrological, meteorological, agricultural, and risk assessment applications [1,2]
The proposed algorithm was developed while using data that were collected over wheat and soybean fields in southwest Ontario, Canada, based on the modified Water Cloud Model (MWCM) and Dubois models
The inclusion of vegetation cover fractions satisfied the conditions for the Water Cloud Model (WCM) in cases of sparse vegetation cover and improved the SSM estimation because it separated backscattering from soil and vegetation
Summary
Surface soil moisture (SSM) is a key state variable that influences various hydrological, meteorological, agricultural, and risk assessment applications [1,2]. It is an important component controlling the partitioning between infiltration and surface runoff [3], which is the main driver behind the flooding and droughts processes [4]. The lower spatial resolutions of the passive microwave sensors in orbit limit their applicability to retrieve SSM over individual agricultural fields [12]. Only active sensors, like radars, especially Synthetic Aperture Radars (SARs) (such as C-band Sentinel-1A and 1B, RADARSAT-2, and L-band ALOS-2), can provide observations at high spatial resolutions of about 10 m to 100 m with a relatively coarse temporal resolution (2–4 weeks) [2,12]
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