Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data

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 (R 2 = 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. of soil moisture data over a large region multiple times is very time-consuming and costly. Satellite remote sensing through optical or microwave sensors provides an effective and cost-efficient means of monitoring and assessing SSM at different scales [7-10]. For example, Sadeghi et al.[8] estimated SSM with a RMSE (root mean square error) less than 0.04 cm 3 /cm −3 with local calibration from Sentinel-2 and Landsat-8 observations for the Walnut Gulch and Little Washita watersheds. Tomer et al. [11] used the RADARSAT-2 images to calculate relative soil moisture. When comparing with the SMOS soil moisture, the results showed good temporal behavior with RMSE of approximately 0.05 m 3 /m 3 and a correlation coefficient of approximately 0.9. For passive microwave, the SMAP-radiometer-based soil moisture data product meets its expected performance of 0.04 m 3 /m 3 volumetric soil moisture (unbiased RMSE) [10]. Optical remote sensing is often hampered by cloud cover, while microwave remote sensing has all-weather day-and-night penetration capabilities [11]. However, the lower spatial resolutions of the passive microwave sensors in orbit limit their applicability to retrieve SSM over individual agricultural fields [12]. Currently, 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].There is a high correlation between radar backscattering coefficient and SSM since the dielectric constant of soil is directly proportional to the amount of water held in the soil [13]. Therefore, the...

Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in Farmland

“…Moreover, VV polarization is more suitable for monitoring the SM of wheat crops, while VH polarization is more suitable for canola. This is consistent with previous studies of Mercier et al [126], El et al [127], Xing et al [128], and Kumar et al [129]. El et al 2019showed that for C-band SAR when NDVI was between 0.4 and 0.7, the correlation between VH polarization and soil moisture in vegetation-covered areas was slightly higher than that between VV polarization [127].…”

“…El et al 2019showed that for C-band SAR when NDVI was between 0.4 and 0.7, the correlation between VH polarization and soil moisture in vegetation-covered areas was slightly higher than that between VV polarization [127]. The disadvantage of using WCM and SAR to invert SM in vegetation-covered areas is that the WCM model ignores the second-order contribution of multiple scattering between soil and vegetation, it may not be suitable for estimating SM of tall crops [27], [124], [128]. Besides, LAI obtained by SNAP is underestimated compared with measured LAI data, which may further affect the estimation accuracy of SM.…”

Retrieving Soil Moisture Over Soybean Fields During Growing Season Through Polarimetric Decomposition

“…OIL MOISTURE (Mv) is a crucial factor in many applications such as agriculture, environment, hydrology, ecology and water management [1][2][3][4][5][6]. For example, soil moisture greatly governs crop growth and nutrient uptake, which in turn affects the final crop yield [7,8]. Traditionally, the soil moisture is measured by in-situ field campaign, which is very timeconsuming and difficult to carry on over a large area.…”

Editorial for the Special Issue “Soil Moisture Retrieval using Radar Remote Sensing Sensors”

“…Several authors [2][3][4][5][6] are developing techniques that allow SAR data to be applied to the estimation of soil moisture. They have also proposed various applications that can be used to interpret soil moisture patterns (for example irrigation mapping).…”

“…This approach relies on the inversion of the semi-empirical Water Cloud Model (WCM), and combines radar data with multispectral (visible and near infrared) Sentinel-2 data, in order to characterize the radar scattering properties of the vegetation cover. A somewhat similar approach was proposed by [4], in which the modified Water Cloud model (MWCM) is used to analyze soils planted with wheat and soybean crops, thus allowing RADARSAT-2 data to be inverted and the SSM to be estimated. These various approaches to the remote measurement of soil moisture have accuracies better than 6.5 vol.%.…”