Soil Moisture Estimates in a Grass Field Using Sentinel-1 Radar Data and an Assimilation Approach

Abstract

The new constellation of synthetic aperture radar (SAR) satellite, Sentinel-1, provides images at a high spatial resolution (up to 10 m) typical of radar sensors, but also at high time resolutions (6–12 revisit days), representing a major advance for the development of operational soil moisture mapping at a plot scale. Our objective was to develop and test an operational approach to assimilate Sentinel 1 observations in a land surface model, and to demonstrate the potential of the use of the new satellite sensors in soil moisture predictions in a grass field. However, for soil moisture retrievals from Sentinel 1 observations in grasslands, there is still the need to identify robust and parsimonious solutions, accounting for the effects of vegetation attenuation and their seasonal variability. In a grass experimental site in Sardinia, where field measurements of soil moisture were available for the 2016–2018 period, three common retrieval methods have been compared to estimate soil moisture from Sentinel 1 data, with increasing complexity and physical interpretation of the processes: the empirical change detection method, the semi-empirical Dubois model, and the physically-based Fung model. In operational approaches for soil moisture mapping from remote sensing, the parameterization simplification of soil moisture retrieval techniques is encouraged, looking for parameter estimates without a priori information. We have proposed a simplified approach for estimating a key parameter of retrieval methods, the surface roughness, from the normalized difference vegetation index (NDVI) derived by simultaneous Sentinel 2 optical observations. Soil moisture was estimated better using the proposed approach and the Dubois model than by using the other methods, which accounted vegetation effects through the common water cloud model. Furthermore, we successfully merged radar-based soil moisture observations and a land surface model, through a data assimilation approach based on the Ensemble Kalman filter, providing robust predictions of soil moisture.

Multiscale Assimilation of Sentinel and Landsat Data for Soil Moisture and Leaf Area Index Predictions Using an Ensemble-Kalman-Filter-Based Assimilation Approach in a Heterogeneous Ecosystem

“…The Sentinel 1 radar data originated from the S1A and S1B satellites, and the level-1 ground-range-detected (GRD) data were used. The images were calibrated, noise corrected with a Lee filter (7 × 7), and resampled from 10 to 30 m spatial resolution [33]. S1A images were available from January 2015.…”

“…The dielectric constant of the surface soil, which is related to soil moisture, can be detected from radar data. We used the Sentinel 1 radar in VV polarization because VV polarization is more sensitive to soil moisture [28,62] and less sensitive to vegetation compared with VH polarization [33,63]. The images were calibrated, noise-corrected, and resampled at a 30 m spatial resolution.…”

“…While the radar configuration parameters are known, σ is undetermined. We used the approach of Montaldo et al [33], which relates σ with NDVI through an empirical σ (NDVI) relationship, for accounting for the grass growth effect on radar signal, and which was just estimated at the Orroli field site (Figure 4b of Montaldo et al [33]):…”

Soil Moisture Estimation for Winter-Wheat Waterlogging Monitoring by Assimilating Remote Sensing Inversion Data into the Distributed Hydrology Soil Vegetation Model

“…Chatterjee et al [12] established an empirical model for surface soil moisture using Sentinel-1 data and ancillary data. Montaldo et al [13] offered a simplified approach for estimating the surface roughness by Sentinel-2 optical observations to assimilate Sentinel-1 observations in a land surface model. The accuracy of remote sensing technology is relatively high for a large area.…”

Retrieving the Soil Moisture in Bare Farmland Areas Using a Modified Dubois Model

“…There are many models that describe the relationship between the backscattering coefficients and surface parameters. These models can be divided into three categories: theoretical models (Ulaby et al, 1990;Fung et al, 1992;Wu et al, 2001;Su and Cao, 2021), semi-empirical models (Oh et al, 2002;Oh, 2004;Yang et al, 2021;Chen et al, 2003) and empirical models (Shi et al, 1997;Baghdadi et al, 2006;Montaldo et al, 2021). Among above models, semi-empirical models are more widely used than other models.…”