Soil Moisture Retrieval in Bare Agricultural Areas Using Sentinel-1 Images

Abstract

Soil moisture maps are essential for hydrological, agricultural and risk assessment applications. To best meet these requirements, it is essential to develop soil moisture products at high spatial resolution, which is now made possible using the free Sentinel-1 (S1) SAR (Synthetic Aperture Radar) data. Some soil moisture retrieval techniques using S1 data relied on the use of a priori weather information in order to increase the precision of soil moisture estimates, which required access to a weather-forecasting framework. This paper presents an improved and fully autonomous solution for high-resolution soil moisture mapping in bare agricultural areas. The proposed solution derives a priori weather information directly from the original Sentinel images, thus bypassing the need for a weather forecasting framework. For soil moisture estimation, the neural network technique was implemented to ensure the optimum integration of radar information. The neural networks were trained using synthetic data generated by the modified Integral Equation Model (IEM) model and validated on real data from two study sites in France and Tunisia. The main findings showed that the use of a radar signal averaged over grids of a few km2 in addition to radar signal at plot scale instead of a priori weather information provides good soil moisture estimations. The accuracy is even slightly better compared to the accuracy obtained using a priori weather information.

Sentinel Data and Machine Learning Algorithms for Soil Moisture Land Classification

“…Forum 2023, 27, x 2 of 7 alterations alongside socio-economic and environmental factors [3], and (iv) establishing correlations between radar backscatter and SM [4]. More recently, researchers in [5] harnessed neural networks to estimate SM in exclusively unvegetated soils across two research sites in France and Tunisia. In this instance, the neural networks were trained using synthetic data generated through the modified integral equation model (IEM) and validated solely with real unvegetated soil data.…”

“…Prior investigations have demonstrated the potential of combining radar and optical data to achieve several objectives: (i) recovering SM levels in cereal fields [1], (ii) mapping irrigated and rainfed areas [2], (iii) scrutinizing landscape alterations alongside socio-economic and environmental factors [3], and (iv) establishing correlations between radar backscatter and SM [4]. More recently, researchers in [5] harnessed neural networks to estimate SM in exclusively unvegetated soils across two research sites in France and Tunisia. In this instance, the neural networks were trained using synthetic data generated through the modified integral equation model (IEM) and validated solely with real unvegetated soil data.…”