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

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

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) da...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 15; no. 14; p. 3502
Main Authors: Ettalbi, Mouad, Baghdadi, Nicolas, Garambois, Pierre-André, Bazzi, Hassan, Ferreira, Emmanuel, Zribi, Mehrez
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-07-2023
Subjects:
Accuracy
Agricultural land
Artificial intelligence
bare agricultural areas
Datasets
Environmental aspects
High resolution
Hydrologic cycle
Hydrology
Integral equations
Mathematical models
Measurement
Meteorological data
Moisture effects
Neural networks
Radar
Remote sensing
Retrieval
Risk assessment
satellite remote sensing
Sensors
Sentinel-1
Soil moisture
Spatial discrimination
Spatial resolution
Synthetic aperture radar
Synthetic data
Testing
Weather
Weather forecasting
France
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15143502