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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/14/3502 |
| _version_ | 1827731905669234688 |
|---|---|
| author | Mouad Ettalbi Nicolas Baghdadi Pierre-André Garambois Hassan Bazzi Emmanuel Ferreira Mehrez Zribi |
| author_facet | Mouad Ettalbi Nicolas Baghdadi Pierre-André Garambois Hassan Bazzi Emmanuel Ferreira Mehrez Zribi |
| author_sort | Mouad Ettalbi |
| collection | DOAJ |
| description | 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 km<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula> 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. |
| first_indexed | 2024-03-11T00:42:19Z |
| format | Article |
| id | doaj.art-b7bcd8c074f94a71bc607f37110d56cc |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T00:42:19Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b7bcd8c074f94a71bc607f37110d56cc2023-11-18T21:11:41ZengMDPI AGRemote Sensing2072-42922023-07-011514350210.3390/rs15143502Soil Moisture Retrieval in Bare Agricultural Areas Using Sentinel-1 ImagesMouad Ettalbi0Nicolas Baghdadi1Pierre-André Garambois2Hassan Bazzi3Emmanuel Ferreira4Mehrez Zribi5INRAE, UMR TETIS, Université de Montpellier, 34090 Montpellier, FranceINRAE, UMR TETIS, Université de Montpellier, 34090 Montpellier, FranceINRAE, UMR RECOVER, Aix-Marseille Université, 13182 Aix-en-Provence, CEDEX 5, FranceUniversité Paris-Saclay, AgroParisTech, INRAE, UMR 518 MIA Paris-Saclay, 91120 Palaiseau, FranceAIWAY, Mercure A, 565 rue Marcellin Berthelot, 13851 Aix-en-Provence, CEDEX 3, FranceCESBIO (CNES/CNRS/INRAE/IRD/UPS), 18 Av. Edouard Belin, bpi 2801, 31401 Toulouse, CEDEX 9, FranceSoil 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 km<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula> 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.https://www.mdpi.com/2072-4292/15/14/3502soil moisturebare agricultural areasneural networkssatellite remote sensingSentinel-1 |
| spellingShingle | Mouad Ettalbi Nicolas Baghdadi Pierre-André Garambois Hassan Bazzi Emmanuel Ferreira Mehrez Zribi Soil Moisture Retrieval in Bare Agricultural Areas Using Sentinel-1 Images Remote Sensing soil moisture bare agricultural areas neural networks satellite remote sensing Sentinel-1 |
| title | Soil Moisture Retrieval in Bare Agricultural Areas Using Sentinel-1 Images |
| title_full | Soil Moisture Retrieval in Bare Agricultural Areas Using Sentinel-1 Images |
| title_fullStr | Soil Moisture Retrieval in Bare Agricultural Areas Using Sentinel-1 Images |
| title_full_unstemmed | Soil Moisture Retrieval in Bare Agricultural Areas Using Sentinel-1 Images |
| title_short | Soil Moisture Retrieval in Bare Agricultural Areas Using Sentinel-1 Images |
| title_sort | soil moisture retrieval in bare agricultural areas using sentinel 1 images |
| topic | soil moisture bare agricultural areas neural networks satellite remote sensing Sentinel-1 |
| url | https://www.mdpi.com/2072-4292/15/14/3502 |
| work_keys_str_mv | AT mouadettalbi soilmoistureretrievalinbareagriculturalareasusingsentinel1images AT nicolasbaghdadi soilmoistureretrievalinbareagriculturalareasusingsentinel1images AT pierreandregarambois soilmoistureretrievalinbareagriculturalareasusingsentinel1images AT hassanbazzi soilmoistureretrievalinbareagriculturalareasusingsentinel1images AT emmanuelferreira soilmoistureretrievalinbareagriculturalareasusingsentinel1images AT mehrezzribi soilmoistureretrievalinbareagriculturalareasusingsentinel1images |