6 Years of SAR (Sentinel-1) and Optical (Sentinel 2, Landsat-8) Acquisitions over Agricultural Surfaces in Southwestern France

Monitoring the biophysical parameters of agricultural surfaces is a key issue for food security in the context of climate change. Since 2016, agricultural surfaces can be monitored from space at high spatial resolution (~10/30 m) in the microwave and optical domains owing to radiometer and SAR sensors onboard Sentinel-1, -2 and Landsat-8 satellites. This paper draws on multi-temporal acquisitions over a six-year period to analyze satellite time series for the main winter and summer crops (corn, sunflower, soybean, sorghum, rapeseed, wheat) grown in southwestern France and more widely cultivated around the world. From January 2016 to December 2021, satellite signals extracted at the field spatial scale offer a unique opportunity to monitor agricultural surfaces with a high temporal resolution (every 1 or 2 days) never achieved before thanks to the combination of multi-sensor and multi-orbit data. Analyses on the impact of the topography and satellites’ viewing angles showed that the NDVI values derived from Sentinel-2 and Landsat-8 are very close (r > 0.92) and can be merged to construct multi-annual time series. Angular sensitivity is much more pronounced for radar images; while it demonstrates a weaker cross-polarization and polarization ratio, it is greater for co-polarization. Optical and radar time series are modulated in time and amplitude depending on yearly climatic conditions and agricultural practices. The combined use of the ascending and descending orbits of the two Sentinel-1 satellites makes it possible to detect specific periods (harvest, flowering) for certain crops (wheat and sunflower). The long-term approach has enabled the modeling of satellite time series using double logistic functions with good performance (r > 0.92 on average), allowing the identification of interannual variations of crop development driven by climatic conditions and agricultural practices.

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Publication Details

Journal
Remote Sensing
Published
2026-03-05
DOI
https://doi.org/10.3390/rs18050790
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

6 Years of SAR (Sentinel-1) and Optical (Sentinel 2, Landsat-8) Acquisitions over Agricultural Surfaces in Southwestern France

Rémy Fieuzal, Azza Gorrab, Bertrand Ygorra, Serge Riazanoff et al.
Remote Sensing
Remote Sensing in Agriculture
article

6 Years of SAR (Sentinel-1) and Optical (Sentinel 2, Landsat-8) Acquisitions over Agricultural Surfaces in Southwestern France

Rémy Fieuzal, Azza Gorrab, Bertrand Ygorra, Serge Riazanoff, Alexis Martin-Comte, Frédéric Frappart
article en

Abstract

Monitoring the biophysical parameters of agricultural surfaces is a key issue for food security in the context of climate change. Since 2016, agricultural surfaces can be monitored from space at high spatial resolution (~10/30 m) in the microwave and optical domains owing to radiometer and SAR sensors onboard Sentinel-1, -2 and Landsat-8 satellites. This paper draws on multi-temporal acquisitions over a six-year period to analyze satellite time series for the main winter and summer crops (corn, sunflower, soybean, sorghum, rapeseed, wheat) grown in southwestern France and more widely cultivated around the world. From January 2016 to December 2021, satellite signals extracted at the field spatial scale offer a unique opportunity to monitor agricultural surfaces with a high temporal resolution (every 1 or 2 days) never achieved before thanks to the combination of multi-sensor and multi-orbit data. Analyses on the impact of the topography and satellites’ viewing angles showed that the NDVI values derived from Sentinel-2 and Landsat-8 are very close (r > 0.92) and can be merged to construct multi-annual time series. Angular sensitivity is much more pronounced for radar images; while it demonstrates a weaker cross-polarization and polarization ratio, it is greater for co-polarization. Optical and radar time series are modulated in time and amplitude depending on yearly climatic conditions and agricultural practices. The combined use of the ascending and descending orbits of the two Sentinel-1 satellites makes it possible to detect specific periods (harvest, flowering) for certain crops (wheat and sunflower). The long-term approach has enabled the modeling of satellite time series using double logistic functions with good performance (r > 0.92 on average), allowing the identification of interannual variations of crop development driven by climatic conditions and agricultural practices.

Remote SensingVol. 18(5)
Centre National de la Recherche Scientifique (FR), Université Toulouse III - Paul Sabatier (FR), Engineering School of Information and Digital Technologies (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Biologie du Fruit et Pathologie (FR), Santé et Agroécologie du Vignoble (FR), Centre d'Études Spatiales de la Biosphère (FR), Center for Northern Studies (CA), Unité mixte de recherche Œnologie (FR), Institut de Recherche pour le Développement (FR), Université du Québec à Trois-Rivières (CA)
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Remote Sensing in Agriculture
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