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.
Delon et al. (Thu,) studied this question.