Evaluation of Sentinel-1 and -2 time series for estimating LAI and biomass of wheat and rapeseed crop types

Monitoring crops at a fine scale is critical because it provides information crucial for assessing the influence of increased food production on sustainable management of agricultural landscapes. We assessed the potential of synthetic aperture radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) images to derive crop biophysical parameters of wheat and rapeseed in Brittany, France. We generated a dataset of 29 features, including spectral bands and vegetation indices derived from S-2 images, and backscattering coefficients and polarimetric indicators derived from S-1 images. Then, we compared the respective value of S-1 and S-2 features to estimate crop LAI, biomass, and water content (WC) using a Gaussian processes regression. The results show that best S-2-based models were achieved using the green band, NIR bands, and vegetation indices for the wheat WC, LAI, and biomass, respectively, and the shortwave-infrared bands for rapeseed biomass. Concerning S-1-based models, the σ0VH  :  σ0VV ratio was the most relevant feature for wheat LAI and rapeseed biomass, and the Shannon entropy polarization contribution best performed for wheat WC. We highlighted not only the value of optical S-2 images but also the importance of polarimetric indicators derived from SAR S-1 images for estimating crop biophysical parameters.