KEYWORDS: Biological research, Vegetation, Synthetic aperture radar, Polarization, Polarimetry, Near infrared, General packet radio service, Backscatter, Image information entropy, Short wave infrared radiation
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.
Crop monitoring at a fine scale is critical from an environmental perspective since it provide crucial information to combine increased food production and sustainable management of agricultural landscapes. The recent Synthetic Aperture Radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) time series offer a great opportunity to monitor cropland (structure, biomass and phenology) due to their high spatial and temporal resolutions. In this study, we assessed the potential of Sentinel data to derive Wet Biomass (WB), Dry biomass (DB), water content and crop Phenological Stages (PS). This study focuses on wheat and rapeseed, which represent two of the most important seasonal crops of the world in terms of occupied area. Satellites and ground data were collected over two French temperate agricultural landscapes, in northern France and Brittany. Spectral bands and vegetation indices were derived from the S-2 images and backscattering coefficients and polarimetric indicators from the S-1 images. We used linear models to estimate the Crop Parameters (CP) of wheat and rapeseed crops. Satellite images were then classified using a random forest incremental procedure based on the importance rank of the input features to discriminate PS. Results showed that S-1 features were more efficient than S-2 features to estimate CP of rapeseed while S-2 features were better for wheat. We demonstrated the high potential of S-1 and 2 to predict principal PS (kappa=0.75) while secondary PS were misclassified. For wheat, the succession of PS predicted was consistent, further research is required to confirm the potential of S-1 and 2.
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