Evaluation of Sentinel-1 and -2 time series to derive crop phenology and biomass of wheat and rapeseed: northern France and Brittany case studies

Audrey Mercier; Julie Betbeder; Jacques Baudry; Julien Denize; Vincent Leroux; Jean-Luc Roger; Fabien Spicher; Laurence Hubert-Moy

doi:10.1117/12.2533132

21 October 2019 Evaluation of Sentinel-1 and -2 time series to derive crop phenology and biomass of wheat and rapeseed: northern France and Brittany case studies

Audrey Mercier, Julie Betbeder, Jacques Baudry, Julien Denize, Vincent Leroux, Jean-Luc Roger, Fabien Spicher, Laurence Hubert-Moy

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Proceedings Volume 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI; 1114903 (2019) https://doi.org/10.1117/12.2533132
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

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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Audrey Mercier, Julie Betbeder, Jacques Baudry, Julien Denize, Vincent Leroux, Jean-Luc Roger, Fabien Spicher, and Laurence Hubert-Moy "Evaluation of Sentinel-1 and -2 time series to derive crop phenology and biomass of wheat and rapeseed: northern France and Brittany case studies", Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 1114903 (21 October 2019); https://doi.org/10.1117/12.2533132

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