Assimilation of Multisensor Optical and Multiorbital SAR Satellite Data in a Simplified Agrometeorological Model for Rapeseed Crops Monitoring
Aubin Alliès, Antoine Roumiguié, Rémy Fieuzal, Jean-François Dejoux, Anne Jacquin, Amanda Veloso, Luc Champolivier, Frédéric Baup
Abstract
This paper investigates the potential of the assimilation of both Synthetic Aperture Radar (SAR) -derived Dry Mass (DM) and optically-derived Green Area Index (GAI) in an agro-meteorological model (i.e. SAFY) for a better rapeseed crops modeling. The GAI was derived from both 566 Sentinel-2 and 149 Landsat-8 images, whereas DM was derived from 884 Sentinel-1 images acquired from six different orbits. The ground data were collected during 3 agricultural years on 43 rapeseed fields located in three study areas in France with contrasted pedoclimatic conditions. Results show that the temporal evolutions of both DM and GAI can be accurately simulated over the 43 monitored rapeseed fields (R2 = 0.84 and 0.92, respectively and relative Root Mean Square Error, RMSEr = 41 % and 28 %, respectively) for the best satellite configuration. Tested assimilation scenarios reveal that the concomitant assimilation of SAR and optical data allows a significant better control of the model than the assimilation of SAR or optical data alone. Small differences in the simulations of the model are observed when it is controlled by either multisensor optical and/or multiorbital SAR data or mono-sensor optical and/or mono-orbital SAR data. The discrepancies of performance between fields push towards the strengthening of this study by considering other rapeseed fields with ground observations acquired worldwide for the calibration of SAR-derived DM. These results are however promising in view of the development of a near-real time assimilation scheme as a decision support tool for farmers and decision makers.