Estimation of Sunflower Yield Using a Simplified Agrometeorological Model Controlled by Optical and SAR Satellite Data

This paper aims at estimating the yield of sunflower at field scale by testing different ways to control an agrometeorological model, using the leaf area index (LAI) and/or the dry mass (DM) of the crop. The LAI is derived from multitemporal satellite images acquired in the visible, near-infrared, and microwave wavelengths by five sensors (Formosat-2, Spot-4/5, TerraSAR-X, Radarsat-2), over a region located in southwestern France, while the DM is measured in situ. Results show that the temporal dynamics of LAI and DM can be accurately simulated over the five studied working farms (R
_DM
²
> 0.85 and R
_LAI
²
> 0.94 with a relative root-mean-square error (RMSE) <; 30%), when the model is controlled by both the DM (measured over only one field) and the LAI (estimated from the modified triangular vegetation index (MTVI2) or σ°
_{C -VV}
). The seed yield is then accurately estimated (R
²
> 0.85) over 140 ha, with low errors ranging from 0.20 to 0.54 t·ha
^-1
by using both the LAI derived from microwave and optical data (LAI
_MTVI2
and LAI
_σ°C-VV
), in order to reduce the overestimations related to the excessive development of the LAI during the first phenological stages, or to the presence of dew on the vegetation, respectively, affecting the LAI derived from the MTVI2 or σ°
_{C -VV}
.