Recent advances in sensors onboard harvesting machines allow accessing the intra-plot variability of yields, spatial scale fully compatible with numerous on-going satellite missions. The aim of this study is to estimate the sunflower yield at the intra-plot spatial scale using the multi-temporal images provided by the Landsat-8 and Sentinel-2 missions. The proposed approach is based on a statistical algorithm, testing different sampling strategies to partition the dataset into independent training and testing sets: A random selection (testing different ratio), a systematic selection (focusing on different plots) and a forecast procedure (using an increasing number of images). Emphasis is put on the use of high spatial and temporal resolution satellite data acquired throughout two agricultural seasons, on a study site located in southwestern France. Ground measurements consist in intra-plot yields collected by a surveying harvesting machine with GPS system on track mode. The forecast of yield throughout the agricultural season provides early accurate estimation two months before the harvest, with R2 equal to 0.59 or 0.66 and root mean square error (RMSE) of 4.7 or 3.4 q ha?1, for the agricultural seasons 2016 and 2017 respectively. Results obtained with the random selection or the systematic selection will be developed later, in a longer paper.
展开▼
