Abstract
The objective of this study is to address the capabilities of multi-temporal optical images to estimate the fine-scale yield variability of wheat, over a study site located in southwestern France. The methodology is based on the Landsat-8 and Sentinel-2 satellite images acquired after the sowing and before the harvest of the crop throughout four successive agricultural seasons, the reflectance constituting the input variables of a statistical algorithm (random forest). The best performances are obtained when the Normalized Difference Vegetation Index (NDVI) is combined with the yield maps collected during the crop rotation, the agricultural season 2014 showing the lower level of performances with a coefficient of determination (R2) of 0.44 and a root mean square error (RMSE) of 8.13 quintals by hectare (q.h−1) (corresponding to a relative error of 12.9%), the three other years being associated with values of R2 close or upper to 0.60 and RMSE lower than 7 q.h−1 (corresponding to a relative error inferior to 11.3%). Moreover, the proposed approach allows estimating the crop yield throughout the agricultural season, by using the successive images acquired from the sowing to the harvest. In such cases, early and accurate yield estimates are obtained three months before the end of the crop cycle. At this phenological stage, only a slight decrease in performance is observed compared to the statistic obtained just before the harvest.
Highlights
Over the last 50 years, the world production of wheat has increased with a positive trend of around8.7 million tons per year
The results are focused on one of the four tested cases, the other cases being presented in the remainder of the paper
The ratio with more data in the training phase is associated with the best performance, as shown by the maximum R2 and minimum root mean square error (RMSE) values obtained with the ratio 90/10
Summary
Over the last 50 years, the world production of wheat has increased with a positive trend of around8.7 million tons per year (value derived from the statistics of [1]). The topics covered are diverse, ranging from land use to monitoring parameters related to the photosynthetic status of vegetation, to estimating height or biomass, or even detecting areas of lodging or inverting top surface moisture throughout the growing cycle [3,4,5,6,7]. These studies are based on a wide variety of satellite missions, characterized by sensors operating in different wavelength domains (i.e., visible, near and medium infrared, microwave) and delivering different types of products
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