Agriculture Remotely-Sensed Yield Algorithm (arya): Application to Winter Wheat.

This work presents the Agriculture Remotely-sensed Yield Algorithm (ARYA), a new EO-based empirical winter wheat yield model. It is based on the un-mixing of the wheat signal from a coarse resolution EO data at 1 km by using yearly crop type masks. This wheat signal is used to calibrate the model using as inputs the seasonal amplitude and length of the DVI peak extracted from MODIS data and the average of the evaporative fraction (EF) 30 days after the peak. The three regressors proposed are focused on the reproduction stage of the wheat (DVI amplitude and length) and the grain filling process (EF 30-days average). The model was applied to estimate the national and subnational winter wheat yield in the main wheat exporting countries from 2001 to 2017. At the subnational level the model shows very good performance with a r2 higher than 0.7 and RMSE lower than 15%. At the national level the model provides a strong r2 higher than 0.8 and a RMSE lower than 8%, which demonstrates good performance of the models at this scale.