Comparing Satellite-Derived and Model-Based Surface Soil Moisture for Spring Barley Yield Prediction in Central Europe

Abstract

Surface soil moisture (SSM) has proven to be an important variable for the yield prediction of main crops like maize and wheat, but its value for spring barley, the third most cultivated crop in Europe, has not yet been evaluated. This study assesses how much of spring barley yield variability can be explained by the commonly used model and satellite-based global SSM products ERA5 SWVL1 and H SAF. A Feed Forward Neural Network, SSM time series, and reference yield data are used to predict spring barley yield at NUTS level for Austria, Czechia, and Germany. A random train-test split is used to assess the explained variability and a cross-validation at the NUTS level for the spatial evaluation. The results indicate the following: (1) ERA5 SWVL1 achieved an R² of 0.37, H SAF an R² of 0.33; (2) Both products achieved the lowest RMSE and MAE in Czechia, high RMSE and MAE values are observed in Eastern Germany. (3) ERA5 SWVL1 performed better in areas with low sensitivity for microwaves like the Alpine region, but both products achieved similar results in 80% of the NUTS regions. These findings contribute to better utilization of SSM and more accurate yield predictions for spring barley and similar crops.