Remote sensing crop group-specific indicators to support regional yield forecasting in Europe

Operational crop yield forecasting services typically provides crop yield forecasts based on regression models between official yields and agro-environmental variables, among which meteorological data, crop simulation model or satellite-derived indicators. The reliability with which these variables infer on yields depends, among many other factors, also on their aggregation in the space domain, for example on the type of the crop masks utilized in the aggregation process from point to regional scale. This work investigates how the yield explanatory power of satellite-derived indicators is changing moving from time-stable arable land masks to annual crop group-specific masks. We compare in particular the linkage between time series of regional crop yield in Europe and the Normalized Difference Vegetation Index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS), when generic arable land masks or crop group-specific and year-specific information are applied to aggregate pixel values at regional level. Regional (Eurostat level NUTS-2) crop yield statistics were collected from official databases for the period 2003-2019, while NDVI data were derived from MODIS daily products at 250 m spatial resolution for the same reference period. Regional NDVI profiles were retrieved by averaging single pixels time series according to the information of five crop masks, including generic arable land masks, crop group-specific static masks (separating winter and spring crops from summer crops), and annual crop groupspecific masks (distinguishing between the two crop groups and varying in time). A compared correlation analysis between yield data and regional temporal NDVI profiles was performed assuming a linear regression model. Coefficient of determination R2 and Root Mean Squared Error (RMSE) were computed to assess the models' errors and to analyze the effect of the aggregation on the different crop masks. Results indicated an improvement in yield estimation when using annual crop group-specific indicators with respect to generic and static products. Although not homogeneously distributed throughout Europe, advantages have been highlighted both in terms of accuracy and timeliness of the prediction. In most regions, the introduction of annual masks allowed to reduce RMSE values by 0.3 t/ha and advanced forecast times by up to 30 days. The added value in the use of annual crop group-specific masks concerned the two European most cultivated crops, namely soft wheat and grain maize.

Automated summary

Operational crop yield forecasting services use regression models to predict crop yields based on agro-environmental variables such as meteorological data, crop simulation models, or satellite-derived indicators. This study examines the impact of using different crop masks on the correlation between yield data and the Normalized Difference Vegetation Index (NDVI) in Europe. The results show that using annual crop group-specific masks improves yield estimation accuracy and timeliness, particularly for soft wheat and grain maize.