Yield estimation using SPOT-VEGETATION products: A case study of wheat in European countries

• We use time series of SPOT-VEGTATION products (NDVI and FAPAR) and a statistical model to estimate and forecast wheat yield in Europe.
• The model is run in monitoring and forecasting mode to estimate yield after and during the growing season, respectively.
• Model accuracy is comparable to that of a reference crop modelling approach.
• Performances are spatially heterogeneous with large errors in specific countries.
• We found strong correlation between the errors of PLSR model and the results of CGMS forecasting.

In the period 1999–2009 ten-day SPOT-VEGETATION products of the Normalized Difference Vegetation Index (NDVI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) at 1 km spatial resolution were used in order to estimate and forecast the wheat yield over Europe. The products were used together with official wheat yield statistics to fine-tune a statistical model for each NUTS2 region, based on the Partial Least Squares Regression (PLSR) method. This method has been chosen to construct the model in the presence of many correlated predictor variables (10-day values of remote sensing indicators) and a limited number of wheat yield observations. The model was run in two different modalities: the “monitoring mode”, which allows for an overall yield assessment at the end of the growing season, and the “forecasting mode”, which provides early and timely yield estimates when the growing season is on-going. Performances of yield estimation at the regional and national level were evaluated using a cross-validation technique against yield statistics and the estimations were compared with those of a reference crop growth model. Models based on either NDVI or FAPAR normalized indicators achieved similar results with a minimal advantage of the model based on the FAPAR product. Best modelling results were obtained for the countries in Central Europe (Poland, North-Eastern Germany) and also Great Britain. By contrast, poor model performances characterize countries as follows: Sweden, Finland, Ireland, Portugal, Romania and Hungary. Country level yield estimates using the PLSR model in the monitoring mode, and those of a reference crop growth model that do not make use of remote sensing information showed comparable accuracies. The largest estimation errors were observed in Portugal, Spain and Finland for both approaches. This convergence may indicate poor reliability of the official yield statistics in these countries.