Predictive modelling of wheat yield from vegetation index time series in Spain: assessing the use of Corine Land Cover and CAP statistics to obtain crop masks

摘要

Quantifying wheat's production is essential to support food security management. It can be achieved with empirical models developed with the information provided by vegetation indices (Ⅵ). This work evaluated the performance of different time series of Ⅵ for the predictive modelling of wheat production and yield in Spain comparing two sources of cropland masks: wheat mask using Common Agricultural Policy declarations (CAP), and arable land from Corine Land Cover (CLC). Both sources were used to analyse the improvement derived from considering specific wheat masks. The wheat production and yield were modelled using time series of MODIS NDVI and EVI2 (2006 to 2016) from weekly surface reflectance products (MOD09Q1 v6) at 250 meters. The sum of Ⅵ values of one month after the maximum was used as this period is related with yield and production. Ⅵ indicators were filtered and aggregated to NUTS-3 level. The cropland masks were obtained either by combining the parcel boundaries with the CAP wheat reports, or from the CLC arable land category of 2006 and 2012 maps. Production (t) and yield (t ha-1) estimates were obtained from official statistics. Subsequently, different regression analyses were carried to build an overall model and single models for some NUTS2. Models using CAP wheat masks outperformed those of CLC, predicting more accurately production than yield. The best performance for production models using CAP was that of EVI2 in Castille and Leon (R~2=96% and Normalized Relative Error (NRE)=14.72%) and the best for CLC that of EVI2 in Spain (R~2=55% and NRE=58.01%). Regarding yield modelling, CAP with EVI2 in Aragon was the best (R~2=81% and NRE=10.57%) as well as CLC with EVI2 in Spain overall model (R~2=50% and NRE=22.34%). The findings of this work suggest that the use of specific crop masks is of paramount importance for the predictive modeling of crop production.