Spatial Downscaling of Agricultural Land-Use Data: An Econometric Approach Using Cross Entropy

Abstract

We propose a spatial disaggregation tool to help researchers make the best use of aggregated data in studies of land use. The proposed approach uses parcel-level agricultural data in conjunction with biophysical processes to break down agricultural regional data to the pixel level. It is a two-step procedure. First, we estimate a land-use model using a multinomial logit model. Second, we disaggregate the observed regional data using a generalized cross-entropy approach, taking the first-step predictions as priors. This procedure is applied to the French Picardie region. Results indicate a significant correlation between observed and estimated land-use shares.