The integration of remote sensing data in agricultural statistics is a research topic with a long history. The research focus is on using statistical models to link ground and remote sensing data such that the resulting estimators are design-consistent.
A design-consistent estimator assisted by linear models is well established in the literature. However, it requires enough geographic information about the boundaries of agricultural parcels to develop a simple sample of areas. Many countries use complex samples based on non-georeferenced list frames of households or farms and reduce to point data the georeferenced information required for linking ground and remote sensing data.
Data on crop acreage observed at a point are necessarily categorical because a point is dimensionless. Little work has been done on the integration of categorical ground data within complex list samples using remote sensing data. Our focus was on using multinomial logit models for this integration. Special attention was paid to evaluate the cost efficiency of remote sensing data.
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