  Crop type mapping at the field level is critical for a variety of
applications in agricultural monitoring, and satellite imagery is becoming an
increasingly abundant and useful raw input from which to create crop type maps.
Still, in many regions crop type mapping with satellite data remains
constrained by a scarcity of field-level crop labels for training supervised
classification models. When training data is not available in one region,
classifiers trained in similar regions can be transferred, but shifts in the
distribution of crop types as well as transformations of the features between
regions lead to reduced classification accuracy. We present a methodology that
uses aggregate-level crop statistics to correct the classifier by accounting
for these two types of shifts. To adjust for shifts in the crop type
composition we present a scheme for properly reweighting the posterior
probabilities of each class that are output by the classifier. To adjust for
shifts in features we propose a method to estimate and remove linear shifts in
the mean feature vector. We demonstrate that this methodology leads to
substantial improvements in overall classification accuracy when using Linear
Discriminant Analysis (LDA) to map crop types in Occitanie, France and in
Western Province, Kenya. When using LDA as our base classifier, we found that
in France our methodology led to percent reductions in misclassifications
ranging from 2.8% to 42.2% (mean = 21.9%) over eleven different training
departments, and in Kenya the percent reductions in misclassification were
6.6%, 28.4%, and 42.7% for three training regions. While our methodology was
statistically motivated by the LDA classifier, it can be applied to any type of
classifier. As an example, we demonstrate its successful application to improve
a Random Forest classifier.
