  Land cover classification in remote sensing is often faced with the challenge
of limited ground truth. Incorporating historical information has the potential
to significantly lower the expensive cost associated with collecting ground
truth and, more importantly, enable early- and in-season mapping that is
helpful to many pre-harvest decisions. In this study, we propose a new approach
that can effectively transfer knowledge about the topology (i.e. relative
position) of different crop types in the spectral feature space (e.g. the
histogram of SWIR1 vs RDEG1 bands) to generate labels, thereby support crop
classification in a different year. Importantly, our approach does not attempt
to transfer classification decision boundaries that are susceptible to
inter-annual variations of weather and management, but relies on the more
robust and shift-invariant topology information. We tested this approach for
mapping corn/soybeans in the US Midwest and paddy rice/corn/soybeans in
Northeast China using Landsat-8 and Sentinel-2 data. Results show that our
approach automatically generates high-quality labels for crops in the target
year immediately after each image becomes available. Based on these generated
labels from our approach, the subsequent crop type mapping using a random
forest classifier reach the F1 score as high as 0.887 for corn as early as the
silking stage and 0.851 for soybean as early as the flowering stage and the
overall accuracy of 0.873 in Iowa. In Northeast China, F1 scores of paddy rice,
corn and soybeans and the overall accuracy can exceed 0.85 two and half months
ahead of harvest. Overall, these results highlight unique advantages of our
approach in transferring historical knowledge and maximizing the timeliness of
crop maps. Our approach supports a general paradigm shift towards learning
transferrable and generalizable knowledge to facilitate land cover
classification.
