  Crop maps are crucial for agricultural monitoring and food management and can
additionally support domain-specific applications, such as setting cold supply
chain infrastructure in developing countries. Machine learning (ML) models,
combined with freely-available satellite imagery, can be used to produce
cost-effective and high spatial-resolution crop maps. However, accessing ground
truth data for supervised learning is especially challenging in developing
countries due to factors such as smallholding and fragmented geography, which
often results in a lack of crop type maps or even reliable cropland maps. Our
area of interest for this study lies in Himachal Pradesh, India, where we aim
at producing an open-access binary cropland map at 10-meter resolution for the
Kullu, Shimla, and Mandi districts. To this end, we developed an ML pipeline
that relies on Sentinel-2 satellite images time series. We investigated two
pixel-based supervised classifiers, support vector machines (SVM) and random
forest (RF), which are used to classify per-pixel time series for binary
cropland mapping. The ground truth data used for training, validation and
testing was manually annotated from a combination of field survey reference
points and visual interpretation of very high resolution (VHR) imagery. We
trained and validated the models via spatial cross-validation to account for
local spatial autocorrelation and selected the RF model due to overall
robustness and lower computational cost. We tested the generalization
capability of the chosen model at the pixel level by computing the accuracy,
recall, precision, and F1-score on hold-out test sets of each district,
achieving an average accuracy for the RF (our best model) of 87%. We used this
model to generate a cropland map for three districts of Himachal Pradesh,
spanning 14,600 km2, which improves the resolution and quality of existing
public maps.
