Country-Scale Cropland Mapping in Data-Scarce Settings Using Deep
  Learning: A Case Study of Nigeria
Joaquin Gajardo Michele Volpi Daniel Onwude Thijs Defraeye
Cropland maps are a core and critical component of remote-sensing-based
agricultural monitoring, providing dense and up-to-date information about
agricultural development. Machine learning is an effective tool for large-scale
agricultural mapping, but relies on geo-referenced ground-truth data for model
training and testing, which can be scarce or time-consuming to obtain. In this
study, we explore the usefulness of combining a global cropland dataset and a
hand-labeled dataset to train machine learning models for generating a new
cropland map for Nigeria in 2020 at 10 m resolution. We provide the models with
pixel-wise time series input data from remote sensing sources such as
Sentinel-1 and 2, ERA5 climate data, and DEM data, in addition to binary labels
indicating cropland presence. We manually labeled 1827 evenly distributed
pixels across Nigeria, splitting them into 50\% training, 25\% validation, and
25\% test sets used to fit the models and test our output map. We evaluate and
compare the performance of single- and multi-headed Long Short-Term Memory
(LSTM) neural network classifiers, a Random Forest classifier, and three
existing 10 m resolution global land cover maps (Google's Dynamic World, ESRI's
Land Cover, and ESA's WorldCover) on our proposed test set. Given the regional
variations in cropland appearance, we additionally experimented with excluding
or sub-setting the global crowd-sourced Geowiki cropland dataset, to
empirically assess the trade-off between data quantity and data quality in
terms of the similarity to the target data distribution of Nigeria. We find
that the existing WorldCover map performs the best with an F1-score of 0.825
and accuracy of 0.870 on the test set, followed by a single-headed LSTM model
trained with our hand-labeled training samples and the Geowiki data points in
Nigeria, with a F1-score of 0.814 and accuracy of 0.842.