  An accurate and precise understanding of global irrigation usage is crucial
for a variety of climate science efforts. Irrigation is highly
energy-intensive, and as population growth continues at its current pace,
increases in crop need and water usage will have an impact on climate change.
Precise irrigation data can help with monitoring water usage and optimizing
agricultural yield, particularly in developing countries. Irrigation data, in
tandem with precipitation data, can be used to predict water budgets as well as
climate and weather modeling. With our research, we produce an irrigation
prediction model that combines unsupervised clustering of Normalized Difference
Vegetation Index (NDVI) temporal signatures with a precipitation heuristic to
label the months that irrigation peaks for each cropland cluster in a given
year. We have developed a novel irrigation model and Python package
("Irrigation30") to generate 30m resolution irrigation predictions of cropland
worldwide. With a small crowdsourced test set of cropland coordinates and
irrigation labels, using a fraction of the resources used by the
state-of-the-art NASA-funded GFSAD30 project with irrigation data limited to
India and Australia, our model was able to achieve consistency scores in excess
of 97\% and an accuracy of 92\% in a small geo-diverse randomly sampled test
set.
