High-resolution global irrigation prediction with Sentinel-2 30m data

TL;DR

This paper introduces a novel high-resolution global irrigation prediction model using Sentinel-2 data, combining unsupervised clustering and precipitation heuristics, achieving high accuracy and consistency with a new Python package.

Contribution

The authors developed a new 30m resolution irrigation prediction model and Python package that outperforms existing methods in accuracy and resource efficiency.

Findings

Achieved over 97% consistency scores and 92% accuracy on a diverse test set.

Produced global irrigation maps at 30m resolution.

Demonstrated efficiency with fewer resources compared to existing projects.

Abstract

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.