IrrMapper Irrigated Lands

Stay organized with collections Save and categorize content based on your preferences.
Dataset Availability
Dataset Provider
Earth Engine Snippet
irrigated-land landsat-derived


IrrMapper is an annual classification of irrigation status in the 11 Western United States made at Landsat scale (i.e., 30 m) using the Random Forest algorithm, covering years 1986 - present. While the IrrMapper paper describes classification of four classes (i.e., irrigated, dryland, uncultivated, wetland), the dataset is converted to a binary classification of irrigated and non-irrigated. 'Irrigated' refers to the detection of any irrigation during the year. The IrrMapper random forest model was trained using an extensive geospatial database of land cover from each of four irrigated- and non-irrigated classes, including over 50,000 human-verified irrigated fields, 38,000 dryland fields, and over 500,000 square kilometers of uncultivated lands.


30 meters


Name Description

Irrigated pixels have the value of 1, the other pixels are masked out.

Terms of Use

Terms of Use

Licensed under the Creative Commons Attribution 4.0 International License.


  • Ketchum, D.; Jencso, K.; Maneta, M.P.; Melton, F.; Jones, M.O.; Huntington, J. IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S., Remote Sens. 2020, 12, 2328. doi:10.3390/rs12142328

Explore in Earth Engine

var dataset = ee.ImageCollection("UMT/Climate/IrrMapper_RF/v1_0");
var irr_2013 = dataset.filterDate('2013-01-01', '2013-12-31').select('classification');

var visualization = {
  min: 0.0,
  max: 1.0,
  palette: ['blue']
Map.addLayer(irr_2013, visualization, 'IrrMapper 2013');
Map.setCenter(-112.516, 45.262, 10);