- Dataset Availability
- 1986-01-01T00:00:00Z–2018-01-01T00:00:00
- Dataset Provider
- University of Montana / Montana Climate Office
- Earth Engine Snippet
-
ee.ImageCollection("UMT/Climate/IrrMapper_RF/v1_1")Sign up for Earth Engine
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- Tags
Description
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.
For version 1.1, the original training data was re-run on Landsat Collection 2 and brought up-to-date.
Bands
Resolution
30 meters
Bands
| Name | Description |
|---|---|
classification |
Irrigated pixels have the value of 1, the other pixels are masked out. |
Terms of Use
Terms of Use
Citations
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 with Earth Engine
Code Editor (JavaScript)
var dataset = ee.ImageCollection('UMT/Climate/IrrMapper_RF/v1_1');
var irr = dataset.filterDate('2018-01-01', '2018-12-31').mosaic();
var irr = irr.mask(irr.eq(1));
var visualization = {
min: 0.0,
max: 1.0,
palette: ['blue']
};
Map.addLayer(irr, visualization, 'IrrMapper 2018');
Map.setCenter(-112.516, 45.262, 10);