2024 Irrigated Lands for the Eastern Snake Plain Aquifer (ESPA): Machine Learning Generated

This raster file represents land within the ESPA study boundary classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 10-meter spatial resolution. These classifications were determined at the pixel level by using random forest, a supervised machine learning algorithm. To build a random forest model and supervise the learning process, IDWR staff create pre-labeled data, or training points, which are used by the algorithm to construct decision trees. A validation dataset was not used to evaluate model performance/accuracy, but the resulting classification was reviewed and post-processed by IDWR staff. Several satellite-based input datasets are made available to the random forest model, which aid in distinguishing characteristics of irrigated lands. ESPA Irrigated Lands 2024 employed the following input datasets: US Geological Survey (USGS) products, including Landsat 8/9 and 10-meter 3DEP DEM, European Space Agency (ESA) Copernicus products, including Harmonized Sentinel-2, and Global 30m Height Above Nearest Drainage (HAND) (Donchyts et al., 2016), period averaged PRISM (Daly et al., 2008) variables, and OpenET eeMETRIC Monthly Evapotranspiration v2.0 provided by OpenET on Earth Engine. All temporal datasets were confined to March 1- November 1. IDWR staff used the following datasets to label training data and review model output: Landsat 8/9, Sentinel-2 SWIR visualizations and NDVI (Normalized Difference Vegetation Index), US Department of Agriculture National Agricultural Statistics Service (USDA NASS) Cropland Data Layer, IDWR’s Active Water Rights Place of Use, and USDA’s National Agriculture Imagery Program (NAIP) imagery. NAIP imagery from 2023 and 2025 were used as a reference; all other datasets were available for 2024. Post-processing of model output included a manually adjusted wetland mask from the Fish and Wildlife Service’s National Wetlands Inventory wetlands dataset, as well as a manually created mask specific to issues found in the ESPA 2024 model results. The masks and final iteration of training points area available on request. References: Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J. & Pasteris, P.A. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology, 28, 2031-2064. [doi:10.1002/joc.1688](https://doi.org/10.1002/joc.1688) Donchyts, G., Winsemius, H., Schellekens, J., Erickson, T., Gao, H., Savenije, H., & van de Giesen, N. (2016). Global 30m height above the nearest drainage (HAND). Geophysical Research Abstracts, 18, EGU2016-17445-3. EGU General Assembly 2016.