2019 Irrigated Lands for the Eastern Snake River Plain: Machine Learning Generated

ESPA Irrigated Lands 2019 was created for use in water budget studies in the ESPA study boundary. The area of interest was determined by Hydrology Section staff at IDWR, and a study boundary was given to GIS staff and used to clip the model output. The random forest (RF) model is a type of supervised machine learning algorithm requiring GIS staff to provide manually labeled training data. GIS staff also provide the RF model with several input features, typically raster datasets that help distinguish characteristics of irrigated lands. ESPA Irrigated Lands 2019 used the following as input features: • Landsat 8 [2] and Sentinel-2 [3] surface reflectance imagery (bands: SWIR 2, NIR, Blue, and calculated NDVI) • 10-meter digital elevation model [4] (including slope and aspect) • PRISM Climate Dataset [5] (800 m resolution; parameters: precipitation, mean temperature, dewpoint temperature, maximum vapor pressure deficit) • Height Above Nearest Drainage (HAND) [6] • IDWR METRIC [7] evapotranspiration dataset • Topographic Wetness Index, derived from the digital elevation model For additional information on processing Landsat and Sentinel-2 imagery, please see below. Additional datasets used only for labeling training data include Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC), IDWR-provided Active Water Rights Place of Use, the Cropland Data Layer [8] for 2019, and the National Agriculture Imagery Program (NAIP) imagery [9] for Idaho 2019. The accuracy of the ESPA Irrigated Lands 2019 dataset was verified by several methods. Firstly, a validation test was conducted by withholding a subset of the training data to evaluate how well the model classified unseen information. Second, GIS staff ran several iterations of the model with variations of training data, with the goal of improving classification for areas consistently misclassified. This process requires GIS staff knowledge, aided by supplementary datasets, to review the area and make decisions. Once a model iteration is determined as ‘final’, a manual mask was created to correct any remaining misclassification in the dataset. Manual corrections for the ESPA Irrigated Lands 2019 dataset were focused on the area between Ashton and Lamont, where false positive labels of “irrigated” occurred on dryland-managed fields. Some areas classified as irrigated near Bellevue were masked out due to suspected wetland. A general wetland mask for the entire ESPA study boundary was also applied. Other manual corrections were made throughout the study area, specifically for pivot-irrigated fields not matching the NAIP field boundaries. Decisions made during manual masking were conservative, relying heavily on both the presence of an active water right and clear indications of artificial application of water as observed in satellite imagery. References: [1] https://developers.google.com/earth-engine/apidocs/ee-classifier-smilerandomforest [2] https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSL30_v002 [3] https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSS30_v002 [4] https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m [5] 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) [6] 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. [7] https://data-idwr.hub.arcgis.com/documents/365d91be4da4407bbe3df11f242b34c7/about [8] https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL [9] https://developers.google.com/earth-engine/datasets/catalog/USDA_NAIP_DOQQ