This raster file represents land within the Mountain Home 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 use of Random Forest, a supervised machine learning algorithm. Classification models often employ Random Forest due to its accuracy and efficiency at labeling large spatial datasets. 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 that will be later used on unseen data. Model accuracy is determined using a subset of the training points, otherwise known as a validation dataset. Several satellite-based input datasets are made available to the Random Forest model, which aid in distinguishing characteristics of irrigated lands. These characteristics allow patterns to be established by the model, e.g., high NDVI during summer months for cultivated crops, or consistently low ET for dryland areas. Mountain Home Irrigated Lands 2023 employed the following input datasets: US Geological Survey (USGS) products, including Landsat 8/9 and 10-meter 3DEP DEM, and European Space Agency (ESA) Copernicus products, including Harmonized Sentinel-2 and Global 30m Height Above Nearest Drainage (HAND). For the creation of manually labeled training points, IDWR staff accessed the following datasets: NDVI derived from Landsat 8/9, Sentinel-2 CIR imagery, US Department of Agriculture National Agricultural Statistics Service (USDA NASS) Cropland Data Layer, Active Water Rights Place of Use data from IDWR, and USDA’s National Agriculture Imagery Program (NAIP) imagery. All datasets were available for the current year of interest (2023). The published Mountain Home Irrigated Lands 2023 land classification raster was generated after four model runs, where at each iteration, IDWR staff added or removed training points to help improve results. Early model runs showed poor results in riparian areas near the Snake River, concentrated animal feeding operations (CAFOs), and non-irrigated areas at higher elevations. These issues were resolved after several model runs in combination with post-processing masks. Masks used include Fish and Wildlife Service’s National Wetlands Inventory (FWS NWI) data. These data were amended to exclude polygons overlying irrigated areas, and to expand riparian area in specific locations. A manually created mask was primarily used to fill in areas around the Snake River that the model did not uniformly designate as irrigated. Ground-truthing and a thorough review of IDWR’s water rights database provided further insight for class assignments near the town of Mayfield. Lastly, the Majority Filter tool in ArcGIS was applied using a kernel of 8 nearest neighbors to smooth out “speckling” within irrigated fields. The masking datasets and the final iteration of training points are available on request. Information regarding Sentinel and Landsat imagery:All satellite data products used within the Random Forest model were accessed via the Google Earth Engine API. To find more information on Sentinel data used, query the Earth Engine Data Catalog https://developers.google.com/earth-engine/datasets) using “COPERNICUS/S2_SR_HARMONIZED.” Information on Landsat datasets used can be found by querying “LANDSAT/LC08/C02/T1_L2” (for Landsat 8) and “LANDSAT/LC09/C02/T1_L2” (for Landsat 9).Each satellite product has several bands of available data. For our purposes, shortwave infrared 2 (SWIR2), blue, Normalized Difference Vegetation Index (NDVI), and near infrared (NIR) were extracted from both Sentinel and Landsat images. These images were later interpolated to the following dates: 2023-04-15, 2023-05-15, 2023-06-14, 2023-07-14, 2023-08-13, 2023-09-12. Interpolated values were taken from up to 45 days before and after each interpolated date. April-June interpolated Landsat images, as well as the April interpolated Sentinel image, were not used in the model given the extent of cloud cover overlying irrigated area. For more information on the pre-processing of satellite data used in the Random Forest model, please reach out to IDWR at gisinfo@idwr.idaho.gov.

This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 – from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code.

citations

• Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031

Abstract

This data package consists of multiple decades of modified normalized difference water index (MNDWI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). By providing a metric by which to reliably identify bodies of open water, these MNDWI data are intended to facilitate analyses of land-based environmental variables (e.g., urbanization, vegetation, land surface temperature) and can also be used to track long-term and seasonal change in the coarse extent of open water as a land-cover type. MNDWI was derived, following the methods of Xu (2006), from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see \'Methods and Protocols\') and accompanying Javascript code.

Citations:

• Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18--27. https://doi.org/10.1016/j.rse.2017.06.031
• Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025--3033. https://doi.org/10.1080/01431160600589179