The spatial extents of verified irrigated lands were compiled from various federal and state sources across the nation and combined into a single Geographic Information System (GIS) geodatabase for the purpose of model training and validation. In cooperation with U.S. Geological Survey (USGS), researchers at the University of Wisconsin (UW) generated a nation-wide map of irrigated lands using remote-sensing techniques that will be incorporated into future irrigation water-use models. The verified spatial data varies in scope, accuracy, and time period represented, but in general represents GIS coverages (polygons) of agricultural land irrigated for at least some period during 2002–17. Data from 14 states were provided to UW (Arizona, California, Colorado, Florida, Georgia, Idaho, Illinois, Mississippi, Montana, New Mexico, Texas, Utah, Washington, and Wyoming). It is important to validate that the remote sensing techniques correctly identify both irrigated and non-irrigated land. Varying data sources prevent this approach from being applied throughout the United States, but most datasets used for validation include at least some “non irrigated” land identification.

In 2015, agricultural irrigation withdrawals accounted for about 42 percent of the total freshwater withdrawals in the United States (Dieter and others, 2018). Consistent and accurate designations of irrigated agricultural lands, irrigation system type, conveyance systems, and water source (groundwater or surface water) are essential for the determination of irrigation water use and ultimately the sound management of our nation’s water resources. Several local, state, and federal agencies compile data (crops, irrigation, irrigation system type, etc.) that can be used to estimate irrigation withdrawals for agricultural. The format of these data varies from data tables, typically compiled at the county level, to spatial Geographic Information System (GIS) polygon layers of agricultural lands. These data sources are often incomplete, out of date, or inconsistently compiled. The USGS and the University of Wisconsin-Madison developed annual Landsat-based Irrigation Dataset (LANID), which consists of irrigation maps, derivative products, and manually collected ground reference data covering the conterminous US (CONUS) for the period of 1997–2017 (Xie and Lark, 2021a). These maps were developed using verified irrigated-lands GIS datasets (i.e. training data) coupled with remotely-sensed, 30-meter resolution Landsat-derived data. The current and future availability of verified field-level data is required to train and validate this and other models.

In 2015, agricultural irrigation withdrawals accounted for about 42 percent of the total freshwater withdrawals in the United States (Dieter and others, 2018). Consistent and accurate designations of irrigated agricultural lands, irrigation system type, conveyance systems, and water source (groundwater or surface water) are essential for the determination of irrigation water use and ultimately the sound management of our nation’s water resources. Several local, state, and federal agencies compile data (crops, irrigation, irrigation system type, etc.) that can be used to estimate irrigation withdrawals for agricultural. The format of these data varies from data tables, typically compiled at the county level, to spatial Geographic Information System (GIS) polygon layers of agricultural lands. These data sources are often incomplete, out of date, or inconsistently compiled. The USGS and the University of Wisconsin-Madison developed annual Landsat-based Irrigation Dataset (LANID), which consists of irrigation maps, derivative products, and manually collected ground reference data covering the conterminous US (CONUS) for the period of 1997–2017 (Xie and Lark, 2021a). These maps were developed using verified irrigated-lands GIS datasets (i.e. training data) coupled with remotely-sensed, 30-meter resolution Landsat-derived data. The current and future availability of verified field-level data is required to train and validate this and other models.

In 2015, agricultural irrigation withdrawals accounted for about 42 percent of the total freshwater withdrawals in the United States (Dieter and others, 2018). Consistent and accurate designations of irrigated agricultural lands, irrigation system type, conveyance systems, and water source (groundwater or surface water) are essential for the determination of irrigation water use and ultimately the sound management of our nation’s water resources. Several local, state, and federal agencies compile data (crops, irrigation, irrigation system type, etc.) that can be used to estimate irrigation withdrawals for agricultural. The format of these data varies from data tables, typically compiled at the county level, to spatial Geographic Information System (GIS) polygon layers of agricultural lands. These data sources are often incomplete, out of date, or inconsistently compiled. The USGS and the University of Wisconsin-Madison developed annual Landsat-based Irrigation Dataset (LANID), which consists of irrigation maps, derivative products, and manually collected ground reference data covering the conterminous US (CONUS) for the period of 1997–2017 (Xie and Lark, 2021a). These maps were developed using verified irrigated-lands GIS datasets (i.e. training data) coupled with remotely-sensed, 30-meter resolution Landsat-derived data. The current and future availability of verified field-level data is required to train and validate this and other models.

In 2015, agricultural irrigation withdrawals accounted for about 42 percent of the total freshwater withdrawals in the United States (Dieter and others, 2018). Consistent and accurate designations of irrigated agricultural lands, irrigation system type, conveyance systems, and water source (groundwater or surface water) are essential for the determination of irrigation water use and ultimately the sound management of our nation’s water resources. Several local, state, and federal agencies compile data (crops, irrigation, irrigation system type, etc.) that can be used to estimate irrigation withdrawals for agricultural. The format of these data varies from data tables, typically compiled at the county level, to spatial Geographic Information System (GIS) polygon layers of agricultural lands. These data sources are often incomplete, out of date, or inconsistently compiled. The USGS and the University of Wisconsin-Madison developed annual Landsat-based Irrigation Dataset (LANID), which consists of irrigation maps, derivative products, and manually collected ground reference data covering the conterminous US (CONUS) for the period of 1997–2017 (Xie and Lark, 2021a). These maps were developed using verified irrigated-lands GIS datasets (i.e. training data) coupled with remotely-sensed, 30-meter resolution Landsat-derived data. The current and future availability of verified field-level data is required to train and validate this and other models.

Field-verified irrigated lands data were collected for the Remote Sensing Component of the St. Mary-Milk Rivers Consumptive Use study to aid in the identification and delineation of agricultural fields that are irrigated in 2021 and 2022 in the Milk River basin. This field verification of irrigated fields will provide data that will be used to ground truth evapotranspiration estimates obtained in the Milk River basin using remote sensing methods. This work is part of a larger project aimed at developing a historical database representing monthly actual evapotranspiration (ETa) totals in the Milk River basin from 1985-present using remote sensing. This database will lay the foundation for the establishment of a remote sensing tool with which to objectively estimate ETa in the upper Milk River basin on a monthly interval using high spatial resolution (100 meter) satellite imagery.

Field-verified irrigated lands data were collected for the Remote Sensing Component of the St. Mary-Milk Rivers Consumptive Use study to aid in the identification and delineation of agricultural fields that are irrigated in 2021 and 2022 in the Milk River basin. This field verification of irrigated fields will provide data that will be used to ground truth evapotranspiration estimates obtained in the Milk River basin using remote sensing methods. This work is part of a larger project aimed at developing a historical database representing monthly actual evapotranspiration (ETa) totals in the Milk River basin from 1985-present using remote sensing. This database will lay the foundation for the establishment of a remote sensing tool with which to objectively estimate ETa in the upper Milk River basin on a monthly interval using high spatial resolution (100 meter) satellite imagery.

This dataset contains Landsat-derived images of Evaporative Fraction (ETf), Reference Evapotranspiration (ETo), and Actual Evapotranspiration (ETa) over a portion of California’s Central Valley for 15 dates in 2016. Each of the 15 images used in this study had three corresponding Tif files representing ETf, ETo, and ETa. Data used in this project was sourced from Landsat 8 Surface Reflectance Tier 1 images processed in Google Earth Engine (GEE). These images contain five visible and near-infrared (VNIR) bands and two short-wave infrared (SWIR) bands processed to orthorectified surface reflectance, and two thermal infrared (TIR) bands processed to orthorectified brightness temperature. To determine thermal properties of images to aid in ET calculation, the TIR Band 10 (B10) containing brightness temperature was chosen to determine Land Surface Temperature (LST).

Agricultural land cover for the western United States. This dataset was developed from Sagestitch, the Eastern Washington Shrubsteppe Mapping Project, and several state level GAP products (AZ, CA, NM, OR, and WA).

Agricultural land cover for the western United States. This dataset was developed from Sagestitch, the Eastern Washington Shrubsteppe Mapping Project, and several state level GAP products (AZ, CA, NM, OR, and WA).