Yanhua Xie | Geology Reports

USGS2025

Estimating irrigation consumptive use for the conterminous United States: Coupling satellite-sourced estimates of actual evapotranspiration with a national hydrologic model

Irrigation consumptive use is crucial information for water resource management and this information is generally not available for most regional and national assessments. From 2000 to 2020, daily estimates of consumptive use were produced for the conterminous U.S. at the 12-digit hydrologic unit code watershed level. Using the Simplified Surface Energy Balance model within the OpenET tool on Google Earth Engine, actual evapotranspiration estimates were derived from Landsat land surface temperature data. These estimates were mapped to the National Hydrologic Model (NHM) spatial units for irrigated areas defined by the Landsat Irrigation Dataset (LANID). The NHM allows for simultaneous estimation of consumptive use and effective precipitation. Results were compared to the 2015 USGS water use report, revealing annual maximum difference in consumptive use of 37 cm, with an average absolute difference of 13 cm and a bias of 2.7 %. Comparisons to field resolution irrigation withdrawals reported by growers in 4 major irrigation regions across the nation were made using efficiencies reported in the literature. These comparisons resulted in root mean square errors that ranged between 10 and 18 cm per year and biases of −18.2 to 15.8 %. Significant differences in these different reported estimates represent challenges in accurate data availability and inconsistent methods and definitions used in assessments. Annual consumptive use in the U.S. can vary by 37 % due to droughts, particularly in regions more heavily dependent on precipitation. Significant increases in consumptive use were noted over the 20-year period in western states, especially the Colorado River basin from 2010 to 2020, exacerbating water supply declines.

conterminous United States↗

USGS2019

Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine

Accurate and timely information on the distribution of irrigated croplands is crucial to research on agriculture, water availability, land use, and climate change. While agricultural land use has been well characterized, less attention has been paid specifically to croplands that are irrigated, in part due to the difficulty in mapping and distinguishing irrigation in satellite imagery. In this study, we developed a semi-automatic training approach to rapidly map irrigated croplands across the conterminous United States (CONUS) at 30 m resolution using Google Earth Engine. To resolve the issue of lacking nationwide training data, we generated two intermediate irrigation maps by segmenting Landsat-derived annual maximum greenness and enhanced vegetation index using county-level thresholds calibrated from an existing coarse resolution irrigation map. The resulting intermediate maps were then spatially filtered to provide a training data pool for most areas except for the upper midwestern states where we visually collected samples. We then used random samples extracted from the training pool along with remote sensing-derived features and climate variables to train ecoregion-stratified random forest classifiers for pixel-level classification. For ecoregions with a large training pool, the procedure of sample extraction, classifier training, and classification was conducted 10 times to obtain stable classification results. The resulting 2012 Landsat-based irrigation dataset (LANID) identified 23.3 million hectares of irrigated croplands in CONUS. A quantitative assessment of LANID showed superior accuracy to currently available maps, with a mean Kappa value of 0.88 (0.75–0.99), overall accuracy of 94% (87.5–99%), and producer’s and user’s accuracy of the irrigation class of 97.3% and 90.5%, respectively, at the aquifer level. Evaluation of feature importance indicated that Landsat-derived features played the primary role in classification in relatively arid regions while climate variables were important in the more humid eastern states. This methodology has the potential to produce annual irrigation maps for CONUS and provide insights into the field-level spatial and temporal aspects of irrigation.

conterminous United States↗