Mapping three decades of annual irrigation across the US High Plains Aquifer using Landsat and Google Earth Engine

Understanding how irrigated areas change over time is vital to effectively manage limited agricultural water resources, but long-term, high-resolution, and spatially explicit datasets are rare. The High Plains Aquifer (HPA) in the central United States is one of the largest and most stressed aquifer systems in the world. It supports a $20 billion economy, but groundwater use is unsustainable over much of the aquifer. Emerging cloud computing tools like Google Earth Engine (GEE) make it possible to use the full Landsat record to monitor regional systems like the HPA with high spatial and temporal resolution over multiple decades. However, challenges remain to develop irrigation classification methods that are robust to a wide range of climate conditions and crop types, evolving management, and missing data. Here, we addressed these challenges to produce an annual, moderately high resolution (30 m) irrigation map time series from 1984 to 2017 over the aquifer. Leveraging GEE's extensive data catalog, we combined Landsat imagery, environmental covariables, and a large heterogeneous ground truth dataset to create a single random forest classifier applied annually to the entire region. Following classification, we applied the Bayesian Updating of Land-Cover (BULC) algorithm to fill imagery gaps and reduce commission errors in the provisional irrigation time series. Novel neighborhood greenness indices contributed to an overall 91.4% map accuracy across years; county statistics (r(2) = 0.86) were similarly well-matched. Trend analysis of irrigated area through time identified regions of stable, expanding, and declining irrigated area. Given declining aquifer storage, we estimate that up to 24% of currently irrigated area may be lost this century. To date, the map dataset is the longest, highest resolution large-scale record of where and when irrigation occurs. It is freely available for stakeholders, managers, and researchers to inform policies and management decisions, as well as for use in hydrology, agronomy, and climate models.