To validate our maps, we collected approximately 10 000 irrigation and rainfed samples for the east (∼5000 for each category) (Fig. 2). Each irrigation sample records a center pivot location and the presence of irrigation infrastructure during 1997–2017 (Fig. 3). In addition, we measured the radius of each center pivot irrigation system, i.e., the distance from its center to its field boundary. Note that the length of corner arms (designed for corner irrigation) was not measured (e.g., Field 1 in Fig. 3). Stable non-irrigation samples record the locations with clear evidence of no irrigation infrastructure during the entire mapping period. The average pivot radius for all samples collected in the eastern CONUS was 330 m, but distributed bimodally around approximately 200 and 400 m, which correspond respectively to broader rectangular circumscribed crop fields of 40 and 160 acres (16.19 and 64.75 ha), respectively.

Our LANID reveals a steady increase in irrigated area throughout the CONUS, although there are some years with exceptional lower values – for example, 2012 and 2002, years in which there was significant drought (Fig. 4) (Otkin et al., 2018). Overall, irrigation area has increased by around 1.5×106 ha (Mha) during the period, from ∼23 Mha before 2000 to ∼24.5 Mha in 2016 with an average annual increase of ∼80000 ha. Consistent with earlier findings, the Central Valley of California, the High Plains portion of Texas, south-central Florida, and select western states (e.g., Utah, Colorado, Idaho, and Wyoming) experienced substantial irrigation loss during the period (per-state plots in Fig. 4). In contrast, irrigation increased in states across the Midwest (including Nebraska, North Dakota, and South Dakota), the Mississippi Alluvial Plain, and the East Coast. The largest gains occurred in Nebraska, Missouri, Michigan, Illinois, Arkansas, Mississippi, and Indiana, where irrigated area grew by over 100 000 ha per state.

Our LANID-derived irrigation changes agree well with USDA-NASS census-reported values (with R2 from 0.81 to 0.96), indicating that LANID and the USDA-NASS data are consistent in their detection of irrigation change at both county and state scales. Relative to the NASS data, however, our LANID maps predict slightly greater irrigated extent at the national level and slightly fewer net changes at both state and county levels, especially for the eastern CONUS (Fig. 5).

Aggregating the annual LANID maps to a finer but still intermediate 6 km resolution can reveal more localized trends than state- or county-level data allow, while also accommodating for the field-level stochasticity and variations that often occur within a single farm or shared water source (Fig. 6). Such a resolution is particularly helpful for identifying small pockets of change with countervailing trends that would otherwise be masked or undetected. For example, we found outlier locations of irrigation loss in the Mississippi Alluvial Plain and of irrigation gain in the central and southern High Plains aquifer.

Ultimately, when applied at the highest resolution, our LANID maps can be used to reliably characterize irrigation dynamics at the sub-field- to field-level with overall accuracy and kappa index of 81 % and 0.62, respectively (Table 2). For instance, sub-field to field level expansions, losses, and interannual variations in irrigation that are detectable from LANID can be clearly observed in north Texas (Fig. 7a). Although such a level of change detection in more humid areas is not as effective as more arid states due to a weaker contrast between irrigated and rainfed fields, LANID still provides a reasonable and accurate characterization of irrigation change through time there as well, as shown in the example in Michigan (Fig. 7b).

This study provides the first complete mapping and delineation of irrigated pasture and hay for the western US (Fig. 8). In this region, forage and fodder crops provide valuable feed for livestock, and irrigation is often necessary to cultivate certain species or attain viable yields. This contrasts with pasture and hay in the eastern states, where annual precipitation and soil moisture are typically sufficient for robust production of grass-based forage and fodder. Areas of irrigated pasture and hay have a pattern of land use distinct from that of irrigated croplands, as well as unique implications for water use and the environment.

Compared to the first version of LANID, which did not explicitly include irrigated pasture–hay, we found an average of 0.34 Mha more irrigated land (i.e., more irrigated pasture and hay included in LANID_V2 compared to LANID_V1) for the years 2013 to 2017 and a similarly larger amount (0.36 Mha) since the start of the study period. This increase in irrigated extent is lower than that of the USDA census of Agriculture's estimate of 1 Mha of irrigated pasture – the only other spatial (but coarse) estimate of such irrigated land use (Sanderson et al., 2012). The difference between our annual estimates and that of the census data likely reflects the fact that a large portion of irrigated pasture and hay (especially alfalfa) had already been mapped in the first version of LANID. To confirm this, we further calculated a direct estimate of only irrigated pasture–hay as all irrigated pixels classified as pasture or hay in the NLCD or CDL and estimated an average area of 1.39 Mha across the years 2008, 2011, 2013, and 2016. This estimate is 0.39 Mha higher than the 1 Mha reported by the Census of Agricultural but includes both pasture and hay, whereas the census estimate is for pasture only.

Across all types of irrigation – including cultivated cropland and pasture and hay – a total of 38.5 Mha of land was irrigated at least one time between 1997 and 2017, representing the maximum irrigated extent in the US for our study period (Fig. 9a and Table 3). Of these areas, just 24.2 Mha (62.8 %) was irrigated in 2017, and this annual utilization percentage ranged from 58.8 % to 64.0 % over the full study period. Across all pixels within the maximum irrigated extent, the mean annual irrigated frequency was 12.9 out of 21 years (Fig. 9b). The distribution of irrigated frequency suggests many areas consist of stable, persistent irrigation but that there also exists a substantial amount of land with intermittent irrigation use. Those pixels with the very lowest irrigation frequency likely reflect locations where irrigation ceased very early in the study period or was first initiated very late in the study period, and/or areas of potential misclassification.

Looking at the subset of lands that are no longer irrigated, we found 4 Mha of formerly irrigated land (i.e., not irrigated anytime in the most recent 3 years, 2015–2017, but that was irrigated at least three times prior) (Table 3). This formerly irrigated land is primarily distributed across the western states (as shown in Fig. 6) and may reflect areas where insufficient water availability has limited the ongoing use, or where salination of soils, socioeconomic drivers, or other superseding factors have resulted in a cessation of irrigated agriculture. Of these formerly irrigated areas, 71.6 % remains in crop production under rainfed conditions, primarily planted for corn (13.2 %), soybeans (12.3 %), and spring–winter wheat (12.2 %) as of 2017. The remaining locations have either been abandoned from cultivated crop production altogether (26.3 %) or converted to urban use (2.1 %). Those areas for which an irrigated crop is no longer viable may represent an opportunity for farmers to transition to grassland-based agriculture (Deines et al., 2020), for example via the introduction of pasture for livestock grazing or the harvesting of biomass for use as forage or cellulosic bioenergy feedstock (Robertson et al., 2017). As climate change and decreasing freshwater availability continue to strain water resources, the total area of formerly irrigated lands is likely to increase, thereby creating even further opportunity and greater need for alternative drought-resistant agricultural opportunities, such as those afforded by perennial feedstock production.

In addition to those locations where irrigation has ceased completely, we observed a substantial amount of land where irrigation remained active in the most recent years but where its use across time was discontinuous. For example, we found 25.5 Mha of land across the CONUS that had been irrigated spanning the whole study period (i.e., irrigated at least once for both 1997–1999 and 2015–2017), where over half of that subset (i.e., 13.5 Mha) could be best described as intermittently irrigated (frequency ≤ 18) (Table 3). As opposed to those locations with continuous annual irrigation use or where irrigation has ceased altogether, these intermittently irrigated lands appear to remain in irrigated agriculture today yet rely on such irrigation use just 67 % (median value) of the time across the 21-year study period. While further investigation is needed to better characterize these areas of partial irrigation use over time, it may be possible that they represent locations where irrigation is only supplemental (e.g., used only in dry years or when needed), shared among a single water source but rotated among multiple nearby fields, or used only in years with sufficient water availability or water application rights and allocations. Similar to formerly irrigated lands, these locations of intermittent irrigation application may present areas of opportunity or economic need for alternative rainfed agriculture in non-irrigated years. In such cases, drought-tolerant annual crops like forage or energy sorghum could potentially provide economic opportunities for producers and limited further strain on local hydrology (Enciso et al., 2015; Mullet et al., 2014; Cui et al., 2018).

Figure 10 presents the nationwide view of a single year LANID as well as other irrigation-specific products. The 30 m LANID 2005 map was aggregated to 10 km resolution (Fig. 10b) for comparing with other coarser-resolution maps. Across broad scales, all maps show similar irrigation hotspots of the High Plains aquifer, the Central Valley aquifer, the Mississippi Alluvial Plain, the Snake River aquifer, and the East Coast. While it might be reasonable to conclude that all these coarse-resolution maps can capture similar irrigation patterns at the national scale, regional views emphasize the details that are uniquely captured by LANID. For instance, LANID identifies fewer irrigated pixels at the eastern Columbia Plateau aquifer than other maps, especially compared to MIF and GIAM (Fig. 11). In another example of the High Plains aquifer, GIAM and MIF substantially overestimate irrigation extent in western and central Kansas compared to both LANID and MIrAD (Fig. 12). Among all comparison products, MIrAD provides the most similarity of irrigation patterns as LANID in the arid to semi-arid west and Midwest.

In more humid areas like the upper Midwest, our LANID map captures patterns that are considerably misclassified by other maps (Fig. 13). For example, GIAM and MIF omit the majority of irrigated fields in the region; MIrAD shows a clear administrative boundary effect and near-random distribution of irrigation within each county. At 10 km resolution, GMIA provides similar patterns as LANID but exaggerates the overall irrigation extent.

Locally, LANID shows a substantial improvement of spatial detail compared to other maps. For example, boundaries of center pivot and rectangular fields are clearly recognizable in LANID, while they are obscured even on the 250 m resolution MIrAD (insets (h) and (j) of Figs. 11 and 12). It is also evident that LANID shows comparable spatial details as other regional maps IrrMapper and AIM-HPA (inset (i) of Figs. 11 and 12) while still offering consistent and comprehensive coverage across the CONUS.

At the state level, our LANID estimates are consistent with USDA-NASS-reported data (Fig. 14b), although the agreement is weaker than that of products like MIrAD and GMIA, which both rely directly and exclusively on census data as areal reference (not shown in the figure). In contrast, MIF underestimates irrigated area at the state level (Fig. 14c), whereas GIAM substantially overestimates irrigation extent, especially for the states with reported area greater than 106 ha (Fig. 14d).

The results of pixel-based assessment further reveal the advantages of LANID over other nationwide maps (Table 4). We find that the overall accuracy is generally high for the NKOT region (i.e., Nebraska, Kansas, Oklahoma, and Texas) across all nationwide maps except for GIAM, with mean accuracy ranging from 78.9 % (MIF) to over 95 % (the LANID maps). Similarly, all maps show relatively high overall accuracy for the 11 western states, with values ranging from 82.6 % of MIF to 94.2 % of MIrAD. Despite these maps' reasonable accuracy in the west and even Midwest, they incorrectly assign a considerable number of rainfed fields as irrigated possibly due to coarse resolution and their difficulty separating them in some areas such as the Columbia Plateau aquifer (Fig. 11). For example, GIAM captures many low-density pixels in the west (Fig. 15c); MIF overestimates the locations with irrigation fraction between 0 % and 60 % (Fig. 15b); MIrAD maps irrigated pixels with a median fraction around 80 % (Fig. 15a).

In the eastern US, our LANID maps stand out with overall accuracy of 94.4 % – on par with their performance in the western US – whereas other maps show accuracy below 60 %. The extremely low accuracy of MIrAD, MIF, and GIAM in the east is attributable to their missing of most irrigated cropland as well as frequent false identification of rainfed cropland as irrigated (see Fig. 13 as an example), as characterized by omission error rates of over 80 % and commission error rates of over 45 % for the “irrigated” and “non-irrigated” classes, respectively. As a result, MIrAD maps irrigated pixels in the east that have a median irrigated fraction of about 50 % according to LANID (Fig. 15a); GIAM misclassifies a substantial number of low-density pixels (Fig. 15c); MIF substantially overestimates the locations with an irrigation fraction beyond 30 % (Fig. 15b).

We also compared our maps to AIM-HPA (i.e., Annual Irrigation Maps – High Plains aquifer) (Deines et al., 2019), a dataset with the same spatial and temporal resolution as LANID but covering only the High Plains aquifer. In this region, LANID performs comparably to the HPA-specific dataset, with overall accuracy of 95.9 % vs. 93.2 %, respectively, and kappa values of 0.89 vs. 0.82.

For a broader region with the 11 western states, our LANID maps show 92.8 % congruence (kappa of 0.84) with the reference data from IrrMapper (Ketchum et al., 2020) compared to a 99.1 % (kappa of 0.98) congruence of the IrrMapper product with its reference data. Such results follow in part from the methods of reference data utilization, as IrrMapper used 60 % of the validation data used in our comparison for its classifier training. Further differences between LANID and IrrMapper may stem from differences in sampled data and irrigated class definition. For example, the IrrMapper point-based irrigation samples were stratified from verified fields that were digitized in years different from the time of irrigation verification, such that they likely capture permanently irrigated croplands well but may potentially include fields that are partially irrigated or fallowed in any given year. In addition, IrrMapper's reference irrigation samples appear to include both irrigated croplands and other grass-like lands, such as irrigated turfgrass and groundwater- or fluvially subsidized grasslands and wetlands. This broader and more variable pool of reference data may thus help explain additional observed differences, such as occasionally less distinct field boundaries in IrrMapper compared with LANID and GI (e.g., left-hand portions of Fig. 11h–k) as well as the slightly higher apparent accuracy of MIrAD (which relies only on vegetation greenness) compared to LANID in the west when assessed against the IrrMapper reference data (Table 4). Thus, while overall performances of LANID and other datasets are similar in overlapping regions like the HPA and the western states, differences in each product's intent and class specificity will likely dictate preferences for specific user applications.

Both qualitative and quantitative assessments show extensive improvements of LANID compared to other currently available nationwide maps in terms of spatial detail and temporal frequency. Despite the advances, caution is still needed, especially when applying the dataset at the scale of individual fields in the eastern US. For example, mapping accuracy in the Mississippi Alluvial Plain region is uncertain due to the absence of reference data and the difficulty of collecting aerial ground truth in the area. In addition, map accuracy in the humid east is slightly lower than in the arid and semi-arid west. The quality of maps might also vary over time due to availability of clear Landsat observations. For instance, fewer Landsat images in 2012 constrained map quality, and scan-off effects of the ETM+ sensor might remain in some areas.

We took several post-classification steps to improve mapping accuracy, which also introduces limitations to LANID. First, our minimum mapping unit of 5 acres (2.02 ha) (i.e., 23 Landsat pixels) improved mapping confidence but also excluded smaller irrigated fields, such as fragmented irrigated vegetable fields often found in suburban and peri-urban areas. Second, the assumption that fields equipped with irrigation systems tend to be cropped and irrigated frequently could have incorrectly masked out some irrigated fields historically under long-term and frequent fallow conditions (e.g., irrigated – long-term fallow conditions – irrigated). Lastly, our current version of LANID covers only the period of 1997 to 2017, which might be problematic for users who want maps outside the study period. However, we hope to regularly update the existing dataset in the future to include the most recent years of available imagery, and, if able, extend the time series back in time through the duration of the Landsat record.

Given these uncertainties and limitations, future generations of LANID could benefit from the following improvements. First, we anticipate using our temporally extendable methodology to routinely update LANID, such that coverage could extend prior to 1997 and up to the most recent year. Efforts could also be made to enhance spatial detail (e.g., 10 m resolution) and mapping accuracy, particularly in the humid eastern US where contrasts between irrigated and rainfed crops are obscure. This is practical for recent years when both the revisit frequency and spatial resolution of satellite observations are greatly improved. Lastly, implementation of an irrigation-specific change detection algorithm could help improve the identification and consistency of monitoring variations in irrigation over time.

Our annual 30 m resolution nationwide LANID maps may be valuable to local, state, and regional water governance bodies, agribusinesses, and the research community for a variety of applications including water use estimation, risk assessment, use as model input, and more.

Our LANID maps could benefit water and agricultural managers by providing insights into irrigation changes (e.g., expansion and abandonment) at geographic and temporal scales relevant to decision-making. Our field-scale, wall-to-wall data will enable local and regional water management organizations, which may not otherwise have sufficient data or resources, to make better decisions that influence regional water availability. For example, state-level water managers and engineers who need to plan how much water to allocate for agriculture could utilize our irrigation distribution and change information to estimate demand. Policy makers may also use LANID to navigate future decision making and to evaluate federal agricultural, bioenergy, and conservation policies (Mccarthy et al., 2020; Lark, 2020).

Our dataset may also be useful for agribusinesses and entities across agricultural supply chains. For example, our maps could be used by companies that seek to reduce risk from water scarcity within their supply chains or lower the water footprint of their sourced products (Brauman et al., 2020). Additional applications may include business decision-making and financial investment (Turral et al., 2010), precise field-level water use estimation and solutions (Sadler et al., 2005), and crop yield prediction and its water resilience (Troy et al., 2015).

A key informant and collaborator in the development of our LANID maps has been the USGS, and the produced outputs may help support several ongoing USGS efforts, such as the National Water Census's efforts to provide water budgets at the watershed level (USGS, 2020a), the National Water-Use Information Program (NWUIP) dissemination of water use data (USGS, 2020b), and the Water Availability and Use Science Program (WAUSP) assessments of regional groundwater availability (USGS, 2020c). The research community within USGS also has high-priority goals to improve quantification of crop consumptive water use and project future water use. Our improved estimates of irrigation location, extent, and dynamics could help refine evapotranspiration estimates of irrigated croplands, thereby improving estimates of agricultural water use from field to aquifer scales and further supporting the ongoing expansion of detailed water use estimates across the continental US (Senay et al., 2016, 2017).

We also hope that our dataset will serve several needs in the broader research community, especially for those who study hydrology, agriculture, and the environment from local to nationwide scales. For example, our 30 m resolution irrigation data could be used to potentially improve the classification accuracy of or add irrigation status to existing USGS and USDA land cover maps (Brown et al., 2020; Lark et al., 2021; Wickham et al., 2021), investigate the relationships among irrigation changes and cropland expansion and abandonment (Lark et al., 2020; Yin et al., 2020), or explore the competition and biophysical interactions between irrigated agriculture and urban expansion (Xie et al., 2019a; Van Vliet, 2019; Bren D'amour et al., 2017). Users of previous coarser-resolution irrigation datasets will also benefit from the improvements in spatial detail, product frequency, and map accuracy. Existing nationwide irrigation datasets like MIrAD have been accessed by hundreds of users in academia and government via the USGS EROS website (Brown and Pervez, 2014). These data have been incorporated into studies to evaluate trends in ground and surface water quality, model evapotranspiration, and energy–water exchange at the surface boundary layer, and they reveal locations at risk of unsustainable irrigation (Brown and Pervez, 2014; Pryor et al., 2016; Seyoum and Milewski, 2016; Jin et al., 2011; Zaussinger et al., 2019). Our 30 m data products will enhance similar types of applications and enable many others through the improved spatial and temporal resolution. To this extent, several organizations have begun using our previously published LANID 2012 for further research and development activities, despite there being only 1 of the presently described 21 annual years of data available; such applications should be further enabled by the current full suite of products and time periods.

Lastly, our collected samples could help generate new threads of irrigation maps for the eastern US. Because insufficient ground reference data have long been a bottleneck to producing accurate classifiers for irrigation mapping, our verified locations could facilitate the development and evaluation of new models for irrigation detection, especially when other constraints are becoming relieved due to increasingly available high- to moderate-resolution remote sensing images, development of machine learning algorithms, and open access of cloud computing platforms.