Remote sensing of field-scale irrigation withdrawals in the central Ogallala aquifer region

Estimating irrigation water use from remotely sensed evapotranspiration data: Accuracy and uncertainties across spatial scales

“…The sources of uncertainty we discuss in Section 4.1 contributed to variable levels of agreement between ET-based and reported water withdrawals and applications across thecomparisons we conducted. At the management area scale, we found a generally strong positive correlation (e.g., R 2 generally above 0.85; Table 1), comparable to other studies using remotely sensed data to estimate irrigation depths with statistical models (Filippelli et al, 2022;Majumdar et al, 2022;Wei et al, 2022). However, we observed a general positive bias and substantially more year-to-year variability in ET-based irrigation than in the reported data.…”

“…While developing these links may not be needed for many applications, such as estimating regional water use (Figure 3) or field-based water management (Figure 7), connecting the point of diversion with place of use is critical to evaluate irrigation application depths and to assess the effectiveness of conservation measures and the ultimate impacts of pumping on other aspects of regional agrohydrological systems such as streamflow (Kniffin et al, 2020;Zipper, Carah, et al, 2019;Zipper et al, 2021), aquifer dynamics (Feinstein et al, 2016;Peterson & Fulton, 2019;Wilson et al, 2021), or groundwater-dependent ecosystems (Tolley et al, 2019). At the WRG scale, our ET-based calculations of irrigation volume had better agreement than calculations of irrigation depth (Figure 5), consistent with results from the nearby Colorado portion of the Republican River Basin (Filippelli et al, 2022). The weaker relationship between calculated and reported irrigation depth, compared to irrigation volume, reflects the importance of irrigated area as a determinant of overall irrigation volumes (Lamb et al, 2021;Wei et al, 2022).…”

“…Furthermore, our ET-based irrigation volumes did not account for leakage in irrigation systems and other losses of water between where it is pumped from the ground but before it reaches the field, though based on the high efficiency in the SD-6 LEMA area we expect that these losses are minimal. These findings suggest that, for annual or finer temporal resolutions, the use of more complex water balance approaches, such as soil water balance models (Dhungel et al, 2020;Kharrou et al, 2021;Pereira, Paredes, & Jovanovic, 2020), will be necessary to accurately disentangle the rates, locations, and timing of irrigation applications, and there may be promise through the assimilation of additional data sets such as in situ or remotely sensed soil moisture (Dari et al, 2020;Filippelli et al, 2022;Jalilvand et al, 2019).…”

“…While many past studies have sought to estimate irrigation water use using satellite-based ET data and other hydrological variables such as soil moisture (Brocca et al, 2018;Dari et al, 2020;Ketchum et al, 2023), these estimates have typically been evaluated against aggregated statistics or synthetic model estimates of water use. Other studies use statistical or machine learning approaches to relate ET to observed water use, but these approaches are limited in terms of their applicability outside of the model training region (Filippelli et al, 2022;Majumdar et al, 2022;Wei et al, 2022). As a result, there is a lack of knowledge about how effectively ET data can be translated into irrigation water withdrawals and applications across different spatial scales, from an individual field to a region, which are relevant to regulatory and management purposes.…”

Is It Possible to Quantify Irrigation Water‐Use by Assimilating a High‐Resolution Satellite Soil Moisture Product?

“…Satellite observations provide a unique opportunity to monitor irrigation and address many of the above‐mentioned issues with in situ measurements. Thermal and optical sensors can measure evapotranspiration (ET) using crop coefficients obtained from satellite‐based vegetation indices (Allen et al., 2005; Farg et al., 2012) or surface energy balance that is highly correlated with irrigation (Allen et al., 2007; Bastiaanssen et al., 2014; Filippelli et al., 2022; Javadian et al., 2019; Karimi et al., 2013). However, ET only measures consumptive water use which is generally less than the amount of irrigation applied to the field.…”