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<p>As water scarcity intensifies in arable land, accurate estimation of regional agricultural water requirements is becoming increasingly important. Where direct measurement of actual water use is impractical, agronomists and land managers commonly rely on crop water requirement estimates to quantify water demand and thereby guide irrigation scheduling and manage water allocations. A widely used approach for calculating crop water requirements is to determine the crop coefficient (<i>K</i><sub>c</sub>) from satellite-derived NDVI within a functional form such as <i>K</i><sub>c</sub> = <i>K</i><sub>c max</sub> × <i>f</i>(NDVI), which, when multiplied by reference evapotranspiration (ET<sub>o</sub>), yields crop evapotranspiration under standard conditions (ET<sub>c</sub>) for field and agronomic crops. In the absence of local measurements, FAO-56 climate-adjusted mid-season coefficients (<i>K</i><sub>c mid</sub>) may be used, but these often deviate from field conditions due to site-specific variability in canopy, soil, and management. To address this limitation, this study proposes a <i>K</i><sub>c</sub> correction method that integrates satellite NDVI with a two-source energy balance model (TSEB-SM) to determine corrected <i>K</i><sup><i>*</i></sup><sub>c−TSEB−SM</sub>. The corrected coefficients were tested within the <i>K</i><sub>c</sub>-ET<sub>o</sub> framework using field data from ten cropland sites (10 crop types) across North America, Europe, and China, where crops were grown under local management practices. Results showed that the <i>K</i><sup>*</sup><sub>c−TSEB−SM</sub> approach improved ET<sub>c</sub> estimation accuracy relative to the climate-adjusted FAO-56 <i>K</i><sub>c mid</sub>, with RMSE reduced by ~ 40% (1.43 to 0.83&#xa0;mm/d), bias reduced by ~ 80% (0.92 to 0.04&#xa0;mm/d) and Nash–Sutcliffe Efficiency (NSE) increased from 0.18 to 0.72. This remote-sensing-model-based <i>K</i><sub>c</sub> correction method improves estimates of <i>K</i><sub>c</sub> and crop water requirements, offering potential for more resource-efficient agricultural water management across various spatial scales, including fields, watersheds, and groundwater-dependent farming areas.</p>

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Improved estimation of crop coefficients and water requirements for agronomic crops using a remote sensing modeling approach

  • 期刊论文

  • 发表日期：

    2026年4月16日

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  • Teng Zhang,
  • Dong Chu,
  • Junkang Hu,
  • Michael Liddell,
  • Xiaolong Liu,
  • Liang Sun,
  • Lisheng Song

摘要

As water scarcity intensifies in arable land, accurate estimation of regional agricultural water requirements is becoming increasingly important. Where direct measurement of actual water use is impractical, agronomists and land managers commonly rely on crop water requirement estimates to quantify water demand and thereby guide irrigation scheduling and manage water allocations. A widely used approach for calculating crop water requirements is to determine the crop coefficient (Kc) from satellite-derived NDVI within a functional form such as Kc = Kc max × f(NDVI), which, when multiplied by reference evapotranspiration (ETo), yields crop evapotranspiration under standard conditions (ETc) for field and agronomic crops. In the absence of local measurements, FAO-56 climate-adjusted mid-season coefficients (Kc mid) may be used, but these often deviate from field conditions due to site-specific variability in canopy, soil, and management. To address this limitation, this study proposes a Kc correction method that integrates satellite NDVI with a two-source energy balance model (TSEB-SM) to determine corrected K*c−TSEB−SM. The corrected coefficients were tested within the Kc-ETo framework using field data from ten cropland sites (10 crop types) across North America, Europe, and China, where crops were grown under local management practices. Results showed that the K*c−TSEB−SM approach improved ETc estimation accuracy relative to the climate-adjusted FAO-56 Kc mid, with RMSE reduced by ~ 40% (1.43 to 0.83 mm/d), bias reduced by ~ 80% (0.92 to 0.04 mm/d) and Nash–Sutcliffe Efficiency (NSE) increased from 0.18 to 0.72. This remote-sensing-model-based Kc correction method improves estimates of Kc and crop water requirements, offering potential for more resource-efficient agricultural water management across various spatial scales, including fields, watersheds, and groundwater-dependent farming areas.