Improved regular and rapid monitoring techniques are needed to quantify evapotranspiration (ET) precisely on agricultural fields to enhance the efficient usage of water resources and protect water quality and the environment. This study proposes estimating and mapping ET using a vegetation index coupled with a crop coefficient estimation method over the famous rice bowl region of Tamil Nadu, India. The study utilized a very high-resolution satellite—Sentinel—of resolution 10 m. The study is based on a crop coefficient—Normalized Difference Vegetation Index (NDVI)—based ET estimation, which is one of the most adaptable practical approaches in quantifying crop water usage in multiple phases of crop growth on a regional scale. The results have been further calibrated using the surface energy-based Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model to assess estimation correctness. The estimations analysed for the 2016–2019 kharif cropping season show good overall correlation with the coefficient of determination (R²) of ≥0.72, with mean bias error (MBE) of ≤0.0009 mm day⁻¹ and root mean square error (RMSE) of ≤0.67 mm day⁻¹, indicating a positive assessment of ET for the delta region of Tamil Nadu for reaching vindicable intendance of crop water in this region.

中文翻译：

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泰米尔纳德邦饭碗的基于卫星的区域尺度蒸发蒸腾量估算图：还有一点点水*

需要改进定期和快速监测技术来精确量化农田的蒸散量 (ET)，以提高水资源的有效利用并保护水质和环境。本研究建议在印度泰米尔纳德邦著名的稻谷地区使用植被指数和作物系数估计方法来估计和绘制 ET。该研究使用了分辨率为 10 m 的超高分辨率卫星 Sentinel。该研究基于作物系数——归一化植被指数 (NDVI)——基于 ET 估计，这是在区域尺度上量化作物生长多个阶段的作物用水量的最适应性强的实用方法之一。结果已使用基于表面能的高分辨率内化校准（METRIC）模型映射蒸散进行了进一步校准，以评估估计的正确性。为 2016-2019 年分析的估计数kharif种植季节与决定系数 ( R ² ) ≥ 0.72显示出良好的整体相关性，平均偏差误差 (MBE) ≤ 0.0009 mm day ^-1和均方根误差 (RMSE) ≤ 0.67 mm day ^-1，表明对泰米尔纳德邦三角洲地区的 ET 进行了积极的评估，以达到该地区作物用水的合理意图。