Estimating wheat evapotranspiration through remote sensing utilizing GeeSEBAL and comparing with lysimetric data

IF 5.7 3区环境科学与生态学 Q1 WATER RESOURCES Applied Water Science Pub Date : 2024-08-12 DOI:10.1007/s13201-024-02248-6

Neda Baboli, Houshang Ghamarnia, Maryam Hafezparast Mavaddat

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Abstract

Accurate estimation of ET is vital for water resource management. In recent decades, researchers have focused on utilizing satellite imagery for this purpose. The use of RS data has enabled the development of new models that provide detailed spatial assessments. GeeSEBAL, an automated ET estimation tool, employs the SEBAL algorithm via GEE. The current version of GeeSEBAL utilizes Landsat images and ERA5 global reanalysis data to produce time series estimates. Landsat 8 images were processed into a 16-day time series spanning 2013–2022, specifically during the wheat growing season. To validate the GeeSEBAL model for 2013–2014, results were compared against lysimeter data. Subsequently, ET was calculated for the years 2015–2022. The evaluation of GeeSEBAL against lysimetric data, by metrics such as R², RMSE, MAE, NSE, and NRMSE, yielded values of 0.94, 0.98, 0.07, 0.86, and 0.62, respectively. Those findings underscore the importance of GeeSEBAL for estimating wheat ET in regions with limited data availability.

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利用 GeeSEBAL 遥感估算小麦蒸散量并与水分测定数据进行比较

准确估算蒸散发对水资源管理至关重要。近几十年来，研究人员主要利用卫星图像来实现这一目的。RS 数据的使用使得新模型的开发成为可能，从而提供详细的空间评估。自动蒸散发估算工具 GeeSEBAL 通过 GEE 采用了 SEBAL 算法。当前版本的 GeeSEBAL 利用大地遥感卫星图像和 ERA5 全球再分析数据进行时间序列估算。Landsat 8 图像被处理成跨度为 2013-2022 年的 16 天时间序列，特别是在小麦生长季节。为了验证 2013-2014 年的 GeeSEBAL 模型，将结果与浸透计数据进行了比较。随后，计算了 2015-2022 年的蒸散发。根据 R2、RMSE、MAE、NSE 和 NRMSE 等指标对 GeeSEBAL 与浸透仪数据进行评估后，得出的数值分别为 0.94、0.98、0.07、0.86 和 0.62。这些发现强调了 GeeSEBAL 在数据有限地区估算小麦蒸散发的重要性。

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