Satellite remote sensing-based surface energy balance (SEB) techniques have emerged as useful tools for quantifying spatialized actual evapotranspiration at various temporal and spatial scales. However, discontinuous data acquisitions and/or gaps in image acquisition due to cloud cover can limit the applicability of satellite remote sensing (RS) in agriculture water management where continuous time series of daily crop actual evapotranspiration (ET_{c act}) are more valued. The aim of the research is to construct continuous time series of daily ET_{c act} starting from temporal estimates of actual evapotranspiration obtained by SEB modelling (ET_{a eb}) on Landsat-TM images. SEBAL model was integrated with the FAO 56 evaporation model, RS-retrieved vegetative biomass dynamics (by NDVI) and on-field measurements of soil moisture and potential evapotranspiration. The procedure was validated by an eddy covariance tower on a vineyard with partial soil coverage in the south of Sardinia Island, Italy. The integrated modeling approach showed a good reproduction of the time series dynamics of observed ET_{c act} (R² =0.71, MAE=0.54 mm d^-1, RMSE=0.73 mm d^-1). A daily and a cumulative monthly temporal analysis showed the importance of integrating parameters that capture changes in the soil-plant-atmosphere (SPA) continuum between Landsat acquisitions. The comparison with daily ET_{c act} obtained by the referenced ET fraction (ET_rF) method that considers only weather variability (by ET_o) confirmed the lead of the proposed procedure in the spring/early summer periods when vegetation biomass changes and soil water evaporation have a significant weight in the ET process. The applied modelling approach was also robust in constructing the missing ET_{c act} data under scenarios of limited cloud-free Landsat acquisitions. The presented integrated approach has a great potential for the near real time monitoring and scheduling of irrigation practices. Further testing of this approach with diverse dataset and the integration with the soil water modeling is to be analyzed in future work.

基于卫星遥感的表面能量平衡 (SEB) 技术已成为量化不同时空尺度空间化实际蒸散量的有用工具。然而，由于云覆盖导致的不连续数据采集和/或图像采集间隙可能会限制卫星遥感 (RS) 在农业水资源管理中的适用性，其中每日作物实际蒸发量 (ET_{c act}) 的连续时间序列更受重视。这项研究的目的是建立连续时间序列日常ET的_C法案由SEB建模获得的实际蒸散量的时空估计（ET开始_一个EB) 在 Landsat-TM 图像上。SEBAL 模型与 FAO 56 蒸发模型、RS 检索的植物生物量动态（通过 NDVI）以及土壤水分和潜在蒸散量的现场测量相结合。该程序通过位于意大利撒丁岛南部部分土壤覆盖的葡萄园上的涡流协方差塔进行了验证。集成建模方法显示了观察到的 ET _{c act}的时间序列动态的良好再现（R ² =0.71，MAE=0.54 mm d ^-1，RMSE=0.73 mm d ^-1）。每日和每月累积的时间分析显示了整合参数的重要性，这些参数可以捕捉 Landsat 采集之间土壤-植物-大气 (SPA) 连续体的变化。与仅考虑天气变化（通过 ET _o ）的参考 ET 分数（ET _rF）方法获得的每日 ET _{c act}的比较证实了在春季/初夏植被生物量发生变化和土壤水分蒸发时所提出的程序的领先地位在 ET 过程中具有重要的权重。应用的建模方法在构建缺失的 ET _{c 行为方面}也很稳健在有限的无云 Landsat 采集场景下的数据。所提出的综合方法对于灌溉实践的近实时监测和调度具有巨大潜力。将在未来的工作中分析使用不同数据集进一步测试这种方法以及与土壤水模型的整合。