A remote sensing and modeling integrated approach for constructing continuous time series of daily actual evapotranspiration

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 (ETc act) are more valued. The aim of the research is to construct continuous time series of daily ETc act starting from temporal estimates of actual evapotranspiration obtained by SEB modelling (ETa 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 ETc 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 ETc act obtained by the referenced ET fraction (ETrF) method that considers only weather variability (by ETo) 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 ETc 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

From Global Goals to Local Gains—A Framework for Crop Water Productivity

Crop water productivity (CWP) has become a recognised indicator in assessing the state of Sustainable Development Goals (SDG) 6.4—to substantially increase water use efficiency. This indicator, while useful at a global scale, is not comprehensive at a local scale. To fill this gap, this research proposes a CWP framework, that takes advantage of the spatio-temporal availability of remote sensing, that identifies CWP goals and sub-indicators specific to the needs of the targeted domain. Three sub-indicators are considered; (i) a global water productivity score (GWPS), (ii) a local water productivity score (LWPS) and (iii) a land and water use productivity score (YWPS). The GWPS places local CWP in the global context and focuses on maximised CWP. The LWPS differentiates yield zones, normalising for potential product, and focuses on minimising water consumption. The YWPS focuses simultaneously on improving land and water productivity equally. The CWP framework was applied to potato in the West Bank, Palestine. Three management practices were compared under each sub-indicator. The case study showed that fields with high and low performance were different under each sub-indicator. The performance associated with different management practices was also different under each sub-indicator. For example, a winter rotation had a higher performance under the YWPS, the fall rotation had a higher performance under the LWPS and under the GWPS there was little difference. The results showed, that depending on the basin goal, not only do the sub-indicators required change, but also the management practices or approach required to reach those basin goals. This highlights the importance of providing a CWP framework with multiple sub-indicators, suitable to basin needs, to ensure that meeting the SDG 6.4 goal does not jeopardise local objectives.peerReviewe