Agronomy (Feb 2021)

Evaluation of SEBS, METRIC-EEFlux, and QWaterModel Actual Evapotranspiration for a Mediterranean Cropping System in Southern Italy

  • Zaibun Nisa,
  • Muhammad Sarfraz Khan,
  • Ajit Govind,
  • Marco Marchetti,
  • Bruno Lasserre,
  • Enzo Magliulo,
  • Antonio Manco

DOI
https://doi.org/10.3390/agronomy11020345
Journal volume & issue
Vol. 11, no. 2
p. 345

Abstract

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Remote sensing-based evapotranspiration (ET) models with various levels of sophistication have emerged recently with the possibilities of user-defined model calibrations. Their application for water resources management and climate studies from regional to global scale has been rapidly increasing, which makes it important to validate field scale ET in a complex crop assemblage before operational use. Based on in situ flux-tower measurements by the eddy-covariance (EC) system, this study tested three single-source energy balance models for estimating daily ET from fennel/maize/ryegrass-clover cropland rotations in a Mediterranean context in southern Italy. The sensitivity of three user-friendly ET models (SEBS, QWaterModel, and METRIC-EEFlux) with reference to the EC system over a center pivot irrigated cropland is discussed in detail. Results in terms of statistical indicators revealed that SEBS and METRIC-EEFlux showed reasonable agreements with measured ET (r2 = 0.59SEBS, RMSE = 0.71 mm day−1; r2 = 0.65METRIC, RMSE = 1.13 mm day−1) in terms of trends and magnitudes. At 30 m spatial resolution, both models were able to capture the in-field variations only during the maize development stage. The presence of spurious scan lines due to sensor defects in Landsat L7 ETM+ can contribute to the qualities of the METRIC-Efflux’s ET product. In our observation, the QWaterModel did not perform well and showed the weakest congruency (r2 = 0.08QWaterModel) with ground-based ET estimates. In a nutshell, the study evaluated these automated remote sensing-based ET estimations and suggested improvements in the context of a generic approach used in their underlying algorithm for robust ET retrievals in rotational cropland ecosystems.

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