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Aerodynamic Parameterization of the Satellite-Based Energy Balance (METRIC) Model for ET Estimation in Rainfed Olive Orchards of Andalusia, Spain

Santos, C., Lorite, I. J., Allen, R. G., Tasumi, M.
Water resources management 2012 v.26 no.11 pp. 3267-3283
Food and Agriculture Organization, canopy, energy balance, evapotranspiration, field crops, irrigation scheduling, leaf area index, models, olives, orchards, remote sensing, roughness, soil water balance, trees, Spain
Sensitivity of estimates of evapotranspiration (ET) for olive fields located in Andalusia (Spain) to aerodynamic parameterization in METRIC (Mapping EvapoTranspiration with high Resolution and Internalized Calibration) was evaluated to better understand behavior of the model and spatial and temporal distribution of ET from olives, with the ultimate aim of designing customized irrigation schedules. Previous METRIC analyses have primarily focused on the estimation of ET over fields of annual crops, with few applications to complex canopies such as olive. The model was compared against FAO 56-soil water balance-based ET estimations for non-irrigated olive fields. In the first comparisons METRIC model used a general equation for momentum roughness length (zom) based on a fixed function of height estimated from LAI (Leaf Area Index) that underestimated the olives height and therefore zom, so that ET derived as a residual of the energy balance was overestimated (RMSE = 1.12 mm/day) compared to the soil water balance derived ET. The Perrier roughness function based on LAI and tree canopy architecture for sparse trees, coupled with improved olive height estimates (employing tree density and canopy shape factors) improved estimates for zom. This approach produced closer comparisons to rainfall-constrained ET estimates based on soil water balance for rainfed olive orchards (RMSE = 0.25 mm/day). This study does not attempt to validate METRIC results; instead utilizes a comparative approach between two independent methodologies to improve olive ET estimation via remote sensing, with the strong advantage, over the soil water balance approach, of improved spatial resolution over large areas.