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Estimating the water budget components of irrigated crops: Combining the FAO-56 dual crop coefficient with surface temperature and vegetation index data

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Olivera-Guerra, Luis Enrique | Merlin, Olivier | Er-Raki, Salah | Khabba, Said | Escorihuela, Maria-José

Edité par CCSD ; Elsevier Masson -

International audience. The FAO-56 dual crop coefficient (FAO-2Kc) model has been extensively used at the field scale to estimate the crop water requirements by means of the simulated evapotranspiration (ET) and its two components evaporation (E) and transpiration (T). Given that the main limitation of FAO-2Kc for operational irrigation management over large areas is the unavailability (over most irrigated areas) of irrigation data, this study investigates the feasibility 1) to constrain the FAO-2Kc ET from LST and VI data, 2) to retrieve irrigation amounts and dates from LST and VI data and 3) to estimate the root-zone soil moisture (RZSM) at the daily scale. In practice, the vegetation and soil temperatures retrieved from LST/VI data are used to estimate the FAO-2Kc vegetation stress coefficient (Ks) and soil evaporation reduction coefficient (Kr), respectively. The modeling and remote sensing combined approach is tested over a wheat crop field in central Morocco, and results are evaluated in terms of ET, irrigation and RZSM estimates. ET is estimated with a RMSE of 0.68 mm day-1 compared to 0.84 mm day-1 for the standard (without using LST data) FAO-2Kc based on tabulated values for the parameters. The total irrigation depth (67 mm) is correctly estimated and is very close to the actual effective irrigation (69.8 mm) applied by the farmer. Daily RZSM is estimated with an R2 value of 0.68 (0.42) and a RMSE value of 0.034 (0.061) m3 m-3 by forcing FAO-2Kc using the retrieved irrigation (from LST-derived estimates and precipitation only). Since spaceborne LST data are currently not available at both high-spatial and high-temporal resolution, a sensitivity analysis is finally undertaken to assess the potential and applicability of the proposed methodology to temporally sparse thermal data.

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