Estimating Evapotranspiration of Rainfed Winegrapes Combining Remote Sensing and the SIMDualKc Soil Water Balance Model

Soil water balance (SWB) in woody crops is sometimes difficult to estimate with one-dimensional models because these crops do not completely cover the soil and usually have a deep root system, particularly when cropped under rainfed conditions in a Mediterranean climate. In this study, the actual crop evapotranspiration (ETc act) is estimated with the soil water balance model SIMDualKc which uses the dual-Kc approach (relating the fraction of soil cover with the crop coefficients) to improve the estimation of the water requirements of a rainfed vineyard, using data from a deep soil profile. The actual basal crop coefficient (Kcb act) obtained using the SIMDualKc model was compared with the Kcb act estimated using the A&P approach, which is a simplified approach based on measurements of the fraction of ground cover and crop height. Spectral vegetation indices (VIs) derived from Landsat-5 satellite data were used to determine the fraction of ground cover (fc VI) and thus the density coefficient (Kd). The SIMDualKc model was calibrated using available soil water (ASW) measurements down to a depth of 1.85 m, which significantly improved the conditions for using an SWB estimation model. The test of the model was performed using a different ASW dataset. A good agreement between simulated and field-measured ASW was observed for both data sets along the crop season, with RMSE < 12.0 mm and NRMSE < 13%. The calibrated Kcb values were 0.15, 0.60, and 0.52 for the initial, mid-season, and end season, respectively. The ratio between ETc act and crop evapotranspiration (ETc) was quite low between veraison and maturity (mid-season), corresponding to 36%, indicating that the rainfall was not sufficient to satisfy the vineyard’s water requirements. VIs used to compute fc VI were unable to fully track the plants’ conditions during water stress. However, ingestion of data from remote sensing (RS) showed promising results that could be used to support decision making in irrigation scheduling. Further studies on the use of the A&P approach using RS data are required.