Improvement in estimation of soil water deficit by integrating airborne imagery data into a soil water balance model

Abstract: In this study, an approach that integrates airborne imagery data as inputs was used to improve the estimation of soil water deficit (SWD) for maize and sunflower grown under full and deficit irrigation treatments. The proposed model was applied to optimize the maximum total available soil water (TAWr) by minimizing the difference between a water stress coefficient ks and crop water stress index (1-CWSI). The optimal value of maximum TAWr was then used to calibrate a soil water balance model which in turn updated the estimation of soil water deficit. The estimates of SWD in the soil profile of both irrigated maize and sunflower fields were evaluated with the crop root zone SWD derived from neutron probe measurements and the FAO-56 SWD procedure. The results indicated a good agreement between the estimated SWD from the proposed approach and measured SWD for both maize and sunflower. The statistical analyses indicated that the maximum TAWr estimated from CWSI significantly improved the estimates of SWD, which reduced the mean absolute error (MAE) and root mean square error (RMSE) by 40% and 44% for maize and 22% for sunflower, compared with the FAO-56 model. The proposed procedure works better for crops under deficit irrigation condition. With the availability of higher spatial and temporal resolution airborne imagery during the growing season, the optimization procedure can be further improved.