Location: Water Management and Systems ResearchTitle: Improvement in estimation of soil water deficit by integrating airborne imagery data into a soil water balance modelents into a soil water
|HAN, MING - Colorad0 State University
|CHAVEZ, JOSE - Colorado State University
|LAN, YUBIN - South China Agricultural University
Submitted to: International Journal of Agricultural and Biological Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/20/2017
Publication Date: 5/20/2017
Citation: Zhang, H., Han, M., Chavez, J.L., Lan, Y. 2017. Improvement in estimation of soil water deficit by integrating airborne imagery data into a soil water balance modelents into a soil water. International Journal of Agricultural and Biological Engineering. 10:37-46.
Interpretive Summary: We proposed an approach to improve the performance of the FAO-56 soil water balance model using airborne remote sensing data. Field experiments were conducted in maize and sunflower plots with different levels of full and regulated deficit irrigation during the 2015 growing season. The proposed model was applied to optimize the maximum total available soil water in the root zone by comparing water stress coefficient (ks) and crop water stress index that derived from airborne imagery. The method improved the estimation of soil water deficit and worked better for crops under deficit irrigation condition.
Technical Abstract: In this paper, 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 corn 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.