|Anderson, Raymond - Ray|
|SABER, MAZIN - University Of Arizona|
|SANCHEZ, CHARLES - University Of Arizona|
|SCUDIERO, ELIA - University Of California|
Submitted to: Irrigation Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/12/2023
Publication Date: 1/24/2023
Citation: Dhungel, R., Anderson, R.G., French, A.N., Skaggs, T.H., Saber, M., Sanchez, C.A., Scudiero, E. 2023. Remote sensing-based energy balance for lettuce in an arid environment: Influence of management scenarios on irrigation and evapotranspiration modeling. Irrigation Science. 41(2):197-214. https://doi.org/10.1007/s00271-023-00848-9.
Interpretive Summary: Precise, field-scale estimation of irrigation and subsequent evapotranspiration (ET) of winter vegetables is necessary for efficient irrigation management. Remote sensing based ET models are playing a vital role in understanding the consumptive water use of crops. However, the majority of operational remote sensing ET models lack the direct methods to incorporate soil water balance components in energy balance, which is critical for understanding irrigation management. In this study, we utilized remote sensing-based ET model (BAITSSS) that accommodates water balance components and allows to simulate irrigation and ET based on multiple management scenarios, i.e., range of management allowed depletion (MAD). The study was conducted in 12 eddy covariance (EC) fields over multiple seasons (2016-2020) and soil moisture characteristics at the Lower Colorado River Basin, in particular, the Yuma Valley of Arizona). Results showed MAD based model simulated irrigation and applied irrigation in the field closely agreed with some exceptions. The BAITSSS simulated ET was also comparable to EC measured ET, indicating the model’s competency. Overall, this study helped to understand current field practices of irrigation and how a model like BAITSSS can improve farmers’ and irrigatiors’ future water management of drought-impacted arid farmlands. The study results will be important for scientist and hydrologists seeking better ways to manage irrigation water supplies.
Technical Abstract: Efficient irrigation is critical for managing scarce water resources where precipitation is minimal. Field-scale irrigation is largely unaccounted for in landscape evapotranspiration models, primarily due to the unavailability of data and the lack of water balance components in energy balance-based evapotranspiration models. To overcome these challenges, we implemented a remote sensing-based energy and water balance model BAITSSS (Backward-Averaged Iterative Two-Source Surface temperature Solution) to calculate evapotranspiration (ET) and irrigation requirements of winter lettuce in the arid environment of the Lower Colorado River Basin. Predicted evapotranspiration and irrigation were compared against data from twelve eddy covariance (EC) sites for wide range of soil hydraulic properties operating between 2016 and 2020 and the applied irrigation, respectively. BAITSSS estimated evapotranspiration and irrigation based on vegetative formation, weather demand, soil hydraulic characteristics, and predefined management allowed depletion (MAD) (0.4–0.6). Ground-based weather data, Sentinel-2-based vegetation indices, and SSURGO (NRCS soil survey database) soil moisture characteristics were model inputs. The results showed mean seasonal ET from BAITSSS and EC were comparable, differing on average by about 7% based on a constant rooting depth (500 mm) and MAD of 0.5 for entire crop growth stages. Variations in daily and seasonal ET were mainly due to differences in applied and model-simulated irrigation. Seasonal values of applied and simulated irrigation closely agreed (~'6%) in most sites, though some sites applied irrigation more effectively than others. Overall, this study provided insight into consumptive water use and field-scale irrigation practices, as well as the capabilities and limitations of model-simulated ET and irrigation.