Show simple item record

contributor authorEdson Costa-Filho
contributor authorJosé L. Chávez
contributor authorAllan A. Andales
contributor authorAnsley J. Brown
date accessioned2024-12-24T10:31:10Z
date available2024-12-24T10:31:10Z
date copyright10/1/2024 12:00:00 AM
date issued2024
identifier otherJIDEDH.IRENG-10104.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4299070
description abstractSustainable irrigation water management is achievable only when irrigation scheduling is optimized to conserve water and soil resources in an agricultural setting. This study evaluated the use of remote sensing–based algorithms for determining actual crop evapotranspiration (ETa) mapping to update crop coefficients (kc) of an irrigation scheduler software (WISE). The scheduler’s kc values are based on the FAO-56 approach for crop evapotranspiration (ETc) determination. A surface-irrigated (furrow) maize (Zea mays L.) field in Fort Collins, Colorado, was used from July to September 2020 and 2021. An eddy covariance energy balance system (ECSEBS) installed on a tower at 3.5 m above the ground surface was used to determine hourly and daily maize ETa data. These EC-based ETa data were used to evaluate the performance of three approaches for maize ETa estimation and the FAO-56–based ETc predictions from WISE. Microsatellite PlanetScope multispectral imagery, at a 3-m-pixel spatial resolution, provided surface reflectance in the red and near-infrared bands for input in the remote sensing of ETa algorithms. On-site micrometeorological data were measured at the exact location of the ECSEBS tower. Optimization of kc values was done using an ordinary least-squares regression approach. The optimized kc values were calculated for the maize midseason growth stage. Results indicated that using remote sensing of ETa algorithms has excellent potential to improve irrigation scheduling by integrating optimized crop coefficients. WISE overestimated daily maize ETc predictions by as much as 26%. When remote sensing-based optimized kc values were introduced, the overestimation of daily maize ETc was reduced significantly, by 18% to 75%, depending on the remote sensing of the ETa algorithm used. The research findings support the combined use of remote sensing data and the FAO-56 approach for irrigation scheduling to improve agricultural water management at the farm level.
publisherAmerican Society of Civil Engineers
titleImproving WISE Crop Evapotranspiration Estimates Using Crop Coefficients Derived from Remote-Sensing Algorithms
typeJournal Article
journal volume150
journal issue5
journal titleJournal of Irrigation and Drainage Engineering
identifier doi10.1061/JIDEDH.IRENG-10104
journal fristpage04024022-1
journal lastpage04024022-18
page18
treeJournal of Irrigation and Drainage Engineering:;2024:;Volume ( 150 ):;issue: 005
contenttypeFulltext


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record