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Proceedings Paper

Improvements in irrigation system modelling when using remotely sensed ET for calibration
Author(s): J. D. van Opstal; C. M. U. Neale; S. Lecina
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Paper Abstract

Irrigation system modelling is often used to aid decision-makers in the agricultural sector. It gives insight on the consequences of potential management and infrastructure changes. However, simulating an irrigation district requires a considerable amount of input data to properly represent the system, which is not easily acquired or available. During the simulation process, several assumptions have to be made and the calibration is usually performed only with flow measurements. The advancement of estimating evapotranspiration (ET) using remote sensing is a welcome asset for irrigation system modelling. Remotely-sensed ET can be used to improve the model accuracy in simulating the water balance and the crop production. This study makes use of the Ador-Simulation irrigation system model, which simulates water flows in irrigation districts in both the canal infrastructure and on-field. ET is estimated using an energy balance model, namely SEBAL, which has been proven to function well for agricultural areas. The seasonal ET by the Ador model and the ET from SEBAL are compared. These results determine sub-command areas, which perform well under current assumptions or, conversely, areas that need re-evaluation of assumptions and a re-run of the model. Using a combined approach of the Ador irrigation system model and remote sensing outputs from SEBAL, gives great insights during the modelling process and can accelerate the process. Additionally cost-savings and time-savings are apparent due to the decrease in input data required for simulating large-scale irrigation areas.

Paper Details

Date Published: 29 October 2014
PDF: 11 pages
Proc. SPIE 9239, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI, 92390P (29 October 2014); doi: 10.1117/12.2067565
Show Author Affiliations
J. D. van Opstal, Utah State Univ. (United States)
C. M. U. Neale, Utah State Univ. (United States)
Univ. of Nebraska-Lincoln (United States)
S. Lecina, Ctr. de Investigación y Tecnología Agroalimentaria de Aragón (Spain)


Published in SPIE Proceedings Vol. 9239:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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