Grant support

This research was funded in the frame of the collaborative international consortium IRIDA financed under the ERA-NET Cofund Water-Works 2014 Call with Spanish national funds from the Agencia Estatal de Investigaci6n grant PCIN-2015-263. This ERA-NET is an integral part of the 2015 Joint Activities developed by the Water Challenges for a Changing World Joint Programme Initiative (Water JPI) JPI Water WaterWorks 2014. Additional funding for the field study validations were obtained from the AEI-Feder financing project RiegoAsesor (RTC-2015-3453-2).

Analysis of institutional authors
Miras ávalos, José ManuelAuthor or co-author of article in journal with external admissions assessment committee
Publicaciones > Article

A Novel ArcGIS Toolbox for Estimating Crop Water Demands by Integrating the Dual Crop Coefficient Approach with Multi-Satellite Imagery

Publicated to:Water. 11 (1): - 2018-12-25 11(1), doi: 10.3390/w11010038

Miras ávalos, José Manuel;


Centro de Investigación y Tecnología Agroalimentaria de Aragón - Source entity


Summary: Advances in information and communication technologies facilitate the application of complex models for optimizing agricultural water management. This paper presents an easy-to-use tool for determining crop water demands using the dual crop coefficient approach and remote sensing imagery. The model was developed using Python as a programming language and integrated into an ArcGIS (geographic information system) toolbox. Inputs consist of images from satellites Landsat 7 and 8, and Sentinel 2A, along with data for defining crop, weather, soil type, and irrigation system. The tool produces a spatial distribution map of the crop evapotranspiration estimates, assuming no water stress, which allows quantifying the water demand and its variability within an agricultural field with a spatial resolution of either 10 m (for Sentinel) or 30 m (for Landsat). The model was validated by comparing the estimated basal crop coefficients (K-cb) of lettuce and peach during an irrigation season with those tabulated as a reference for these crops. Good agreements between K-cb derived from both methods were obtained with a root mean squared error ranging from 0.01 to 0.02 for both crops, although certain underestimations were observed resulting from the uneven crop development in the field (percent bias of -4.74% and -1.80% for lettuce and peach, respectively). The developed tool can be incorporated into commercial decision support systems for irrigation scheduling and other applications that account for the water balance in agro-ecosystems. This tool is freely available upon request to the corresponding author.

Keywords: Agricultural modelling; Arcpy; Conservation; Crop water stress; Decision-support-system; Energy-balance; Evapotranspiration; Fao-56; Irrigation; Lettuce crops; Model; Python; Soil; Soil water balance; Yield

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