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Open AccessArticle

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

1
Centro de Edafología y Biología Aplicada del Segura (CEBAS), Consejo Superior de Investigaciones Científicas (CSIC), Espinardo, 30100 Murcia, Spain
2
Escola Politécnica Superior de Enxeñaría, Universidade de Santiago de Compostela, Campus de Lugo, 27002 Lugo, Spain
*
Author to whom correspondence should be addressed.
Water 2019, 11(1), 38; https://doi.org/10.3390/w11010038
Received: 6 October 2018 / Revised: 17 December 2018 / Accepted: 21 December 2018 / Published: 25 December 2018
(This article belongs to the Special Issue Water Management Using Drones and Satellites in Agriculture)
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 (Kcb) of lettuce and peach during an irrigation season with those tabulated as a reference for these crops. Good agreements between Kcb 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. View Full-Text
Keywords: agricultural modelling; ArcPy; crop water stress; Python; soil water balance agricultural modelling; ArcPy; crop water stress; Python; soil water balance
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Ramírez-Cuesta, J.M.; Mirás-Avalos, J.M.; Rubio-Asensio, J.S.; Intrigliolo, D.S. A Novel ArcGIS Toolbox for Estimating Crop Water Demands by Integrating the Dual Crop Coefficient Approach with Multi-Satellite Imagery. Water 2019, 11, 38.

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