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Article

Assessing Spatio-Temporal Dynamics of Deep Percolation Using Crop Evapotranspiration Derived from Earth Observations through Google Earth Engine

1
Department of Biosystems Engineering, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisbon, Portugal
2
LEAF-Linking Landscape, Environment, Agriculture and Food-Research Center, Associated Laboratory TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisbon, Portugal
*
Author to whom correspondence should be addressed.
Academic Editors: Guido D’Urso and Daniel Kurtzman
Water 2022, 14(15), 2324; https://doi.org/10.3390/w14152324
Received: 21 June 2022 / Revised: 15 July 2022 / Accepted: 25 July 2022 / Published: 27 July 2022
Excess irrigation may result in deep percolation and nitrate transport to groundwater. Furthermore, under Mediterranean climate conditions, heavy winter rains often result in high deep percolation, requiring the separate identification of the two sources of deep percolated water. An integrated methodology was developed to estimate the spatio-temporal dynamics of deep percolation, with the actual crop evapotranspiration (ETc act) being derived from satellite images data and processed on the Google Earth Engine (GEE) platform. GEE allowed to extract time series of vegetation indices derived from Sentinel-2 enabling to define the actual crop coefficient (Kc act) curves based on the observed lengths of crop growth stages. The crop growth stage lengths were then used to feed the soil water balance model ISAREG, and the standard Kc values were derived from the literature; thus, allowing the estimation of irrigation water requirements and deep drainage for independent Homogeneous Units of Analysis (HUA) at the Irrigation Scheme. The HUA are defined according to crop, soil type, and irrigation system. The ISAREG model was previously validated for diverse crops at plot level showing a good accuracy using soil water measurements and farmers’ irrigation calendars. Results show that during the crop season, irrigation caused 11 ± 3% of the total deep percolation. When the hotspots associated with the irrigation events corresponded to soils with low suitability for irrigation, the cultivated crop had no influence. However, maize and spring vegetables stood out when the hotspots corresponded to soils with high suitability for irrigation. On average, during the off-season period, deep percolation averaged 54 ± 6% of the annual precipitation. The spatial aggregation into the Irrigation Scheme scale provided a method for earth-observation-based accounting of the irrigation water requirements, with interest for the water user’s association manager, and at the same time for the detection of water losses by deep percolation and of hotspots within the irrigation scheme. View Full-Text
Keywords: crop coefficient; irrigation water requirements; irrigation scheme; Sentinel-2; soil water balance model; vegetation indices crop coefficient; irrigation water requirements; irrigation scheme; Sentinel-2; soil water balance model; vegetation indices
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MDPI and ACS Style

Ferreira, A.; Rolim, J.; Paredes, P.; Cameira, M.d.R. Assessing Spatio-Temporal Dynamics of Deep Percolation Using Crop Evapotranspiration Derived from Earth Observations through Google Earth Engine. Water 2022, 14, 2324. https://doi.org/10.3390/w14152324

AMA Style

Ferreira A, Rolim J, Paredes P, Cameira MdR. Assessing Spatio-Temporal Dynamics of Deep Percolation Using Crop Evapotranspiration Derived from Earth Observations through Google Earth Engine. Water. 2022; 14(15):2324. https://doi.org/10.3390/w14152324

Chicago/Turabian Style

Ferreira, Antónia, João Rolim, Paula Paredes, and Maria do Rosário Cameira. 2022. "Assessing Spatio-Temporal Dynamics of Deep Percolation Using Crop Evapotranspiration Derived from Earth Observations through Google Earth Engine" Water 14, no. 15: 2324. https://doi.org/10.3390/w14152324

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