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

2022Journal article Open access
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dc.contributor.authorFerreira, Antónia
dc.contributor.authorRolim, João
dc.contributor.authorParedes, Paula
dc.contributor.authorCameira, Maria do Rosário
dc.date.accessioned2022-09-20T11:41:19Z
dc.date.available2022-09-20T11:41:19Z
dc.date.issued2022
dc.description.abstractExcess 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 schemept_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFerreira, 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, 2324pt_PT
dc.identifier.doihttps://doi.org/10.3390/w14152324pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.5/25544
dc.language.isoengpt_PT
dc.publisherMDPIpt_PT
dc.relationUID/AGR/04129/2020pt_PT
dc.relationLinking Landscape, Environment, Agriculture and Food
dc.relationNot Available
dc.relationNot Available
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectcrop coefficientpt_PT
dc.subjectirrigation water requirementspt_PT
dc.subjectirrigation schemept_PT
dc.subjectSentinel-2pt_PT
dc.subjectsoil water balance modelpt_PT
dc.subjectvegetation indicespt_PT
dc.titleAssessing Spatio-Temporal Dynamics of Deep Percolation Using Crop Evapotranspiration Derived from Earth Observations through Google Earth Enginept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLinking Landscape, Environment, Agriculture and Food
oaire.awardTitleNot Available
oaire.awardTitleNot Available
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FAGR%2F04129%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/DL 57%2F2016/DL 57%2F2016%2FCP1382%2FCT0021/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/DL 57%2F2016/DL 57%2F2016%2FCP1382%2FCT0022/PT
oaire.citation.titleWaterpt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamDL 57/2016
oaire.fundingStreamDL 57/2016
person.familyNameFORTES DUARTE FERREIRA
person.familyNameRolim Fernandes Machado Lopes
person.familyNameParedes
person.familyNamedo Rosário da Conceição Cameira
person.givenNameANTÓNIA
person.givenNameJoão Rui
person.givenNamePaula
person.givenNameMaria
person.identifier.ciencia-id5912-C5F4-3ED7
person.identifier.ciencia-idDB10-1A20-7D98
person.identifier.ciencia-idEC14-3F53-471D
person.identifier.ciencia-idB418-5A61-8B8B
person.identifier.orcid0000-0002-4778-4047
person.identifier.orcid0000-0003-1782-2732
person.identifier.orcid0000-0001-5609-0234
person.identifier.orcid0000-0002-2186-5172
person.identifier.ridF-9463-2010
person.identifier.scopus-author-id25923078000
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublicationb0cf5ac6-ba25-4c12-96a6-d85a2f57fa0b
relation.isAuthorOfPublication.latestForDiscovery8246248d-9151-4403-9fcc-72d61f999151
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