Coupling remote sensing with a water balance model for soybean yield predictions over large areas

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dc.contributor.authorSilva Fuzzo, Daniela F. [UNESP]
dc.contributor.authorCarlson, Toby N.
dc.contributor.authorKourgialas, Nektarios N.
dc.contributor.authorPetropoulos, George P.
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionPennState Univ
dc.contributor.institutionNAGREF Hellen Agr Org HAO DEMETER
dc.contributor.institutionHellen Agr Org HAO Demeter
dc.contributor.institutionNAGREF
dc.contributor.institutionTech Univ Crete
dc.contributor.institutionHarokopio Univ Athens
dc.date.accessioned2020-12-10T19:44:34Z
dc.date.available2020-12-10T19:44:34Z
dc.date.issued2019-12-20
dc.description.abstractIn this study a new method for predicting soybean yield over large spatial scales, overcoming the difficulties of scalability, is proposed. The method is based on the so-called simplified triangle remote sensing technique which is coupled with a crop prediction model of Doorenbos and Kassam 1979 (DK) and the climatological water balance model of Thornthwaite and Mather 1955 (ThM). In the method, surface soil water content (Mo), evapotranspiration (ET), and evaporative fraction (EF) are derived from satellite-derived surface radiant temperature (Ts) and normalized difference vegetation index (NDVI). Use of the proposed method is demonstrated in Brazil's Parana state for crop years 2002-03 to 2011-12. The soybean crop yield model of DK is evaluated using remotely estimated EF values obtained by a simplified triangle. Predicted crop yield by the satellite measurements and from archived Tropical Rainfall Measuring Mission data (TRMM) and European Centre for Medium-Range Weather Forecasts (ECMWF) data were in good agreement with the measured crop yield. A d(2) index (modified Willmott) between 0.8 and 0.98 and RMSE between 30.8 (kg/ha) to 57.2 (kg/ha) was reported. Crop yield predicted using EF from the triangle were statistically better than the DK and ThM using values of the equivalent of EF obtained from archived surface data when compared with the measured soybean crop data. The proposed method requires no ancillary meteorological or surface data apart from the two satellite images. This makes the technique easy to apply allowing providing spatiotemporal estimates of crop yield in large areas and at different spatial scales requiring little or no surface data.en
dc.description.affiliationPaulista State Univ Julio de Mesquita Filho, Dept Geog, Renato Costa Lima 451, BR-19903302 Ourinhos, SP, Brazil
dc.description.affiliationPennState Univ, 604 Walker Bldg, University Pk, PA 16802 USA
dc.description.affiliationNAGREF Hellen Agr Org HAO DEMETER, Water Recourses Irrigat & Env Geoinformat Lab, Inst Olive Tree Subtrop Crops & Viticulture, Khania, Greece
dc.description.affiliationHellen Agr Org HAO Demeter, Dept Soil Water Resources, Inst Ind & Forage Crops, Directorate Gen Agr Res, 1 Theofrastou St, Larisa 41335, Greece
dc.description.affiliationNAGREF, 1 Theofrastou St, Larisa 41335, Greece
dc.description.affiliationTech Univ Crete, Sch Mineral Resources Engn, Khania 73100, Crete, Greece
dc.description.affiliationHarokopio Univ Athens, Dept Geog, El Venizelou 70, Athens 17671, Greece
dc.description.affiliationUnespPaulista State Univ Julio de Mesquita Filho, Dept Geog, Renato Costa Lima 451, BR-19903302 Ourinhos, SP, Brazil
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipFP7-People project ENViSIoN-EO
dc.description.sponsorshipEuropean Commission
dc.description.sponsorshipIdFP7-People project ENViSIoN-EO: 752094
dc.format.extent345-359
dc.identifierhttp://dx.doi.org/10.1007/s12145-019-00424-w
dc.identifier.citationEarth Science Informatics. Heidelberg: Springer Heidelberg, v. 13, n. 2, p. 345-359, 2020.
dc.identifier.doi10.1007/s12145-019-00424-w
dc.identifier.issn1865-0473
dc.identifier.urihttp://hdl.handle.net/11449/196425
dc.identifier.wosWOS:000503667100002
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofEarth Science Informatics
dc.sourceWeb of Science
dc.subjectSoybean yield modeling
dc.subjectSatellite measurements
dc.subjectRemote sensing
dc.subjectEvapotranspiration
dc.subjectCrop yield in large areas
dc.subjectTriangle method
dc.subjectGeospatial data analysis techniques
dc.titleCoupling remote sensing with a water balance model for soybean yield predictions over large areasen
dc.typeArtigo
dcterms.licensehttp://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
dcterms.rightsHolderSpringer

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