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

Silva-Fuzzo Daniela Fernanda, Carlson Toby Nahum, Kourgialas Nektarios N., Petropoulos Georgios

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URIhttp://purl.tuc.gr/dl/dias/DA006D4C-6A0E-4EED-A3D9-554339A76710-
Identifierhttps://doi.org/10.1007/s12145-019-00424-w-
Identifierhttps://link.springer.com/article/10.1007%2Fs12145-019-00424-w-
Languageen-
Extent15 pagesen
TitleCoupling remote sensing with a water balance model for soybean yield predictions over large areasen
CreatorSilva-Fuzzo Daniela Fernandaen
CreatorCarlson Toby Nahumen
CreatorKourgialas Nektarios N.en
CreatorPetropoulos Georgiosen
CreatorΠετροπουλος Γεωργιοςel
PublisherSpringer Natureen
Content SummaryIn 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 Paraná 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 “d2” 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
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2020-11-03-
Date of Publication2020-
SubjectCrop yield in large areasen
SubjectEvapotranspirationen
SubjectGeospatial data analysis techniquesen
SubjectRemote sensingen
SubjectSatellite measurementsen
SubjectSoybean yield modelingen
SubjectTriangle methoden
Bibliographic CitationD.F. Silva Fuzzo, T.N. Carlson, N.N. Kourgialas, G.P. Petropoulos, "Coupling remote sensing with a water balance model for soybean yield predictions over large areas," Earth Sci. Inform., vol. 13, no. 2, pp. 345-359, Jun. 2020. doi: 10.1007/s12145-019-00424-wen

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