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

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Abstract

In 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.

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Acknowledgments

The authors would like to thank to the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES-Brasil) for the scholarship given to Daniela F. Silva Fuzzo and the research financial support. GPP’s contribution has been financially supported by the FP7- People project ENViSIoN-EO (project reference number 752094) and the author gratefully acknowledges the financial support provided by the European Commission. All authors are grateful to the anonymous reviewers for their comments that resulted to improving the manuscript.

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Authors and Affiliations

  1. Paulista State University “Julio de Mesquita Filho”, Department of Geography, Renato da Costa Lima, 451 - Ville de France, 19903-302, Ourinhos, SP, Brazil

    Daniela F. Silva Fuzzo

  2. PennState University, 604 Walker Building, University Park, PA, 16802, USA

    Toby N. Carlson

  3. Institute for Olive Tree Subtropical Crops and Viticulture, Water Recourses-Irrigation & Env. Geoinformatics Lab., NAGREF - Hellenic Agricultural Organization (H.A.O.-DEMETER), Chania, Greece

    Nektarios N. Kourgialas

  4. Department of Soil Water Resources, Institute of Industrial & Forage Crops, Hellenic Agricultural Organization (HAO) “Demeter” (former NAGREF), Directorate General of Agricultural Research, 1 Theofrastou St., 41335, Larisa, Greece

    George P. Petropoulos

  5. School of Mineral Resources Engineering, Technical University of Crete, 73100, Chania, Crete, Greece

    George P. Petropoulos

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  1. Daniela F. Silva Fuzzo
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  2. Toby N. Carlson
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  3. Nektarios N. Kourgialas
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  4. George P. Petropoulos
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Correspondence to Daniela F. Silva Fuzzo.

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Communicated by: H. Babaie

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Silva Fuzzo, D.F., Carlson, T.N., Kourgialas, N.N. et al. Coupling remote sensing with a water balance model for soybean yield predictions over large areas. Earth Sci Inform 13, 345–359 (2020). https://doi.org/10.1007/s12145-019-00424-w

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