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Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/DA006D4C-6A0E-4EED-A3D9-554339A76710 | ||
| Year | 2020 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
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| Bibliographic Citation | D.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-w https://doi.org/10.1007/s12145-019-00424-w | ||
| Appears in Collections |
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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