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.

中文翻译：

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结合遥感与水平衡模型预测大面积大豆单产

在这项研究中，提出了一种在较大空间尺度上预测大豆产量的新方法，克服了可扩展性的难题。该方法基于所谓的“简化三角”遥感技术，该技术与Doorenbos and Kassam 1979（DK）的作物预测模型以及Thornthwaite and Mather 1955（ThM）的气候水平衡模型相结合。在该方法中，地表土壤含水量（Mo），蒸散量（ET）和蒸发分数（EF）来源于卫星衍生的表面辐射温度（Ts）和归一化植被指数（NDVI）。在巴西的Paraná州，从2002-03到2011-12的作物年度证明了该方法的使用。DK的大豆农作物产量模型使用通过简化三角形获得的远程估计EF值进行评估。通过卫星测量以及热带雨量测量任务数据（TRMM）和欧洲中距离天气预报中心（ECMWF）的数据预测的作物产量与所测得的作物产量高度吻合。广告据报道₂英寸指数（改良的Willmott）在0.8到0.98之间，RMSE在30.8（kg / ha）到57.2（kg / ha）之间。与从实测大豆作物数据中比较，使用从归档表面数据获得的等效EF值，使用三角形的EF预测的作物产量在统计学上优于DK和ThM。除了两个卫星图像外，所提出的方法不需要辅助的气象或地表数据。这使得该技术易于应用，可以提供大面积和不同空间规模的作物产量的时空估计，而几乎不需要或不需要地面数据。