A highly accurate model for crop yield estimation was developed by using the entropy combination forecasting method. Firstly, the single-temporal remotely sensed Landsat TM/ETM + images at main growth and development stages of spring maize in 2007 and 2008 were used to construct the single-temporal yield estimation models. Secondly, the weights of the single-temporal estimation models were calculated by applying the entropy methods. And then, a combination forecasting model was developed. Finally, the two models were compared. The results showed that the yield estimation model based on combination forecasting and multi-temporal remote images could increase the precision of the yield estimation model based on single-temporal remote images, and the correlation coefficient was remarkably improved in comparison with those of the single-temporal models. They were increased by 0. 137 and 0. 121 respectively. The values of weights in the combined forecasting showed that the sensitive degree was displayed between main growing stages and maize yield, and that was of great importance for some key aspects; (1) looking for the main limiting factor of maize growth; (2) raising maize yield. Therefore, it is feasible and effective to estimate spring maize yield based on the combined forecasting of entropy method and multi-temporal remotely sensed data.%利用基于熵值的组合预测方法构建高精度遥感估产模型,对黑龙江军川农场2007年和2008年春玉米的主要生育期多时相Landsat TM/ETM+影像数据分别建立单一时相的估产模型,通过信息熵赋予各个时相估产模型的权系数,构建组合估产模型,然后对组合估产模型和单一时相估产模型进行对比分析.结果表明:基于熵值的组合估产模型能够有效提高估产精度,与最佳的单时相遥感估产模型相比,2007年和2008年的组合估产模型的相关系数绝对值分别提高了0.137和0.121;根据组合估产模型的权系数大小,能够获得限制玉米产量的主要生态障碍因素和提高玉米产量的方法.因此,基于熵值组合预测和多时相遥感构建估产模型用于春玉米估产是有效、可行的.
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