基于权重最优组合和多时相遥感的作物估产

多时相遥感数据比单一时相携带了更多的反映作物产量的信息，研究如何将多时相遥感信息进行有机融合以提高作物估产精度的方法是具有意义的。权重最优组合（WOC）是一种通过对单个模型权重的最优化，来构建高精度组合模型的原理方法。论文以黑龙江农垦友谊农场大麦产量遥感估算为例，首先利用大麦4个时相的Landsat5 TM影像分别构建单一时相的大麦产量模型，然后利用WOC的迭代算法，通过赋予4个单一时相产量模型以最优权重，生成基于多时相遥感的组合模型估算大麦产量，结果表明：基于WOC和多时相遥感的组合估产模型的决定系数R2与单一时相的相比得到较大改善，估算精度提高明显。同时，通过对WOC获取的各时相单一模型最优权重大小进行分析表明：应用多时相遥感数据进行作物估产时，权重大小能够反映各时相遥感数据所携带的产量信息的多少，这对于如何选择和确定能有效反映作物产量的敏感遥感时相具有一定的指导意义。

Estimation of crop yield based on weight optimization combination and multi-temporal remote sensing data

Multi-temporal remote sensing data can cover more information related with yield than that of single-temporal, so it is of great significance to explore how to integrate the useful information from multi-temporal remote sensing data for improving the precision of estimating yield. WOC (weight optimization combination) is the algorithm which optimizes the weights of many models to form the combined model with higher precision. Taking the estimation of barley yield as an example in friendship farm, Heilongjiang Province, firstly four different temporal Landsat5 TM images were used to construct the single-temporal estimating models of barley yield, then applying the iteration algorithm of WOC to calculate the weights of the four models formed the new combined model, which was employed to estimate the barley yield finally. The results showed that the combined model based on WOC and multi-temporal remote images displayed better performance, and it was R2 (determinant coefficient) was remarkably improved in comparison with those of the single-temporal models. In addition, analyzing the weight values in the combined model showed that the size of weight of each single model was sensitive to the amount of yield information involved by the corresponding temporal satellite image, and that was of great importance for determining the key temporal satellite images to estimate crop yield.