基于信息扩散和关键期遥感数据的冬小麦估产模型

农作物估产对于国家制定粮食进出口政策和保障粮食安全具有重要意义。为构建高精度的作物估产模型，探讨了一种将信息扩散原理和关键期遥感数据相结合的农作物遥感估产方法。首先利用信息扩散原理将关键期遥感数据生成的NDVI和实割实测产量数据扩散到多维监控空间，采用模糊合成的方法建立关键期遥感数据和实割实测产量之间的离散关系模型。然后针对模型的稳定性和精度进行交叉验证，并与多元线性回归模型和BP神经网络模型进行对比。结果表明，利用信息扩散方法构建的遥感估产模型稳定性和精度都明显提高，与多元回归方法和BP神经网络方法相比，决定系数分别提高0.180、0.491，均方根误差分别降低173.10、487.79 kg/hm2。该方法能较好地模拟冬小麦遥感估产中归一化植被指数和产量之间的非线性关系，且泛化推广能力优异，为应用关键期遥感数据进行冬小麦估产提供了一种有效方法。

Winter wheat yield estimation model based on information diffusion and remote sensing data at major growth stages

Developing high accuracy models for crop yield estimation using remote sensing data is of great significance in decision making for national food policy and food security.Information diffusion methodology was introduced to construct yield estimation model with remote sensing data in the paper.Firstly,Remote sensing data at key stages and ground survey data were diffused into multi-dimensional control space and a fuzzy synthetic method was proposed to construct the relationship between remote sensing data an...