基于作物生长模型和多源数据的融合技术研究进展

精确的区域作物产量估计在社会食品安全生产中起着重要作用。首先讨论了常用的2种区域作物产量估计方法,包括产量监测和产量模拟。其中,作物生长模型能够反映作物的整个生长演变过程,并能最终预报作物产量,因此在区域作物产量预报中起着重要作用,但是由于作物生长模型在输入数据、模型参数和模型结构等方面存在较大的不确定性,导致最终的模拟结果也存在较大的不确定性,尤其是应用到区域尺度时,这种不确定性使得模拟结果同真实的作物产量空间分布图存在较大的不一致性。而产量监测,尤其是利用先进的多源遥感信息,可捕捉真实的区域尺度的地面作物生长信息,但是仅为瞬时信息。因此利用数据融合算法,融合模型和数据的优点,得到更为可靠的区域产量估计结果是十分有意义的。所以在详述了当前主要的作物生长模型的基础上,重点讨论了常用的2种数据融合技术,即优化方法和顺序数据同化方法,以及目前利用这两种方法在作物生长模型中融合观测信息的部分案例。

Progresses on Data Fusion Technology of Crop Growth Model and Multi-source Observation Information

Accurate yield estimation of crop in region scale plays an important role in food safety.In this paper,two methods on regional yield estimation are firstly discussed,that is yield monitoring and yield simulation.Crop growth model can reflect the evolution process of crop growth,and ultimately predicts crop yield.However,due to the great uncertainties of input,parameters and model structure,the final simulation results are fairly uncertainty,especially applied to the regional scale.Yield monitoring,especially the application of multi\|source remote sensing data,can capture the true information of crop growth.But these information is instantaneous.So by using data fusion algorithm,more reliable regional yield estimation results are very useful.So after the discussion on the current major crop growth models,two common data fusion techniques,namely the optimization method and the sequential data assimilation method,were described.Some examples on the use of two methods were discussed.