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遥感技术与应用  2015, Vol. 30 Issue (2): 209-219    DOI: 10.11873/j.issn.1004-0323.2015.2.0209
综述     
基于作物生长模型和多源数据的融合技术研究进展
王静1,李新2
(1.中国气象局兰州干旱气象研究所,甘肃省干旱气候变化与减灾重点实验室,
中国气象局干旱气候变化与减灾重点开放实验室,甘肃 兰州730020;
2.中国科学院寒区旱区环境与工程研究所,甘肃 兰州730000)
Progresses on Data Fusion Technology of Crop Growth Model and Multi-source Observation Information
Wang Jing1,Li Xin2
(1.Lanzhou Institute of Arid Meteorology,China Meteorological Administration,Key Laboratory of
Arid Climatic Change and Reducing Disaster of Gansu Province,Key Open Laboratory of Arid Climate
Change and Disaster Reduction of China Meteorological Administration,Lanzhou 730020,China;
2.Cold and Arid Regions Environmental and Engineering Research Institute,
Chinese Academy of Sciences,Lanzhou 730000,China)
 全文: PDF(1286 KB)  
摘要:

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。

关键词: 作物生长模型优化方法顺序数据同化模型-数据融合    
Abstract:

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.

Key words: Crop growth model    Optimization method    Sequential data assimilation method    Model-data fusion
 
收稿日期: 2013-05-14 出版日期: 2015-05-08
:  TP 722.6  
基金资助:

国家自然科学基金项目“基于遥感信息和作物生长模型同化的作物碳循环研究”(41101422),中国科学院西部行动计划项目“黑河流域生态—水文遥感产品生产算法研究与应用试验”(KZCX2-XB3-15)。

作者简介: 王静(1978-),女,陕西汉中人,副研究员,主要从事灾害风险评估研究。Email:wangjing1102@126.com。
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王静,李新. 基于作物生长模型和多源数据的融合技术研究进展[J]. 遥感技术与应用, 2015, 30(2): 209-219.

Wang Jing,Li Xin. Progresses on Data Fusion Technology of Crop Growth Model and Multi-source Observation Information. Remote Sensing Technology and Application, 2015, 30(2): 209-219.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2015.2.0209        http://www.rsta.ac.cn/CN/Y2015/V30/I2/209

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