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陈浩,樊风雷.基于集合卡尔曼滤波的南雄烟草LAI数据同化研究.生态学报,2017,37(9):3046~3054 本文二维码信息
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基于集合卡尔曼滤波的南雄烟草LAI数据同化研究
Data assimilation for leaf area index of tobacco on the basis of the ensemble Kalman filter in Nanxiong
投稿时间:2016-01-20  最后修改时间:2016-10-17
DOI: 10.5846/stxb201601200135
关键词数据同化  烟草  集合卡尔曼滤波  叶面积指数  数据预测
Key Wordsdata assimilation  tobacco  ensemble Kalman filter  LAI (Leaf area index)  data prediction
基金项目国家自然科学基金资助项目(41201432);广东省烟草专卖局科技资助项目(粤烟科(2012)26,合同号:201203)
作者单位E-mail
陈浩 华南师范大学地理科学学院, 广州 510631  
樊风雷 华南师范大学地理科学学院, 广州 510631 fanfenglei@gig.ac.cn 
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摘要:
叶面积指数(LAI)是表征烟草生长健康状态的重要指标之一,获取准确的LAI数据是监测烟草生长走势的重要步骤。以广东省南雄地区为试验区开展了集合卡尔曼滤波同化方法在烟草LAI的应用研究。通过野外实测得到南雄烟草生长期内的高光谱数据,并计算每个生长期的归一化植被指数(NDVI),依据NDVI值获得LAI测量数据;通过积温数据和实测LAI数据构建了符合南雄地区烟草LAI变化规律的LOGISTIC模型;并以LAI为研究变量,利用集合卡尔曼滤波数据同化技术融合NDVI数据计算得到的LAI和简化LOGSITIC模型拟合得到的LAI这两种不同的数据信息,获取实验区烟草生长期时间序列上的连续LAI数据。最后,进一步对比了数据同化方法、NDVI计算LAI方法和LOGISTIC模型拟合这3种方法获取烟草LAI的效果。结果显示:数据同化方法、NDVI计算LAI方法和LOGISTIC模型拟合3种方法均可一定程度上表征烟草LAI的变化状态,其中数据同化方法拟合效果最优。实验发现NDVI计算LAI方法在烟草生长前后期LAI值出现偏大或偏小的异常情况;LOGISTIC模型拟合则不能有效的描述烟草LAI的突发性变化;同化方法综合作物生长模型和遥感监测的优势,能够动态调节参数得到LAI优化结果,同化后LAI结果和真实值吻合,变化曲线更符合烟草的实际生长状况。
Abstract:
Leaf area index (LAI) can be used as a monitoring index for assessing tobacco health in different growing periods. Hence, acquiring and updating accurate LAI data in a timely manner are necessary for managing the growth of tobacco. Growth information for tobacco in different seasons could provide valuable information for management on a national scale. Accurate and continuous tobacco LAI dynamics data are based on the data fusion framework of the ensemble Kalman filter (EnKF), which is an efficient recursive filter to estimate the state of a dynamic system from a series of incomplete and noise measurements, can be used to obtain optimal results. Nanxiong City in Guangdong Province was selected as the study area to extract LAI data for tobacco and test the effect of EnKF method on the basis of quantitative remote sensing data for the growing state of tobacco in 2014. Tobacco canopy hyperspectral reflectance data in different growing seasons were collected every 15 days by using AvaSpec-ULS2048 HandHeld spectroradiometer made by Avantes company in the Netherlands. Tobacco LAI data were retrieved using the Normalized Difference Vegetation Index (NDVI), which was calculated using the reflectance data. An improved tobacco growth model (LOGISTIC) was established using the LAI data collected around Nanxiong. This improved model used LAI and accumulated temperature to reveal the changes in LAI in different growing seasons. On the basis of integration of LAI data (obtained using remote sensing data) and LAI data (obtained using the simplified LOGISTIC model and EnKF method), continuous LAI data were obtained in the time series during the tobacco growing season in Nanxiong. Finally, we compared three different LAI computing methods in tobacco study: (a) calculated by NDVI, (b) simulated by the LOGISTIC model and (c) data assimilation was based on EnKF. The results indicated that these three methods could describe the growth status of tobacco to a certain extent, especially in the mature growth period, however, the LAI assimilation method was the best, which was able to adjust measured values and model values dynamically, LAI data were more consistent with the practical growth conditions of tobacco. Method (a) was imperfect at early and late growth seasons of tobacco (in these two seasons, the LAI data were either less or more), and method (b) was more dependent on accumulated temperature data (LOGISTIC model could not effectively describe the unexpected changes in tobacco LAI). The results showed that the EnKF algorithm could obtain better estimation on the basis of the dynamic model, and assimilate remote sensing data into the dynamic model to obtain optimal estimation for LAI. The assimilated LAI data were closer to the real values, and the LAI curve was more consistent with actual tobacco growth status.
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