| 基于数据同化的太湖叶绿素多模型协同反演 |
| 摘要点击 2571 全文点击 1244 投稿时间：2014-02-28 修订日期：2014-04-16 |
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| 中文关键词 高光谱数据 叶绿素 数据同化 太湖 多模型协同反演 |
| 英文关键词 hyperspectral data chlorophyll a data assimilation Taihu Lake multi-model collaborative retrieval algorithm |
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| 中文摘要 |
| 在国内外众多学者的不懈努力下，开发了大量的水质参数遥感估算反演模型，但不同的模型都具有其“局限性”，只能从某个层面反映“真值”. 基于上述考虑，本研究发展了基于数据同化方法的太湖叶绿素a浓度多模型协同反演算法. 利用2006~2009年太湖野外实测水体高光谱遥感反射率数据，构建了7个叶绿素a浓度反演模型；通过模型精度对比，最终遴选出6个适宜的叶绿素a浓度反演模型. 进而使用不同模型组合，进行多模型协同反演. 结果表明：1多模型协同反演算法的反演精度要高于单模型反演的反演精度，最优MAPE仅为22.4%；2随着参与多模型协同反演的模型个数的增加，其反演精度也逐渐提高，MAPE均值从25.6%降低到23.4%，RMSE均值从15.082 μg ·L-1降低到14.575 μg ·L-1，相关系数R均值从0.91提升到0.92；3通过对多模型协同反演产品的置信区间进行计算，可以有效地估算产品精度和误差，同时使得获取全湖反演叶绿素a浓度的误差空间分布情况成为可能. |
| 英文摘要 |
| Under the efforts of many scholars, large amount of remote retrieval models of water quality parameters have been developed. However, each model could only reflect the "true value" from one level because of the natural limitation of remote sensing. To get the relatively true value by combining various retrieval models, in this work, we developed a multi-model collaborative retrieval algorithm for retrieving the concentration of Chlorophyll a based on data assimilation. We measured water quality parameters and water reflectance spectra in Taihu Lake during 2006 to 2009. There were seven retrieve models established and six models were selected to participate in the multi-model collaborative retrieval algorithm. Then these selected models were combined to establish a multi-model for retrieving the concentration of Chlorophyll a. The results indicated: 1 the accuracy of multi-model retrieval algorithm was better than that of single-model retrieval method, with an optimal MAPE of only 22.4%; 2 with more models participating in the multi-model collaborative retrieval algorithm, the accuracy became better, the average MAPE was decreased from 25.6% to 23.4%, the average RMSE was decreased from 15.082 μg ·L-1 to 14.575 μg ·L-1, and the average correlation coefficient was improved from 0.91 to 0.92; 3 the accuracy and errors of retrieval products could be effective evaluated through calculating the confidence interval, which makes possible the acquirement of spatial and temporal error distribution of Chlorophyll a concentration retrieval in Taihu Lake. |
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