基于进化建模方法的HJ-1 CCD黄海悬浮物和叶绿素a浓度遥感反演模型研究

Retrieval models of total suspended matter and chlorophyll aconcentration in Yellow Sea based on HJ-1 CCD data and evolutionary modeling method

本文利用实测数据集,发展了基于进化建模方法的HJ-1CCD黄海悬浮物(TSM)和叶绿素a浓度(Chl a)遥感反演模型,建模过程中有针对性地设计了适合水色反演的端点集和函数集,并利用转基因方法引入水色先验知识。经实测数据检验,TSM反演的平均相对误差约为31%(相关系数R2为0.96),Chl a反演误差约为33%(R2为0.88)。分析了模型对输入误差的敏感性,当输入端引入±5%的误差时,模型误差的波动在大多数情形下都可控制在±10%以内。与神经网络模型相比,本文发展的进化模型具有检验精度高、结构简单等优势。利用不同季节的黄、东海实测数据进行了模型精度的独立检验。本文的研究工作表明,进化建模方法适用于水色遥感反演建模问题,可由程序自动生成多个满足精度要求、结构形式多样的显式模型,为水色反演应用提供了多种选择,对于拥有数百个波段的高光谱数据水色反演具有更大的应用潜力。本文最后探讨了进化建模方法的改进方向。

By using the in-situ measuring data, this study developed retrieval models of chlorophyll a （Chl a） and total suspended matter （TSM） for HJ-1 CCD data in the Yellow Sea based on the evolutionary modeling method. The terminal and function set of the evolutionary modeling method were designed to be adapted to retrieval of wa- ter constituents, and the transgene operator was employed to insert and maintain the prior knowledge. The average percentage difference （APD） for TSM was 31% （the correlation coefficient R2 = 0.96）, and that for Chla was 33 % （R^2 =0.88）. The error sensitivity of the retrieval models was analyzed, and the output errors were generally less than ±10 % when introducing ± 5 % error of remote sensing reflectance. Compared with neural network method, the evolutionary models have higher accuracy and simpler structures. In addition, in-situ data with different sea- sons was employed to validate the accuracy of the retrieval models. This study shows that the evolutionary model- ing method is applicable for retrieval of water constituents from ocean color remote sensed data. Many explicit models with well accuracy and different structures could be obtained automatically, and they are of potential appli- cations for hyperspectral data. Finally, we discussed how to improve the method in the near future.