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长春南湖水体透明度高光谱定量模型研究

ISSN号：2095-6134
期刊名称：《中国科学院大学学报》
时间：0
分类：TP79[自动化与计算机技术—控制科学与工程;自动化与计算机技术—检测技术与自动化装置]
作者机构：[1]中国科学院东北地理与农业生态研究所,长春130012, [2]中国科学院研究生院,北京100049
相关基金：国家自然科学基金项目（40401003,40371082）和中国科学院资源环境领域野外台站基金资助

关键词：透明度, 赛克盘, 水质, 模型, 高光谱, 遥感, transparency, Secchi disk, water quality, model, hyperspectral, remote sensing

中文摘要：

利用野外高光谱仪在长春南湖夏季进行了反射光谱测量和同步水质采样分析，通过分析水体透明度与其高光谱反射率之间的相关关系，尝试采用多种半经验算法建立透明度高光谱定量模型，并进行了验证．结果表明：（1）单波段、波段比值和一阶微分反演模型，确定性系数皆在0．74以上，RMSE小于透明度极值差，因此皆可以用于反演透明度；（2）模型精度从高到低依次为：一阶微分模型、单波段模型和波段比值模型．该定量模型的建立，为今后利用成像光谱数据在南湖进行透明度大面积遥感反演研究提供了研究基础和科学依据，对内陆水体透明度反演也有一定的借鉴意义．

英文摘要：

Secchi disk transparency is a measure of the transparency of a water body, and it is influenced by the abundance of organic and inorganic particulate and dissolved matter. Secchi disk transparency may be determined from hyperspectral remote sensing data. The advantage of high spectral resolution reflectance data （hyperspectral data） is the possibility of quantitative analysis due to diagnostic absorption bands. This study was conducted to establish correlations between hyperspectral reflectance spectra and secchi disk transparency in Nanbu Lake, Changchun. Reflectance spectra were measured from June to September in 2004, with ASD FieldSpec spectrometer. Based on in situ water sampling and reflectance measurements, algorithms have been developed. The single band R520 the spectra ratio of （ R530 - R860 ）/（ R740 - R860 ）, and the reflectance first-derivative of 610nm, which showed a linear relationship, were all found to be significantly correlated with secchi disk transparency. The algorithms have been applied to evaluate secchi disk transparency, and the determination coefficient R^2 gained using the first-derivative of 610nm was higher than that gained with other two models. The validation using independent in situ reference data showed RMSE of 4.97 ～9.68cm. There is satisfactory correspondence between hyperspectral models and secchi disk transparency, and it is possible to monitor the water quality of Nanhu Lake by using hyperspetral remote sensing data.

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