基于高光谱数据提取水体叶绿素a浓度的混合光谱模型

Spectral mixing model and its stability of chlorophyll-a concentration extraction based on hyperspectral data

从模型的精度和稳定性方面,与传统的经验模型比较显示,利用原始数据的一阶微分数据进行叶绿素a浓度混合光谱模型反演最佳。最后,以此模型对三种不同年份测量的地面高光谱数据进行了叶绿素a浓度的提取。实验结果表明混合光谱模型可以作为遥感监测水体叶绿素a含量的定量模型。

The development of hyperspectral technology enables the quantitative monitoring of water quality based on the hyperspectral data. By now, the ordinary monitoring method always depends on the developing relationships between radiance/reflectance in single band or band ratios and the chlorophyll-a concentration. In this paper, a spectral mixing model is established based on the field hyperspectral data. And three types of data, the original, normalized and differential data, are selected as the input. The optimal band range （670 - 710 nm） which indicates the chlorophyll-a concentration is selected, and the chlorophyll-a concentration is reverted by the linear spectral mixing model in which the water and chlorophyll-a end-member spectral come from in situ water samples. The compared results show that the spectral mixing model based on differential data gives the best result in the model precision and stabilization. Finally, three-phase field hyperspectral data are processed and the chlorophyll-a concentration is extracted by using this spectral mixing model. The result shows that the spectral mixing model is a feasible, stable and of high resolution model in the practical application of remote sensing water quality monitoring.