基于时空融合算法的水体叶绿素a反演研究

Inversion of Chlorophyll a in Water Based on Spatio-temporal Fusion Algorithm

为了准确反演水体中叶绿素a浓度,以黄柏河东支流域为例,采用STNLFFM时空融合算法,对2017年GF-4和Sentinel-2反射率数据进行融合,以重构Sentinel-2影像的时间序列数据,并对应用算法前后获取的水质参数-光谱特征响应关系建立多元线性回归模型,比较模型对叶绿素a的预测效果以验证时空融合算法的可行性,利用重构后影像光谱特征与水质参数的响应关系建立人工神经网络模型,反演2017年黄柏河东支流域各水库水体叶绿素a浓度。结果表明:利用时空融合算法生成的影像接近真实影像,提高了多元线性回归模型预测叶绿素a的效果,R2从融合前0.659提高至融合后0.844,且基于时空融合算法获取的水质参数-光谱关系建立的人工神经网络模型模拟精度较好,R2和MRE达到0.925和9.461%,反演的叶绿素a浓度空间差异性明显。证明了时空融合算法在水质参数反演过程中具有较好的应用前景。

In order to accurately invert the concentration of chlorophyll a in water,taking the eastern branch of Huangbai River as a case,the STNLFFM space-time fusion algorithm was used to fuse the reflectance data of GF-4 and Sentinel-2 in 2017 to reconstruct the time series data of Sentinel-2 image.A multiple linear regression model was established for the response relationship between water quality parameters and spectral characteristics obtained before and after the application of the algorithm,and the prediction effect of the model on chlorophyll a was compared to verify the feasibility of the space-time fusion algorithm.The artificial neural network model was established by using the response relationship between the reconstructed image spectral characteristics and water quality parameters to invert the chlorophyll a concentration of each reservoir in the eastern branch of Huangbai River in 2017.The results show that the image generated by the spatio-temporal fusion algorithm is close to the real image,which improves the effect of multiple linear regression model to predict chlorophyll a.The R2 is increased from 0.659 before fusion to 0.844 after fusion,and the artificial neural network model based on the water quality parameters-spectral relationship obtained by the spatio-temporal fusion algorithm has better simulation accuracy.The R2 and MRE reach 0.925 and 9.461%,and the spatial difference of retrieved chlorophyll a concentration is obvious.It is proved that the spatio-temporal fusion algorithm has a good application prospect in the process of water quality parameter inversion.