1.College of Hydraulic and Environmental Engineering，China Three Gorges University，Yichang;2.Institute of Qinghai-Tibet Plateau, Chinese Academy of Sciences
Second Tibetan Plateau Scientific Expedition and Research Program (STEP, Grant No. 2019QZKK0103)；Science and Technology Research Project of Hubei Provincial Department of Education (Q20221209)；European Space Agency, China National Remote Sensing Center Project ( 58516 )；
In order to accurately invert the concentration of chlorophyll a in water, taking the eastern branch of Huangbai River as an example, 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.