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Urban rivers are complex ecosystems that directly determine the living environment of human beings. Monitoring the urban river water quality indexes is a challenge in water quality evaluation. The purpose of this study was to propose a multi-source remote sensing water quality inversion method based on a small number of samples to solve the problem of scale inconsistency among multi-source remote sensing data, so as to achieve large-scale and efficient inversion of urban river water quality. Since there is a very important problem that the complex nonlinear relationships must be solved between simple ground point data and remote sensing data in water quality inversion, a novel self-optimizing machine learning monitoring method is proposed, which can automatically find the optimal parameters of the model from a small number of samples, and reduce the training time. Meanwhile, in order to strengthen the correlation between water quality parameters and remote sensing data, the feature enhancement method was used for generating the input data. Moreover, to solve the problem of the multi-source data quantity and quality, the spatial mapping method was used to achieve consistency in the water quality information since these data have different nonlinear characteristics. The experimental results show that for unmanned aerial vehicle (UAV) images, the R2 of chlorophyll a (Chla), turbidity (TUB), and ammonia nitrogen (NH3-N) can reached 0.917, 0.877 and 0.846, respectively. Using a satellite image, the R2 of Chla, TUB, and NH3-N can reach 0.827, 0.679 and 0.779, respectively. This method provides a new way to realize the integration of air-space-ground monitoring of urban inland rivers in the future.

Understanding the change of river islands is essential for water conservancy construction, channel navigation safety, and industrial/agricultural production. Yangtze River is the largest river in China; however, there are few long-term monitoring results on the changes of Yangtze River islands. To fill this gap, Landsat data available on the Google Earth Engine (GEE) were used to obtain the annual changes of the Yangtze River islands located from the lower reaches of the Three Gorges Dam (TGD) to the Yangtze River delta during 1986-2017. Moreover, we focused on the changes of those islands before and after the construction of the TGD. Annual areas of all river islands were calculated and spatial-temporal pattern of Yangtze River islands were analyzed. Our satellite observation results show continuous expansions of Yangtze River islands after the construction of the TGD in 2003. For the total 138 river islands, in three stages: before 2003, after 2003 and in the 32-year study period, 68, 118, and 124 river islands showed increasing trends, with the net increase of 286.47 km2, 417.49 km2, and 665.62 km2. For the total 92 river island groups (138 islands), percentages of river island expansion are 41.30% (before 2003), 80.43% (after 2003) and 88.04% (1986-2017). Moreover, we found 10 newly developed river islands, with a net expansion of 87.42 km2 and an expansion rate of 2.73 km2/year. The significant decrease (2-4 m) in water level before and after the TGD might be the main factor that results in the observed expansion of Yangtze islands downstream the TGD. We also highlighted the importance of using all available Landsat imagery for tracking the changes of river islands. Our satellite-derived dataset provide reference data for the land use planning and environmental protection in the Yangtze River drainage basin.