Water turbidity is an important proxy to measure water quality and environmental conditions. Based on extensive field data and Landsat data (2011-2018), this article developed a retrieval model suitable for turbidity. The determination coefficient (R-2) of the model was 0.946 and the root-mean-square error was 23.82 NTU. The model was implemented to obtain the turbidity information of hundreds of lakes in Northeast China (1984-2018). The results revealed the distinctive spatial pattern of water turbidity values of the lakes (i.e., high turbidity in the south; low turbidity in the northwest; and moderate turbidity in the east). In terms of temporal pattern, the water turbidity values of most lakes trended downward at an average rate of 1.39 NTU/a (P < 0.05) with obvious seasonal differences (i.e., decreased from May to the lowest in July, and then increased from September onward). Finally, we quantitatively examined how several typical factors affect turbidity variation at different scales. We found that water turbidity was highly correlated with NDVI (R = 0.56, P < 0.001), followed by water temperature and wind speed (0.02 < R < 0.5, P < 0.05). Water turbidity varies because of the interaction of multiple factors (e.g., area, temperature, water depth, precipitation, and land use) instead of one factor. This article highlights the potential of remote sensing in large-scale and long-term monitoring of lake water quality, and provides important information and support for water quality management in China.