Lake is an important ecological resource, which not only determines the quality of regionally ecological environment, but also guarantees the sustainable development of the city. Lake ecological environment can reflect the environmental situation of the region. Secchi depth (SD) can directly reflect the state of lake water quality, and mastering long-term and large-scale lake SD is the key to control and improve the lake water environment. Dianchi Lake, located in the middle of Yunnan Guizhou Plateau, is the sixth largest freshwater lake in China. It is the main water area for irrigation in Kunming area, and it is also the basic condition for urban development. Therefore, Dianchi Lake is chosen as the research area. Because the in-situ monitoring of lake water quality for this study area was started late, there are few historical SD data. So, it is necessary to simulate and estimate SD by remote sensing images. In this paper, deep neural network algorithm was used to invert the SD of Dianchi Lake from January 1, 2001 to December 31, 2018 based on in-situ data and MODIS remote sensing images, and explored its spatial-temporal variation characteristics by geospatial analysis. The in-situ data was the daily monitoring values of 10 stations (Baiyukou, Caohai Center, Dianchi South, Duanqiao, Guanyinshan East, Guanyinshan West, Guanyinshan Center, Haikou West, Huiwan Center, and Luojiaying) from 2001 to 2010 and the monthly monitoring values of 2 regions (Caohai and Waihai) from 2018, which was provided by Yunnan Academy of Environmental Sciences; MODIS data was provided by NASA LAADS Web, with daily time resolution and 500 m spatial resolution. The methods used in this paper included Grey Relational Analysis (GRA), Long-Short Term Memory (LSTM), and Theil-Sen slope estimation. In the construction of SD inversion estimation model, the main methods include Correlation Analysis (C), Outlier Processing (O), Denoising Processing (D), and estimation model (COD-LSTM). The results showed that (1) the inversion model proposed in this paper had better performance (RMSE=0.1359, MAE=0.1134), which can objectively reflect SD status. (2) The analysis of temporal variation characteristics indicated that the SD of Dianchi Lake presented a downward trend with an average comprehensive change rate of -0.08 m/10 a. (3) The analysis of spatial variation characteristics indicated that the rate of decline in the region with higher SD was larger, and the trend in the region with lower SD was relatively stable. The SD near the urban and residential areas was relatively low. Human activities would become an important factor affecting the SD changes in Dianchi Lake, and it was also the main factor causing lake pollution.