Lake water clarity is an important parameter of lake water property, which is an integrated response of lake plankton and organic and inorganic solutions, and has significant scientific and practical implications for lake ecological condition research. Remote sensing is a key method for obtaining lake clarity in wide areas and within long time spans. On the Tibetan Plateau,there are more than 389 lakes with area greater than 10 km~2, making the Plateau an ideal region for environmental and climate change research. However, study on the estimation of lake water clarity on the Tibetan Plateau by satellite data is insufficient at present due to the paucity of in situ lake water clarity measurement data. In this study, retrieval models of lake water clarity were established based on the in situ water clarity measurements of 24 lakes distributed in different areas on the Tibetan Plateau and the corresponding MODIS imageries. Statistical methods including linear, exponential, power function,and logarithm regressions were used to build relationships between lake water clarity and the reflectance of MODIS bands on the Tibetan Plateau. The results show that power function model with MODIS green band B4 as single independent variable is the best model for estimating lake water clarity (SD value) on the Plateau (R~2=0.91, N=24). The stability of the model was also tested based on 10 in situ SD data at different times in a single lake. Based on this model, we analyzed the temporal variation of lake water clarity of a typical lake Tangra Yumco as an example. The result demonstrates clear seasonal and interannual variations of lake water clarity for this lake. Preliminary analysis indicates that the variation of water clarity in precipitation-meltwater rich season is correlated with precipitation intensity of the basin. Our work proved that the reflectance of remote sensing imageries is valid for estimating lake water clarity on the Tibetan Plateau. This may promote further investigation of lake water clarity and its influencing factors on the Tibetan Plateau.