Chlorophyll a (Chl-a) concentration is an important indicator for measuring eutrophication and lake water quality. Therefore, a fast and sensitive remote sensing method for Chl-a concentrations is urgently needed, as this will enable real-time spatio-temporal monitoring of Chl-a distribution in large inland lakes, which will enhance water quality management and protection. Using Wuliangsuhai Lake (Inner Mongolia) as an example, this study established an effective remote sensing inversion method for Chl-a concentrations, based on Landsat Thematic Mapper (TM) image data. Chl-a concentration data from January 2010 to November 2014 was collected by the Environmental Monitoring Station of Bayannur city. TM images were acquired by the Information Center of the Chinese Academy of Sciences. After pre-treatment, the Wuliangsuhai TM images were de-noised and reconstructed based on a wavelet analysis. A neural network method was subsequently used to construct a model that relates the TM spectral reflectance ratios and Chl-a concentrations. The results indicated that the proposed method of combining wavelet analysis with a neural network model is suitable for inversely remote sensing Chl-a concentrations. The correlation coefficient between the wavelet de-noised spectral signal and the Chl-a concentration (-0.575) was higher than when the original spectral signal was used (-0.417). Furthermore, the negative correlation between the de\oised spectral signal and water sample Chl-a concentrations was stronger than the original one. This demonstrated that the de\oised monitoring values could further reduce the interference of random errors and noise. Furthermore, the remotely sensed Chl-a values could approach the sampled Chl-a concentrations. In addition, the de\oised reconstruction of the TM images had a narrower reconstructed spectral than before, and part of the signals were enhanced. Nonetheless, the basic cross\sectional structure of the images did not change notably. The mean relative error (MRE) of the proposed method was 0.142, and differed little from other models. In addition, the distribution of Chl-a concentration based on the TM inversion method was consistent with the distribution of the Wuliangsuhai Lake pollution sources. The spatio\temporal distribution of Chl-a concentrations showed some variability. In the wet season, the Chl-a concentrations in shallow water areas were higher than those in the central area, whereas the Chl-a concentrations in the inlet area were higher than those in other areas. In the dry season, the Chl-a concentration decreased gradually from west to east in the middle of the lake, and showed a homogeneous pattern in the west of the lake. Overall, the precision of the TM remote sensing inversion method achieved a satisfactory prediction accuracy. However, given the lack of sufficient Chl-a monitoring sites and monitoring data, some factors that influenced the spectral reflectance ratio of TM image could not be removed or controlled for. Some improvements on TM image data acquisition, such as algorithm optimization and model verification, should therefore be a priority for the future. Alternatively, high\esolution remote sensing image data could be used to acquire the spectral reflectance ratio of lake water, instead of TM images. In conclusion, this study could be used to improve lake water quality monitoring technologies, as well as contribute to real\time water quality monitoring. The proposed method for Wuliangsuhai Lake could be applied in other areas as well, and for other water pollutants.