Turbidity, a critical parameter in underwater environments, has a profound influence on essential processes, particularly algal photosynthesis. In this work, we use satellite images from Landsat 8 (L8) and Sentinel 2 (S2) A/B missions to estimate the water turbidity in Lake Maihue on the South American continent. This mountain lake is difficult to access and is therefore one of the least studied lakes in the region. We used six algorithms for turbidity estimation through satellite images and ACOLITE software. In addition, we created and validated an empirical turbidity estimation algorithm considering the local characteristics of the data collected in situ. Finally, we evaluated the influence of hydrometeorological parameters on the turbidity values in the lake. The best estimation model predicted turbidity values in the range of 0.4–4.0 nephelometric turbidity unit (NTU) with root mean square error (RMSE) values around (0.40–1.56 NTU), with a coefficient of determination (R2) of (0.91) and a mean bias error (MBE) around (0.52–0.67 NTU). Estimation maps were obtained to evaluate the spatiotemporal variation in the lake. It was observed that meteorological conditions can influence turbidity, with precipitation being the most influential. This study contributes to the understanding of the differences between water clarity variations in Lake Maihue and their relationship with hydrometeorological events. We use remote sensing as a support tool in the monitoring of water quality parameters in inland water bodies.