Abstract: A remote sensing inversion model was developed using a random forest machine learning model to study the spatio-temporal variations of chlorophyll a and turbidity in Honghu Wetland, the largest lake in Hubei Province.The inversion models for chlorophyll a and turbidity showed a satisfactory fit, with R2 values of 0.888 and 0.878, respectively.Mean chlorophyll concentrations in Honghu Lake increased from 2020 to 2022, with values of 58.127, 61.847, and 82.017 μg/L. The mean turbidity levels were 50.180 NTU, 47.379 NTU, and 85.377 NTU for the same period.The significant increase in chlorophyll a and turbidity over the three years indicated a continuous deterioration in the water quality. Factors such as long-term cage aquaculture, extreme climate events, external pollution load, and endogenous pollution release contributed to these changes.This study provided valuable insights into the spatio-temporal variation of chlorophyll a and turbidity in Honghu Lake to help take early warning and effective management efforts.