Inner Mongolia, a northern Chinese border province, is characterized by vast geography and serves as a crucial ecological regulation area. Advancements in remote sensing technology, coupled with the availability of medium-and high-resolution Analysis Ready Data (ARD) products and cloud computing capabilities on the Google Earth Engine (GEE) platform, have enabled large-scale, long-term surface water monitoring. This study proposes a water quality assessment method integrating Sentinel-1 and Sentinel-2 data. Water bodies are delineated using the Water Detection Index applied to Sentinel-1 imagery, validated with the Gaofen Image Dataset (GID). Subsequently, validated models for Secchi Disk Depth (SDD), chlorophyll-a (Chl-a), and suspended solids concentrations (SSC) are applied to Sentinel-2 data for water quality parameter retrieval and evaluation. Field sampling and satellite data validation in Inner Mongolia reveal that surface water extraction over the past decade aligns with actual conditions, achieving 93% accuracy. From 2015 to 2023, the total surface water area initially decreased, then increased. The smallest area was recorded in 2017 (6815.22 km2) and the largest in 2023 (8191.24 km2), with an average annual change of 172 km2. Since 2019, the proportion of water bodies with high suspended solids and chlorophyll-a concentrations and low transparency has declined, while better quality water bodies increased, especially in the three major freshwater lakes in Inner Mongolia, where the water quality has significantly improved. This study provides valuable insights for regional water resource management, disaster monitoring, environmental protection, and aquaculture. However, limitations include restricted temporal resolution of satellite data. Future research will focus on integrating multi-source remote sensing data to improve temporal resolution and explore machine learning methods for enhancing monitoring accuracy and efficiency in water quality parameter retrieval.