Water quality monitoring is important in maintaining the cleanliness and health of water bodies. It enables us to identify sources of pollutions and study trends. While modern methods include the use of satellite images to estimate water quality parameters, commonly used satellite systems, such as Landsat and Sentinel, only generate images with temporal resolution of 2 to 16 days on the average. Himawari-8 satellite system, on the other hand, generates full-disk images every 10-minutes, making it possible to generate water quality parameters concentration maps more frequently. This paper presents the preliminary analysis of the generation of yearly and seasonal Chlorophyll-<i>a</i> (Chl-<i>a</i>) and Total Suspended Matter (TSM) estimation models using Himawari-8 satellite images and linear regression. Correlation analysis shows that the single spectral bands and band ratios involving Red band have the strongest relationship with Chl-<i>a</i> and TSM. Generated linear regression yearly and seasonal models resulted to R² values of 0.4 to 0.5 with RMSE values around 3 micrograms/cm³ for Chl-<i>a</i> and 9.5 grams/m³ for TSM. Results also indicate that the seasonal models are better than the yearly models in terms of fit and error. Results from the preliminary investigation will be used to generate a more robust global model in future studies.