Remote sensing techniques are proven methods for quantifying chlorophyll-a levels by inference algal concentrations in reservoirs. One traditional method is to use Landsat imagery and field data from a limited time period to develop a model for a reservoir which relates reflectance in various bands to measured algal (or chlorophyll-a) concentrations and use that model and associated imagery to determine spatial algal concentrations in the reservoir. In this work, we extend these techniques to use historical Landsat data over long time periods to develop seasonal models that will more accurately describe the conditions throughout the growing season. Previous work at Deer Creek included the development of a chlorophyll-a model using data from the months of August to September. This model did not account for seasonal variation and algal succession, which affects the relationship between measured reflectance and algal concentration. Early summer algal blooms are dominated by diatoms (yellow-brown), while the algae vary from chlorophyta (green) in the mid-summer to cyanobacteria (blue-green) in late summer months. This study presents and explores the development and use of seasonal algorithms based on reflective characteristics of various algal communities to create a more accurate model for the reservoir. This study uses water quality data collected over a 20-year period during non-ice conditions along with associated Landsat data. As the field measurements were not taken to support remote sensing measurements, this study evaluates the use of historical data to support remote sensing analysis. It is assumed that reservoir conditions do not change rapidly, the field data can be used to develop correlations with satellite imagery taken within a day of the field measurements, and the seasonal algal communities have different reflective properties (or colors). We present statistical analysis that shows the seasonal algorithms better fit the data than the non-seasonal model and the traditional model calibrated with late-season data. We recommend the use of sub-seasonal algorithms to more accurately model and predict water quality throughout the growing season.