Monitoring algal blooms in shallow eutrophic lakes remains challenging due to subtle biomass signals and interference from bottom reflectance and high suspended sediment loads. Utah Lake, a large eutrophic and highly turbid freshwater lake in central Utah (average depth about 2.7 m), frequently experiences summer cyanobacterial blooms driven by elevated nutrient inputs and resuspension of sediments. This study evaluated two Landsat compatible spectral indicies for bloom detection: a Landsat adapted Normalized Difference Chlorophyll Index (NDCI_L) and the natural log transformed NIR/Red ratio (ln(B5/B4)). The study analyzed atmospherically corrected Landsat-9 OLI surface reflectance imagery, with water masking performed using the Modified Normalized Water Index (MNDWI). Both indices were compared to situ chlorophyll-A observations. Results indicate that ln(NIR/Red) exhibited stronger linear correlations with chlorophyll-A in relatively deeper portions of the lake; however, the logarithmic scaling amplified noise, making it more sensitive to reflectance uncertainty. Because chlorophyll-A in eutrophic lakes commonly follows a log-normal distribution, the log-transformed NIR/Red ratio can enhance regression models when high-quality surface reflectance data are used. In contrast, NDCI_L, being a normalized index, provided better discrimination under moderate conditions and is more suitable for threshold-based classification, cross sensor comparisons, and operational monitoring. This work highlights the importance of using atmospherically corrected surface reflectance rather than raw digital numbers, outlines preprocessing requirements, and evaluates the relative performance of two practical Landsat based indicies for algal bloom monitoring. These findings contribute to improving near real time bloom surveillance in shallow eutrophic systems such as Utah Lake.