Accurate and cost-effective assessment of water quality is necessary for proper management and restoration of inland water bodies susceptible to algal bloom conditions. Landsat and MODIS satellite images were used to create chlorophyll and Secchi depth predictive models for algal assessment of Great Lakes and other lakes of the United States. Boosted regression tree (BRT) models using satellite imagery are both easy to use and can have high predictive performance. BRT models inferred chlorophyll and Secchi depth more accurately than linear regression models for all study locations. Inferred chlorophyll of inner Saginaw Bay was subsequently used in ecological models to help understand the ecological drivers of algal blooms in this ecosystem.For small lakes (non-Great Lakes), the best national Landsat model for ln-transformed chlorophyll was the BRT model and had a cross-validation R2 of 0.44 and a 0.76 ln-transformed &mug/L RMSE. The best national Landsat model for Secchi depth was also a BRT model that had an adjusted R2 of 0.52 and a 0.80 m RMSE. We assessed the applicability of the national chlorophyll model for ecological analysis by comparing the total phosphorus- chlorophyll relationship with chlorophyll determined from sampling or remote sensing, which showed the total phosphorus- chlorophyll relationship had an adjusted R2 = 0.58 and 1.02 ln-transformed μg/L RMSE with sampled chlorophyll versus an adjusted R2 = 0.56 and 1.04 ln-transformed &mug/L RMSE with chlorophyll determined by the boosted regression tree remote sensing model.For Great Lakes models, the MODIS BRT model predicted chlorophyll most accurately of the three BRT models and compared well to other models in the literature. BRT models for Landsat ETM+ and TM more accurately predicted chlorophyll than the MSS model and all Landsat models had favorable results when compared to the literature. BRT chlorophyll predictive models are useful in helping to understand historical, long-term chlorophyll trends and to inform us of how climate change may alter ecosystems in the future. In inner Saginaw Bay, annual average and upper quartile Landsat-derived chlorophyll decreased from 7.44 to 6.62 and 8.38 to 7.38 &mug/L between 1973-1982, and annual upper quartile of 8-day phosphorus loads increased from 5.29 to 6.79 kg between 1973-2012. Simple linear and multiple regression models and Wilcoxon rank test results for MODIS and Landsat-derived chlorophyll indicate that distance from the Saginaw River mouth influences chlorophyll concentration in Saginaw Bay; Landsat-derived surface water temperature and phosphorus loads to a lesser extent. Mixed-effect models for MODIS and Landsat-derived chlorophyll were related to chlorophyll better than simple linear or multiple regressions, with random effects of pixel and sample date contributing substantially to predictive power (NSE=0.35-70), though phosphorus loads, distance to Saginaw River mouth, and water were significant fixed effects in most models. Water quality changes in Saginaw Bay between 1972-2012 were influenced by phosphorus loading and distance to the Saginaw River's mouth. Landsat and MODIS imagery are complementary platforms because of the long history of Landsat operation and the finer spectral resolution and image frequency of MODIS. Remote sensing water quality assessment tools can be valuable for limnological study, ecological assessment, and water resource management.
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