A Bayesian hierarchical model for assessing the eutrophication status of inland freshwater systems in the contiguous United States from Landsat time series: the promise of a universal transferable model

Inland water bodies are variable and complex environments, which are indispensable for maintaining biodiversity and providing ecosystem services. The ecological functions of these environments are increasingly threatened by several stressors such as climate change, human activities and other natural stressors. Anthropogenic eutrophication has become one of the most pressing causes of water quality degradation of freshwater ecosystems worldwide. The eutrophication process accelerates the occurrence of algal blooms, with the dominance of potentially toxic cyanobacterial species. As a result, the assessment and monitoring of change in the eutrophic status of these systems is deemed necessary for adopting efficient and adaptive water quality management plans. While conventional monitoring methods provide accurate snapshots of eutrophication metrics at discrete points, they do not provide a synoptic coverage of the status of a water body in space and time. Compared with in situ monitoring, remote sensing provides an effective method to assess the water quality dynamics of water bodies globally at a relatively high spatio-temporal resolution. Yet, the full potential of remote sensing towards assessing eutrophication in inland freshwater systems has so far remained limited by the need to develop site specific models that need extensive local calibration and validation. This constraint is associated with the poor transferability of these models between systems. In this work, we develop a Bayesian hierarchical modelling (BHM) framework that provides a comprehensive models that can be used to predict chlorophyll-a levels, Secchi disk depth (SDD), and total suspended solids across the continental United States (US) based on Landsat 5, 7 and 8 surface reflectance data. The proposed BHM is able to assess, account, and quantify the lake, ecoregion, and trophic status variabilities. The model is developed based on the AquaSat database that contains more than 600,000 observations collected between 1984 and 2019 from lakes and reservoirs across the contiguous US. The model improved the predictions of SDD and Chlorophyll-a the most as compared to the pooled model; yet no such improvements were observed for TSS. Meanwhile, making use of the ecoregion categorization to develop the BHM structure proved to be the most advantageous.