Assessing the Effectiveness of Landsat 8 Chlorophyll-a Retrieval Algorithms for Regional Freshwater Management

Predicting algal blooms has become a priority for municipalities, businesses, and citizens. Remote sensing (RS) offers solutions to the spatial and temporal challenges facing existing lake monitoring programs that rely primarily on high-investment in situ measurements. Techniques to remotely measure chlorophyll-a (chl-a) as a proxy for algal biomass have been limited to large water bodies in particular seasons and chl-a ranges. This study explores the relationship between in-lake measured chl-a data in Maine and New Hampshire and chl-a retrieval algorithms. Landsat 8 images were obtained and required atmospheric and radiometric corrections. Six indices including the NDVI and KIVU algorithms were tested to validate their applicability on a regional scale on ten scenes from 2013-2015 covering 169 lakes. In addition, more robust novel models were also explored. For late-summer scenes, existing algorithms accounted for nearly 90% of the variation in in-situ measurements, however, we found a significant effect of time of year on each index. A sensitivity analysis revealed that rainfall in the region as well as a longer time difference between in situ measurements and the satellite image increased noise in the models. The quantification of these confounding influences points to potential solutions such as incorporating remotely sensed water temperature into models as a proxy of seasonal effects. Novel models built to fit particular scenes reduced this variability, but they required more satellite band inputs that do not yet have a clear ecological relevance. These results suggest that RS could be an effective and accessible tool for monitoring programs at the regional scale. Although they are subject to some of the limitations of traditional monitoring imposed by the weather, high-resolution satellites like Landsat 8 provide a promising opportunity for protecting freshwater resources.