NH and ME Landsat chlorophyll-a retrieval algorithms and in situ measurements 2000 (Landsat 7), 2013-2015 (Landsat 8)

Predicting algal blooms has become a priority for scientists, municipalities, businesses, and citizens. Remote sensing offers solutions to the spatial and temporal challenges facing existing lake research and 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 specific large water bodies in particular seasons and narrow chl-a ranges. Thus, a first step toward prediction of algal blooms is generating regionally robust algorithms using in situ and remote sensing data. This study explores the relationship between in-lake measured chl-a data from Maine and New Hampshire lakes and remotely-sensed chl-a retrieval algorithm outputs. Landsat 8 images were obtained and then processed after required atmospheric and radiometric corrections. Six previously developed algorithms were tested on a regional scale on eleven scenes from 2013-2015 covering 192 lakes. Additionally, data related to one Landsat 7 scene (2000) are included in this data set. Boucher, J, K.C. Weathers, H. Norouzi, B. Steele. In Press. Assessing the effectiveness of Landsat 8 chlorophyll-a retrieval algorithms for regional freshwater monitoring. Ecological Applications.