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Assessing the effectiveness of Landsat 8 chlorophyll a retrieval algorithms for regional freshwater monitoring
- Boucher, Jonah, Weathers, Kathleen C., Norouzi, Hamid, Steele, Bethel
- Ecological applications 2018 v.28 no.4 pp. 1044-1054
- Landsat, algae, algal blooms, algorithms, biomass, business enterprises, chlorophyll, correlation, freshwater, lakes, models, monitoring, prediction, radiometry, remote sensing, scientists, seasonal variation, spatial data, summer, surface water, uncertainty, Maine, New Hampshire
- 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, USA 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 11 scenes from 2013 to 2015 covering 192 lakes. The best performing algorithm across data from both states had a 0.16 correlation coefficient (R²) and P ≤ 0.05 when Landsat 8 images within 5 d, and improved to R² of 0.25 when data from Maine only were used. The strength of the correlation varied with the specificity of the time window in relation to the in‐situ sampling date, explaining up to 27% of the variation in the data across several scenes. Two previously published algorithms using Landsat 8's Bands 1–4 were best correlated with chl a, and for particular late‐summer scenes, they accounted for up to 69% of the variation in in‐situ measurements. A sensitivity analysis revealed that a longer time difference between in situ measurements and the satellite image increased uncertainty in the models, and an effect of the time of year on several indices was demonstrated. A regional model based on the best performing remote sensing algorithm was developed and was validated using independent in situ measurements and satellite images. These results suggest that, despite challenges including seasonal effects and low chl a thresholds, remote sensing could be an effective and accessible regional‐scale tool for chl a monitoring programs in lakes.