Assessing the effectiveness of Landsat 8 chlorophyll a retrieval algorithms for regional freshwater monitoring

Abstract

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 (R ) and P ≤ 0.05 when Landsat 8 images within 5 d, and improved to R 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.

Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity

“…Where matchups were available at the exact moment of the satellite overpass, we conducted a sensitivity analysis to quantify changes in accuracies resulting from using more or less restrictive time windows. Boucher et al (2016) showed using a time window of 2 instead of 5 days improved agreement between L8-retrieved and in situ Chl-a values in northeastern lakes. In this study, using a time window of ± 3 instead of 24 h reduced differences by 5% (ACOLITE) and 31% (SeaDAS) for Chl-a.…”

Hyperspectral Satellite Remote Sensing of Water Quality in Lake Atitlán, Guatemala

“…This confirms the adequacy of the results. Other studies that retrieve Chl a concentration in fresh water bodies using multispectral sensors show low degree of certainty (Boucher et al, 2018). However, better performance was obtained when time window of multispectral satellite data acquisition was closer to acquisition of in situ data used in correlation.…”

The case for research integration, from genomics to remote sensing, to understand biodiversity change and functional dynamics in the world's lakes

“…Similarly, combined traditional in situ monitoring and metagenomic sampling shows promise for understanding the environmental drivers of viral communities, and their potential impacts upon ecosystem functioning (Palermo, Fulthorpe, Saati, & Short, 2019). Another example of causal complementarity comes from the Earth observation field; here spatial and temporal variations in algorithm performance for retrieval of phytoplankton biomass data (i.e., the strength of the relationship between water-leaving radiance and water column chlorophyll a concentrations) can be marked (Boucher, Weathers, Norouzi, & Steele, 2018), and may be a result of species turnover within the observed plankton community (Ligi et al, 2017). Clearly there remains a role for in situ taxonomic monitoring to quantify community structure and change, so that we can understand the causality underpinning observed relationships between satellite-derived reflectance and integrative ecosystem properties.…”