Abstract
Common aquatic remote sensing algorithms estimate the trophic state (TS) of inland and nearshore waters through the inversion of remote sensing reflectance (Rrs (λ)) into chlorophyll-a (chla) concentration. In this study we present a novel method that directly inverts Rrs (λ) into TS without prior chla retrieval. To successfully cope with the optical diversity of inland and nearshore waters the proposed method stacks supervised classification algorithms and combines them through meta-learning. We demonstrate the developed methodology using the waveband configuration of the Sentinel-3 Ocean and Land Colour Instrument on 49 globally distributed inland and nearshore waters (567 observations). To assess the performance of the developed approach, we compare the results with TS derived through optical water type (OWT) switching of chla retrieval algorithms. Meta-classification of TS was on average 6.75% more accurate than TS derived via OWT switching of chla algorithms. The presented method achieved > 90% classification accuracies for eutrophic and hypereutrophic waters and was > 12% more accurate for oligotrophic waters than derived through OWT chla retrieval. However, mesotrophic waters were estimated with lower accuracy from both our developed method and through OWT chla retrieval (52.17% and 46.34%, respectively), highlighting the need for improved base algorithms for low - moderate biomass waters. Misclassified observations were characterised by highly absorbing and/or scattering optical properties for which we propose adaptations to our classification strategy.
Highlights
Eutrophication is the process whereby nutrient enrichment leads to excessive primary production of phytoplankton in water bodies (Conley et al, 2009; Smith et al, 2006)
To successfully cope with the optical diversity of inland and nearshore waters, we explore the concept of stacking classifiers in a meta-learning scheme
In the comparison of the meta-classifier against trophic state (TS) derived via optical water type (OWT) switching, we evaluated the results of the regression of chla retrieved from an algorithm (estimated (E)) versus the in situ chla values (observed (O))
Summary
Eutrophication is the process whereby nutrient enrichment leads to excessive primary production of phytoplankton (cyanobacteria and algae) in water bodies (Conley et al, 2009; Smith et al, 2006). Cyanobacteria may produce cyano toxins which adversely affect human and animal health (Codd, 2000; Merel et al, 2013). Lentic waters such as lakes are significant emitters of the greenhouse gases carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) (Cole et al, 2007; DelSontro et al, 2018). Enhanced eutrophication due to anthropogenic climate change is expected to in crease aquatic CH4 emissions from lentic waters by 30 – 90% over the century (Beaulieu et al, 2019; Tranvik et al, 2009)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: ISPRS Journal of Photogrammetry and Remote Sensing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
