Optical types of inland and coastal waters

Abstract

Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in-water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n 5 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions.

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

“…However, considering the significant methodological differences in approaches, this convergence between aquatic and terrestrial techniques is encouraging and suggests choice of atmospheric correction may be less important when using L8 in highly reflective systems. The range of spectral shapes shown here also fall within that observed within an analysis conducted by Spyrakos et al (2018) of over 250 inland and coastal water R rs spectra. The differences in spectra across optical gradients observed here are similar to those reported by Jackson et al (2017) in a recent analysis of a large scale in situ R rs and Chl-a dataset (OC-CCI v2.0, Valente et al, 2016).…”

“…Establishing turbidity, CDOM and non-algal particle concentration thresholds for the use of blue/green chlorophyll-a algorithms could prevent the masking of chlorophyll-a by sediments and the overestimation of chlorophyll-a caused by the presence of non-algal particles and CDOM. Spyrakos et al (2018) has made progress in classifying inland waters by their reflectance, which could be one approach to developing flexible bio-optical retrieval algorithms such as available for the open oceans. Future research is required to determine the dynamic range of river optical properties, their relationship to biogeochemistry, and their influence on remote sensing reflectance.…”

Extending Landsat 8: Retrieval of an Orange contra-Band for Inland Water Quality Applications

“…To evaluate the generality of the coefficients fitted with the in situ dataset, the retrieval was applied to the standardized spectra of the Optical Water Types (OWT) cluster averages [38]. The OWT clusters were based on the LIMNADES dataset and representative of inland and coastal waters in a wide geographical set [38]. Specifically, the nine coastal (C) and thirteen inland (I) waters cluster averages were used.…”

Diversity II water quality parameters from ENVISAT (2002–2012): a new global information source for lakes

“…Therefore, the development of such methods should receive much more attention, relative to the number of retrieval algorithms that were developed in recent years. The latest generation of retrieval algorithms will be based on further advanced water types (Spyrakos et al, 2018) and ensemble approaches that account for the selection of multiple algorithms' estimates (as e.g. left to the user with chl_mph_mean/chl_fub_mean).…”