Estimating specific inherent optical properties of tropical coastal waters using bio-optical model inversion and in situ measurements: case of the Berau estuary, East Kalimantan, Indonesia

Specific inherent optical properties (SIOP) of the Berau coastal waters were derived from in situ measurements and inversion of an ocean color model. Field measurements of water-leaving reflectance, total suspended matter (TSM), and chlorophyll a (Chl a) concentrations were carried out during the 2007 dry season. The highest values for SIOP were found in the turbid waters, decreasing in value when moving toward offshore waters. The specific backscattering coefficient of TSM varied by an order of magnitude and ranged from 0.003 m2 g-1, for clear open ocean waters, to 0.020 m2 g-1, for turbid waters. On the other hand, the specific absorption coefficient of Chl a was relatively constant over the whole study area and ranged from 0.022 m2 mg-1, for the turbid shallow estuary waters, to 0.027 m2 mg-1, for deeper shelf edge ocean waters. The spectral slope of colored dissolved organic matter light absorption was also derived with values ranging from 0.015 to 0.011 nm-1. These original derived values of SIOP in the Berau estuary form a corner stone for future estimation of TSM and Chl a concentration from remote sensing data in tropical equatorial water

Optical types of inland and coastal waters

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 = 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

Optical types of inland and coastal waters

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 = 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

Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes

The growth of mass populations of toxin-producing cyanobacteria is a serious concern for the ecological status of inland waterbodies and for human and animal health. In this study we examined the performance of four semi-analytical algorithms for the retrieval of chlorophyll a (Chl a) and phycocyanin (C-PC) from data acquired by the Compact Airborne Spectrographic Imager-2 (CASI-2) and the Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor. The retrieval accuracies of the semi-analytical models were compared to those returned by optimally calibrated empirical band-ratio algorithms. The best-performing algorithm for the retrieval of Chl a was an empirical band-ratio model based on a quadratic function of the ratio of re!ectance at 710 and 670 nm (R2=0.832; RMSE=29.8%). However, this model only provided a marginally better retrieval than the best semi-analytical algorithm. The best-performing model for the retrieval of C-PC was a semi-analytical nested band-ratio model (R2=0.984; RMSE=3.98 mg m−3). The concentrations of C-PC retrieved using the semi-analytical model were correlated with cyanobacterial cell numbers (R2=0.380) and the particulate and total (particulate plus dissolved) pools of microcystins (R2=0.858 and 0.896 respectively). Importantly, both the empirical and semi-analytical algorithms were able to retrieve the concentration of C-PC at cyanobacterial cell concentrations below current warning thresholds for cyanobacteria in waterbodies. This demonstrates the potential of remote sensing to contribute to early-warning detection and monitoring of cyanobacterial blooms for human health protection at regional and global scales

Bio-Optical Modeling in a Tropical Hypersaline Lagoon Environment

In this chapter, we attempted to present an overview of the use of remote sensing to monitor water quality parameters, mainly chlorophyll-a (chl-a) and turbidity. We summarized the main concepts of bio-optical modeling and presented a case study of the application of the Hyperspectral Imager for the Coastal Ocean (HICO) for the monitoring of water quality in a tropical hypersaline aquatic environment. Using HICO, we evaluated a set of different semi-empirical bio-optical algorithms for chl-a and turbidity estimation developed for inland and oceanic waters in the Araruama Lagoon, RJ, Brazil, which is an extreme environment due to its high salinity values. We also developed an empirical algorithm for both water quality parameters and compared the performances. Results showed that for chl-a estimation all models have a low performance with a normalized root mean square error (NRMSE) varying from 24.13 to 30.46. For turbidity, the bio-optical algorithms showed a better performance with the NRMSE between 15.49 and 28.04. Overall, these results highlight the importance of including extreme environments, such as the Araruama Lagoon, on the validation of bio-optical algorithms as well as the need for new orbital hyperspectral sensors which will improve the development of the field

Towards high fidelity mapping of global inland water quality using earth observation data

This body of work aims to contribute advancements towards developing globally applicable water quality retrieval models using Earth Observation data for freshwater systems. Eutrophication and increasing prevalence of potentially toxic algal blooms among global inland water bodies have become a major ecological concersn and require direct attention. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products. This study provides one of the first assessments of the state-ofthe-art for trophic status (chlorophyll-a) retrievals for small water bodies using Sentinel-3 Ocean and Land Color Imager (OLCI). Multiple fieldwork campaigns were undertaken for the collection of common aquatic biogeophysical and bio-optical parameters that were used to validate current atmospheric correction and chlorophyll-a retrieval algorithms. The study highlighted the difficulties of obtaining robust retrieval estimates from a coarse spatial resolution sensor from highly variable eutrophic water bodies. Atmospheric correction remains a difficult challenge to operational freshwater monitoring, however, the study further validated previous work confirming applicability of simple, empirically derived retrieval algorithms using top-of-atmosphere data. The apparent scarcity of paired in-situ optical and biogeophysical data for productive inland waters also hinders our capability to develop and validate robust retrieval algorithms. Radiative transfer modeling was used to fill this gap through the development of a novel synthetic dataset of top-of-atmosphere and bottom-of-atmosphere reflectances, which attempts to encompass the immense natural optical variability present in inland waters. Novel aspects of the synthetic dataset include: 1) physics-based, two-layered, size and type specific phytoplankton IOPs for mixed eukaryotic/cyanobacteria 6 assemblages, 2) calculations of mixed assemblage chl-a fluorescence, 3) modeled phycocyanin concentration derived from assemblage based phycocyanin absorption, 4) and paired sensor-specific TOA reflectances which include optically extreme cases and contribution of green vegetation adjacency. The synthetic bottom-of-atmosphere reflectance spectra were compiled into 13 distinct optical water types similar to those discovered using in-situ data. Inspection showed similar relationships and ranges of concentrations and inherent optical properties of natural waters. This dataset was used to calculate typical surviving water-leaving signal at top-of-atmosphere, as well as first order calculations of the signal-to-noise-ratio (SNR) for the various optical water types, a first for productive inland waters, as well as conduct a sensitivity analysis of cyanobacteria detection from top-of-atmosphere. Finally, the synthetic dataset was used to train and test four state-of-the-art machine learning architectures for multi-parameter retrieval and cross-sensor capability. Initial results provide reliable estimates of water quality parameters and inherent optical properties over a highly dynamic range of water types, at various spectral and spatial sensor resolutions. It is hoped the results of this work incrementally improves inland water Earth observation on multiple aspects of the forward and inverse modelling process, and provides an improvement in our capabilities for routine, global monitoring of inland water quality

Sathyendranath S., Bracher A., Brockmann C., Platt T., Ramon D., Regner P. (2017) Colour and Light in the Ocean (CLEO) 2016: A Scientific Roadmap from the CLEO Workshop Organised by ESA and PML. Held at ESRIN, Frascati, Italy on 6 - 8 September, 2016.

The Colour and Light in the Ocean (CLEO) Workshop, organized by the European Space Agency (ESA) and the Plymouth Marine Laboratory (PML) was held on the ESRIN, the ESA Centre for Earth Observations, at Frascati, Italy on 6-8 September 2016. The workshop is sponsored through selected SEOM (Scientific Exploitation of Operational Missions) projects, including: Pools of Carbon in the Ocean (POCO), Photosynthetically Active Radiation and Primary Production (PPP), Synergistic Exploitation of Hyper- and Multispectral Sentinel-Measurements to Determine Phytoplankton Functional Types (PFT) (SynSenPFT), and Extreme Case-2 Waters (C2X). Additional partner projects of ESA are: Marine Photosynthesis Parameters from Space (MAPPS), a Pathfinder STSE (Support to Science Element) project; and Ocean Colour Climate Change Initiative (OC-CCI) through the CCI (Climate Change Initiative). The objectives of the workshop were to: Evaluate state-of-art Exchange information with other relevant projects and activities Bring together remote sensing community, in situ data providers, modellers and other users Explore applications in marine ecosystem models Plan for the future: Identify challenge areas and research priorities for future EO data exploitation activities Discuss key science issues and make recommendations to strengthen community engagement Shape ideas for potential new ocean-colour products to be developed in the era of the Sentinel-3 mission The workshop was organized in five themes, developed around the activities of the sponsoring projects. Each t heme had oral, poster and discussion sessions. The workshop attracted some 160 registered participants. The workshop served an important need to connect the community, to provide a forum for lively exchange of ideas, and to recommend priorities for future activities in a collective manner. The workshop brought together scientists working on development of novel products from ocean-colour data and the user community, including, notably, the modeling community. One of the key outputs of the workshop is this report, which provides the Scientific Roadmap for future activities. Another planned outcome is a Special Issue on Colour and Light in the Oceans, to be published in the Journal, which will highlight the major scientific results presented at the workshop. Each section of the report, dealing with one of the themes of the workshop, is self-contained, but cross-references to other sections are provided where appropriate. Some recommendations found common resonance across sections, such as the need for continuous, consistent, ocean-colour data streams from satellites for long-term monitoring of the marine ecosystem; the need for an integrated approach, bringing together the remote-sensing community, the in situ data providers and the modeling community; the need to promote development of novel products and advanced sensors; and the importance of providing high-quality and uninterrupted support to the user community, through easy and free access to data and products. Each section discusses the current state of the art, identifies user requirements and gaps, and priorities for research in the short and medium terms. The workshop served the important function of sounding the community’s aspirations, and presenting them in a concise manner for ESA, through this Scientific Roadmap. One of the recommendations from the participants was that CLEO workshops be organized on a regular basis in the future, to develop the ocean-colour community , to promote exchange of new results and ideas, and to plan future activities. We thank all workshop participants, keynote speakers, authors of the oral presentations and the posters, the Scientific Committee and the Organising Committee, and the Session Chairs for all their contributions to the workshop. For the logistical support and local organization and hospitality, we thank the ESRIN Graphics Bureau, Administration, Catering Service and the Events Office, especially Irene Renis, Anne Lisa Pichler and Giulia Vinicola

Water quality estimation by optical remote sensing in boreal lakes

Monitoring of lakes is mainly based on collection of water samples, which are transported to a laboratory for analyses. In lake-rich regions gathering of water quality information is challenging, because only a small proportion of the lakes can be assessed each year, often only a few times a year. One of the techniques for improving the temporal and spatial representativeness of lake monitoring is remote sensing.The main objectives of this study were to investigate and test remote sensing interpretation algorithms for water quality estimation in Finnish lakes, to develop optical models for the needs of interpretation and for the estimation of light attenuation, and to study the advantages of the use of remote sensing data as compared to the conventional monitoring methods. The experimental material included detailed optical measurements in 11 lakes, remote sensing measurements with concurrent in situ sampling, automatic raft measurements and a national dataset of routine water quality measurements. Remote sensing data consisted of airborne and satellite measurements (ETM+, ALI and MERIS).The analyses of the spatially high-resolution airborne remote sensing data of eutrophic and mesotrophic lakes showed that one or a few discrete water quality observations of conventional monitoring can yield a clear over- or underestimation of the overall water quality. The use of TM-type satellite instruments in addition to routine monitoring results substantially increases the number of lakes for which water quality information is obtained. The results indicated preliminarily that coloured dissolved organic matter (CDOM) can be estimated with TM-type satellite instruments, which could be possible  utilised as an aid in the estimation of the role of lakes in global carbon budgets. Based on the results of reflectance modelling and experimental data, MERIS satellite instrument has optimal or near-optimal channels for the estimation of turbidity, chlorophyll a and CDOM in Finnish lakes. MERIS images with 300 m spatial resolution can be utilised in production of water quality information in different parts of large and medium-size lakes, and in filling the gaps of conventional monitoring. Regional algorithms that would not require simultaneous in situ data for algorithm training would increase the amount of remote‑sensing-based information available for lake monitoring.  The MERIS Boreal Lakes processor, trained with the optical data and concentration ranges provided by this study, enabled turbidity estimation with good accuracy without need for algorithm correction with in situ measurements, while chlorophyll a and CDOM estimation requires further development of the processor. The accuracy of interpretation of chlorophyll a via semi‑empirical algorithms can be improved by classifying lakes prior to interpretation by CDOM level and trophic status, and by creating lake-type-specific algorithms. The results of optical modelling showed that spectral diffuse attenuation coefficient can be estimated with reasonable accuracy from the measured water quality concentrations. This provides more detailed information on light attenuation from routine monitoring measurements than is available through the Secchi disk transparency.This study improves the interpretation of water quality by remote sensing and encourages the use of remote sensing in lake monitoring.

Dissolved carbon and CDOM in lake ice and underlying waters along a salinity gradient in shallow lakes of Northeast China

The variations of DOC and DIC concentrations in lake ice and underlying waters were examined in 40 shallow lakes across the Songnen Plain, Northeast China. The lakes, frozen annually during winter, included freshwater and brackish systems (EC > 1000 μS cm−1; range: 171–12607 μS cm−1 in underlying water). Results showed that lake ice contained lower DOC (7.2 mg L−1) and DIC (6.7 mg L−1) concentration compared to the underlying waters (58.2 and 142.4 mg L−1, respectively). Large differences in DOC and DIC concentrations of underlying waters were also observed between freshwater (mean ± SD: 22.3 ± 11.5 mg L−1, 50.7 ± 20.6 mg L−1) and brackish lakes (83.3 ± 138.0 mg L−1, 247.0 ± 410.5 mg L−1). A mass balance model was developed to describe the relative distribution of solutes and chemical attributes between ice and the underlying waters. Results showed that water depth and ice thickness were the key factors regulating the spatial distribution of solutes in the frozen lakes. Chromophoric dissolved organic matter (CDOM) absorption coefficient at 320 nm, aCDOM(320) and specific UV absorbance (SUVA254) were used to characterize CDOM composition and quality. Compared to the underlying waters, CDOM present in ice largely included low aromaticity organic substances, an outcome perhaps facilitated by ice formation and photo-degradation. In ice and underlying freshwaters, CDOM predominantly included organic C fractions of high aromaticity, while low aromaticity organic substances were observed for brackish lakes. Results of this study suggest that, if water salinity increases due to climate change and anthropogenic activities, significant changes can occur in the dissolved carbon and fate of CDOM in these shallow lakes

Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters

One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP) -based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N \u3e 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (Rrs), (b) retrieval of particulate backscattering (bbp) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from bbp via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m3], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e.,from 10 to \u3e100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in Rrs leads to \u3c20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID has a potential for producing TSS products in global coastal and inland waters, our statistical analysis certainly verifies that there is still a need for improving retrievals across a wide spectrum of particle loads