A tuned ocean color algorithm for the Arctic Ocean: a solution for waters with high CDM content

The Arctic Ocean (AO) is the most river-influenced ocean. Located at the land-sea interface wherein phytoplankton blooms are common, Arctic coastal waterbodies are among the most affected regions by climate change. Given phytoplankton are critical for energy transfer supporting marine food webs, accurate estimation of chlorophyll a concentration (Chl), which is frequently used as a proxy of phytoplankton biomass, is critical for improving our knowledge of the Arctic marine ecosystem and its response to the ongoing climate change. Due to the unique and complex bio-optical properties of the AO, efforts are still needed to obtain more accurate Chl estimates, especially for coastal waters with high colored detrital material (CDM) content. In this study, we optimized the the Garver-Siegel-Maritorena (GSM) algorithm, using an Arctic bio-optical dataset comprised of seven wavelengths (the original GSM wavelengths plus 625 nm). Results suggested that our tuned algorithm, denoted GSMA, outperformed an alternative AO GSM algorithm denoted AO.GSM, but the accuracy of Chl estimates was only improved by 8%. In addition, GSMA showed appreciable robustness when assessed using a satellite image and two non-Arctic coastal datasets.

Semi-empirical models for remote estimating colored dissolved organic matter (CDOM) in a productive tropical estuary

Inland waters are important components of the global carbon cycle as they regulate the flow of terrestrial carbon to the oceans. In this context, remote monitoring of Colored Dissolved Organic Matter (CDOM) allows for analyzing the carbon content in aquatic systems. In this study, we develop semi-empirical models for remote estimation of the CDOM absorption coefficient at 400 nm (a_CDOM) in a tropical estuarine-lagunar productive system using spectral reflectance data. Two-band ratio models usually work well for this task, but studies have added more bands to the models to reduce interfering signals, so in addition to the two-band ratio models, we tested three- and four-band ratios. We used a genetic algorithm (GA) to search for the best combination of bands, and found that adding more bands did not provide performance gains, showing that the proper choice of bands is more important. NIR-Green models outperformed Red-Blue models. A two-band NIR-Green model showed the best results (R² = 0.82, RMSE = 0.22 m^-1, and MAPE = 5.85%) using field hyperspectral data. Furthermore, we evaluated the potential application for Sentinel-2 bands, especially using the B5/B3, Log(B5/B3) and Log(B6/B2) band ratios. However, it is still necessary to further explore the influence of atmospheric correction (AC) to estimate the a_CDOM using satellite data.

Deriving Particulate Organic Carbon in Coastal Waters from Remote Sensing: Inter-Comparison Exercise and Development of a Maximum Band-Ratio Approach

Recently, different algorithms have been developed to assess near-surface particulate organic matter (POC) concentration over coastal waters. In this study, we gathered an extensive in situ dataset representing various contrasted bio-optical coastal environments at low, medium, and high latitudes, with various bulk particulate matter chemical compositions (mineral-dominated, 50% of the data set, mixed, 40%, or organic-dominated, 10%). The dataset includes 606 coincident measurements of POC concentration and remote-sensing reflectance, Rrs, with POC concentrations covering three orders of magnitude. Twelve existing algorithms have then been tested on this data set, and a new one was proposed. The results show that the performance of historical algorithms depends on the type of water, with an overall low performance observed for mineral-dominated waters. Furthermore, none of the tested algorithms provided satisfactory results over the whole POC range. A novel approach was thus developed based on a maximum band ratio of Rrs (red/blue, red/yellow or red/green ratio). Based on the standard statistical metric for the evaluation of inverse models, the new algorithm presents the best performance. The root-mean square deviation for log-transformed data (RMSDlog) is 0.25. The mean absolute percentage difference (MAPD) is 37.48%. The mean bias (MB) and median ratio (MR) values are 0.54 μg L−1 and 1.02, respectively. This algorithm replicates quite well the distribution of in situ data. The new algorithm was also tested on a matchup dataset gathering 154 coincident MERIS (MEdium Resolution Imaging Spectrometer) Rrs and in situ POC concentration sampled along the French coast. The matchup analysis showed that the performance of the new algorithm is satisfactory (RMSDlog = 0.24, MAPD = 34.16%, MR = 0.92). A regional illustration of the model performance for the Louisiana continental shelf shows that monthly mean POC concentrations derived from MERIS with the new algorithm are consistent with those derived from the 2016 algorithm of Le et al. which was specifically developed for this region.

Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI

The monitoring of Chlorophyll-a (Chl-a) concentration in high northern latitude waters has been receiving increased focus due to the rapid environmental changes in the sub-Arctic, Arctic. Spaceborne optical instruments allow the continuous monitoring of the occurrence, distribution, and amount of Chl-a. In recent years, the Ocean and Land Color Instruments (OLCI) onboard the Sentinel 3 (S3) A and B satellites were launched, which provide data about various aquatic environments on advantageous spatial, spectral, and temporal resolutions with high SNR. Although S3 OLCI could be favorable to monitor high northern latitude waters, there have been several challenges related to Chl-a concentration retrieval in these waters due to their unique optical properties coupled with challenging environments including high sun zenith angle, presence of sea ice, and frequent cloud covers. In this work, we aim to overcome these difficulties by developing a machine-learning (ML) approach designed to estimate Chl-a concentration from S3 OLCI data in high northern latitude optically complex waters. The ML model is optimized and requires only three S3 OLCI bands, reflecting the physical characteristic of Chl-a as input in the regression process to estimate Chl-a concentration with improved accuracy in terms of the bias (five times improvements.) The ML model was optimized on data from Arctic, coastal, and open waters, and showed promising performance. Finally, we present the performance of the optimized ML approach by computing Chl-a maps and corresponding certainty maps in highly complex sub-Arctic and Arctic waters. We show how these certainty maps can be used as a support to understand possible radiometric calibration issues in the retrieval of Level 2 reflectance over these waters. This can be a useful tool in identifying erroneous Level 2 Remote sensing reflectance due to possible failure of the atmospheric correction algorithm.

High colored dissolved organic matter (CDOM) absorption in surface waters of the central-eastern Arctic Ocean: Implications for biogeochemistry and ocean color algorithms

As consequences of global warming sea-ice shrinking, permafrost thawing and changes in fresh water and terrestrial material export have already been reported in the Arctic environment. These processes impact light penetration and primary production. To reach a better understanding of the current status and to provide accurate forecasts Arctic biogeochemical and physical parameters need to be extensively monitored. In this sense, bio-optical properties are useful to be measured due to the applicability of optical instrumentation to autonomous platforms, including satellites. This study characterizes the non-water absorbers and their coupling to hydrographic conditions in the poorly sampled surface waters of the central and eastern Arctic Ocean. Over the entire sampled area colored dissolved organic matter (CDOM) dominates the light absorption in surface waters. The distribution of CDOM, phytoplankton and non-algal particles absorption reproduces the hydrographic variability in this region of the Arctic Ocean which suggests a subdivision into five major bio-optical provinces: Laptev Sea Shelf, Laptev Sea, Central Arctic/Transpolar Drift, Beaufort Gyre and Eurasian/Nansen Basin. Evaluating ocean color algorithms commonly applied in the Arctic Ocean shows that global and regionally tuned empirical algorithms provide poor chlorophyll-a (Chl-a) estimates. The semi-analytical algorithms Generalized Inherent Optical Property model (GIOP) and Garver-Siegel-Maritorena (GSM), on the other hand, provide robust estimates of Chl-a and absorption of colored matter. Applying GSM with modifications proposed for the western Arctic Ocean produced reliable information on the absorption by colored matter, and specifically by CDOM. These findings highlight that only semi-analytical ocean color algorithms are able to identify with low uncertainty the distribution of the different optical water constituents in these high CDOM absorbing waters. In addition, a clustering of the Arctic Ocean into bio-optical provinces will help to develop and then select province-specific ocean color algorithms.

Effects of sea ice cover on satellite-detected primary production in the Arctic Ocean

The influence of decreasing Arctic sea ice on net primary production (NPP) in the Arctic Ocean has been considered in multiple publications but is not well constrained owing to the potentially large errors in satellite algorithms. In particular, the Arctic Ocean is rich in coloured dissolved organic matter (CDOM) that interferes in the detection of chlorophyll a concentration of the standard algorithm, which is the primary input to NPP models. We used the quasi-analytic algorithm (Lee et al 2002 Appl. Opti. 41, 5755-5772. (doi:10.1364/AO.41.005755)) that separates absorption by phytoplankton from absorption by CDOM and detrital matter. We merged satellite data from multiple satellite sensors and created a 19 year time series (1997-2015) of NPP. During this period, both the estimated annual total and the summer monthly maximum pan-Arctic NPP increased by about 47%. Positive monthly anomalies in NPP are highly correlated with positive anomalies in open water area during the summer months. Following the earlier ice retreat, the start of the high-productivity season has become earlier, e.g. at a mean rate of -3.0 d yr^-1 in the northern Barents Sea, and the length of the high-productivity period has increased from 15 days in 1998 to 62 days in 2015. While in some areas, the termination of the productive season has been extended, owing to delayed ice formation, the termination has also become earlier in other areas, likely owing to limited nutrients.

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production. Despite this, the Earth's surface waters are impacted by multiple natural and anthropogenic pressures and drivers of environmental change. The complex interaction between physical, chemical and biological processes in surface waters poses significant challenges for in situ monitoring and assessment and often limits our ability to adequately capture the dynamics of aquatic systems and our understanding of their status, functioning and response to pressures. Here we explore the opportunities that Earth observation (EO) has to offer to basin-scale monitoring of water quality over the surface water continuum comprising inland, transition and coastal water bodies, with a particular focus on the Danube and Black Sea region. This review summarises the technological advances in EO and the opportunities that the next generation satellites offer for water quality monitoring. We provide an overview of algorithms for the retrieval of water quality parameters and demonstrate how such models have been used for the assessment and monitoring of inland, transitional, coastal and shelf-sea systems. Further, we argue that very few studies have investigated the connectivity between these systems especially in large river-sea systems such as the Danube-Black Sea. Subsequently, we describe current capability in operational processing of archive and near real-time satellite data. We conclude that while the operational use of satellites for the assessment and monitoring of surface waters is still developing for inland and coastal waters and more work is required on the development and validation of remote sensing algorithms for these optically complex waters, the potential that these data streams offer for developing an improved, potentially paradigm-shifting understanding of physical and biogeochemical processes across large scale river-sea systems including the Danube-Black Sea is considerable.

Assessment of the colored dissolved organic matter in coastal waters from ocean color remote sensing

Knowledge on absorption by colored dissolved organic matter, a(cdom), spatio-temporal variability in coastal areas is of fundamental importance in many field of researches related to biogeochemical cycles studies, coastal areas management, as well as land and water interactions in the coastal domain. A new method, based on the theoretical link between the vertical attenuation coefficient, K(d), and the absorption coefficient, has been developed to assess a(cdom). This method, confirmed from radiative transfer simulations and in situ measurements, and tested on an independent in situ data set (N = 126), allows a(cdom) to be assessed with a Mean Relative Absolute Difference, MRAD, of 33% over two order of magnitude (from 0.01 to 1.16 m(-1)). In the frame of ocean color observation, K(d) is not directly measured but estimated from the remote sensing reflectance, R(rs). Based on 109 satellite (SeaWiFS) and in situ coincident (i.e. match-up) data points a(cdom) is retrieved with a MRAD value of 37%. This simple model generally presents slightly better performances than recently developed empirical or semi-analytical algorithms.

The global distribution and dynamics of chromophoric dissolved organic matter

Chromophoric dissolved organic matter (CDOM) is a ubiquitous component of the open ocean dissolved matter pool, and is important owing to its influence on the optical properties of the water column, its role in photochemistry and photobiology, and its utility as a tracer of deep ocean biogeochemical processes and circulation. In this review, we discuss the global distribution and dynamics of CDOM in the ocean, concentrating on developments in the past 10 years and restricting our discussion to open ocean and deep ocean (below the main thermocline) environments. CDOM has been demonstrated to exert primary control on ocean color by its absorption of light energy, which matches or exceeds that of phytoplankton pigments in most cases. This has important implications for assessing the ocean biosphere via ocean color-based remote sensing and the evaluation of ocean photochemical and photobiological processes. The general distribution of CDOM in the global ocean is controlled by a balance between production (primarily microbial remineralization of organic matter) and photolysis, with vertical ventilation circulation playing an important role in transporting CDOM to and from intermediate water masses. Significant decadal-scale fluctuations in the abundance of global surface ocean CDOM have been observed using remote sensing, indicating a potentially important role for CDOM in ocean-climate connections through its impact on photochemistry and photobiology.