Hybrid Inversion Algorithms for Retrieval of Absorption Subcomponents from Ocean Colour Remote Sensing Reflectance

Semi-analytical algorithms (SAAs) invert spectral remote sensing reflectance (Rrs(λ), sr−1) to Inherent Optical Properties (IOPs) of an aquatic medium (λ is the wavelength). Existing SAAs implement different methodologies with a range of spectral IOP models and inversion methods producing concentrations of non-water constituents. Absorption spectrum decomposition algorithms (ADAs) are a set of algorithms developed to partition anw(λ), m−1 (i.e., the light absorption coefficient without pure water absorption), into absorption subcomponents of phytoplankton (aph(λ), m−1) and coloured detrital matter (adg(λ), m−1). Despite significant developments in ADAs, their applicability to remote sensing applications is rarely studied. The present study formulates hybrid inversion approaches that combine SAAs and ADAs to derive absorption subcomponents from Rrs(λ) and explores potential alternatives to operational SAAs. Using Rrs(λ) and concurrent absorption subcomponents from four datasets covering a wide range of optical properties, three operational SAAs, i.e., Garver–Siegel–Maritorena (GSM), Quasi-Analytical Algorithm (QAA), Generalized Inherent Optical Property (GIOP) model are evaluated in deriving anw(λ) from Rrs(λ). Among these three models, QAA and GIOP models derived anw(λ) with lower errors. Among six distinctive ADAs tested in the study, the Generalized Stacked Constraints Model (GSCM) and Zhang’s model-derived absorption subcomponents achieved lower average spectral mean absolute percentage errors (MAPE) in the range of 8–38%. Four hybrid models, GIOPGSCM, GIOPZhang, QAAGSCM and QAAZhang, formulated using the SAAs and ADAs, are compared for their absorption subcomponent retrieval performance from Rrs(λ). GIOPGSCM and GIOPZhang models derived absorption subcomponents have lower errors than GIOP and QAA. Potential uncertainties associated with datasets and dependency of algorithm performance on datasets were discussed.

Trichome abundance, chlorophyll content and the spectral coefficient for light absorption of Trichodesmium slicks observed in the Southwestern Atlantic

Dense slicks of Trichodesmium were found in the shelf-break region in the Southwestern Atlantic during austral spring and autumn. A total of 14 slicks were sampled, and the absorption coefficients of phytoplankton (aph(λ)) indicated clear spectral features of phycobilin pigments. Although these samples showed low-degradation products and detrital importance, the chemotaxonomy, shape, and magnitude of aph(λ) indicated the importance of co-occurring species in the slicks. In addition to the difficulties of enumerating trichomes in situ, co-occurring species affect the expected chlorophyll-a (Chl-a) to trichome ratio, further complicating the detection of Trichodesmium by ocean colour remote sensing. Our results showed that trichome density could be predicted similarly by Chl-a and by aph(621), especially for trichome densities above 8000 trichomes L−1. The phycocyanin spectral feature is a potential source of quantitative information for the detection of Trichodesmium, but noninvasive techniques for quantifying the abundance of Trichodesmium in natural waters are necessary.

ESTIMATION OF OPTICAL PROPERTIES USING QAA-V6 MODEL BASED ON MODIS DATA

Optical property parameters play an important role in ocean colour studies. As a key variable, the absorption coefficient is of great significance for calculating the content of each component in water and simulating the physical, chemical and biological properties of water. The inversion algorithms mainly include empirical model, semi-analytical model and neural network model. In this study, we focused on the QAA_V6, which is the newest version of Quasi-Analytical Algorithm (QAA). It is necessary to test the QAA_V6 model in different conditions. IOCCG data set is used to verify the accuracy of QAA_V6. Additionally, MODIS data of case 1 waters and case 2 waters were selected. After extraction and matching, the data was finally imported into QAA_V6 model to calculate the absorption coefficient with a R2 of 0.999 in both case 1 waters and case 2 waters, thus the QAA_V6 model showed a high accuracy and robust applicability in the inversion of inherent optical properties. Subsequently, it can be further verified for the waters in more complicated areas, providing a firm foundation for implementing QAA into the research of ocean colour remote sensing.

Hyperspectral ultraviolet to shortwave infrared characteristics of marine-harvested, washed-ashore and virgin plastics

Combating the imminent environmental problems associated with plastic litter requires a synergy of monitoring strategies, clean-up efforts, policymaking and interdisciplinary scientific research. Lately, remote sensing technologies have been evolving into a complementary monitoring strategy that might have future applications in the operational detection and tracking of plastic litter at repeated intervals covering wide geospatial areas. We therefore present a dataset of Lambertian-equivalent spectral reflectance measurements from the ultraviolet (UV, 350 nm) to shortwave infrared (SWIR, 2500 nm) of synthetic hydrocarbons (plastics). Spectral reflectance of wet and dry marine-harvested, washed-ashore, and virgin plastics was measured outdoors with a hyperspectral spectroradiometer. Samples were harvested from the major accumulation zones in the Atlantic and Pacific oceans, suggesting a near representation of plastic litter in global oceans. We determined a representative bulk average spectral reflectance for the dry marine-harvested microplastics dataset available at https://doi.org/10.21232/jyxq-1m66 (Garaba and Dierssen, 2019c). Similar absorption features were identified in the dry samples of washed-ashore plastics: dataset available at https://doi.org/10.21232/ex5j-0z25 (Garaba and Dierssen, 2019a). The virgin pellets samples consisted of 11 polymer types typically found in floating aquatic plastic litter: dataset available at https://doi.org/10.21232/C27H34 (Garaba and Dierssen, 2017). Magnitude and shape features of the spectral reflectance collected were also evaluated for two scenarios involving dry and wet marine-harvested microplastics: dataset available at https://doi.org/10.21232/r7gg-yv83 (Garaba and Dierssen, 2019b). Reflectance of wet marine-harvested microplastics was noted to be lower in magnitude but had similar spectral shape to that of dry marine-harvested microplastics. Diagnostic absorption features common in the marine-harvested microplastics and washed-ashore plastics were identified at ∼931, 1215, 1417 and 1732 nm. In addition, we include metrics for a subset of the marine-harvested microplastics related to particle morphology, including sphericity and roundness. These datasets are also expected to improve and expand the scientific evidence-based knowledge of optical characteristics of common plastics found in aquatic litter. Furthermore, these datasets have potential use in radiative transfer simulations exploring the effects of plastics on ocean colour remote sensing and developing algorithms applicable to remote detection of floating plastic litter.

Hyperspectral ultraviolet to shortwave infrared characteristics of marine-harvested, washed ashore and virgin plastics

Combating the imminent environmental problems associated with plastic litter requires a synergy of monitoring strategies, clean-up efforts, policymaking and interdisciplinary scientific research. Lately, remote sensing technologies have been evolving into a complementary environmental monitoring approach that might have future applications in the operational detection and tracking of plastic litter at repeated intervals covering wide geo-spatial areas. We therefore present a dataset of spectral reflectance measurements from the ultraviolet (350 nm) to shortwave infrared (2500 nm) of synthetic hydrocarbons (plastics). Spectral reflectance of wet and dry marine-harvested, washed ashore and virgin plastics was measured outdoors with a hyperspectral spectroradiometer. Samples were harvested from the major accumulation zones in the Atlantic and Pacific ocean suggesting a near representation of plastic litter in global oceans. We determined a representative bulk average spectral reflectance for the dry marine-harvested microplastics and identified common absorption features at ~ 931, 1215, 1417 and 1732 nm, dataset available at https://doi.org/10.21232/jyxq-1m66 (Garaba and Dierssen, 2019a). Similar absorption features were identified in the dry samples of washed ashore plastics, dataset available at https://doi.org/10.21232/ex5j-0z25 (Garaba and Dierssen, 2019b). The virgin pellets samples consisted of eleven polymer types typically found in floating aquatic plastic litter, dataset available at https://doi.org/10.21232/C27H34 (Garaba and Dierssen, 2017). Magnitude and shape features of the spectral reflectance collected were also evaluated for two scenarios involving dry and wet marine-harvested microplastics, dataset available at https://doi.org/10.21232/r7gg-yv83 (Garaba and Dierssen, 2019c). Reflectance of wet marine-harvested microplastics was noted to be lower in magnitude but had similar spectral shape to the one of dry marine-harvested microplastics. In addition, we include metrics for microplastic particle morphology including sphericity and roundness. These open-access datasets will be useful in radiative transfer analyses exploring the effect of plastics to ocean colour remote sensing and developing algorithms applicable to remote detection of floating plastic litter. The dataset is expected to improve and expand the scientific evidence-based knowledge on optical characteristics of common plastics found in aquatic litter.

Dissolved organic matter at the fluvial–marine transition in the Laptev Sea using in situ data and ocean colour remote sensing

River water is the main source of dissolved organic carbon (DOC) in the Arctic Ocean. DOC plays an important role in the Arctic carbon cycle, and its export from land to sea is expected to increase as ongoing climate change accelerates permafrost thaw. However, transport pathways and transformation of DOC in the land-to-ocean transition are mostly unknown. We collected DOC and aCDOM(λ) samples from 11 expeditions to river, coastal and offshore waters and present a new DOC–aCDOM(λ) model for the fluvial–marine transition zone in the Laptev Sea. The aCDOM(λ) characteristics revealed that the dissolved organic matter (DOM) in samples of this dataset are primarily of terrigenous origin. Observed changes in aCDOM(443) and its spectral slopes indicate that DOM is modified by microbial and photo-degradation. Ocean colour remote sensing (OCRS) provides the absorption coefficient of coloured dissolved organic matter (aCDOM(λ)sat) at λ=440 or 443 nm, which can be used to estimate DOC concentration at high temporal and spatial resolution over large regions. We tested the statistical performance of five OCRS algorithms and evaluated the plausibility of the spatial distribution of derived aCDOM(λ)sat. The OLCI (Sentinel-3 Ocean and Land Colour Instrument) neural network swarm (ONNS) algorithm showed the best performance compared to in situ aCDOM(440) (r2=0.72). Additionally, we found ONNS-derived aCDOM(440), in contrast to other algorithms, to be partly independent of sediment concentration, making ONNS the most suitable aCDOM(λ)sat algorithm for the Laptev Sea region. The DOC–aCDOM(λ) model was applied to ONNS-derived aCDOM(440), and retrieved DOC concentration maps showed moderate agreement to in situ data (r2=0.53). The in situ and satellite-retrieved data were offset by up to several days, which may partly explain the weak correlation for this dynamic region. Satellite-derived surface water DOC concentration maps from Medium Resolution Imaging Spectrometer (MERIS) satellite data demonstrate rapid removal of DOC within short time periods in coastal waters of the Laptev Sea, which is likely caused by physical mixing and different types of degradation processes. Using samples from all occurring water types leads to a more robust DOC–aCDOM(λ) model for the retrievals of DOC in Arctic shelf and river waters.

Impact of Coastal Infrastructure on Ocean Colour Remote Sensing: A Case Study in Jiaozhou Bay, China

Spatial and temporal ocean colour data are increasingly accessible through remote sensing, which is a key tool for evaluating coastal biogeochemical and physical processes, and for monitoring water quality. Coastal infrastructure such as cross-sea bridges may impact ocean colour remote sensing due to the different spectral characteristics of asphalt and the seawater surface. However, this potential impact is typically ignored during data post-processing. In this study, we use Jiaozhou Bay (East China) and its cross-bay bridge to examine the impact of coastal infrastructure on water-quality remote-sensing products, in particular on chlorophyll-a concentration and total suspended sediment. The values of these products in the bridge area were significantly different to those in the adjacent water. Analysis of the remote-sensing reflectance and application of the Normalised Difference Water Index demonstrate that this phenomenon is caused by contamination of the signal by bridge pixels. The Moderate Resolution Imaging Spectroradiometer (MODIS) products helped estimate the pixel scale that could be influenced by contamination. Furthermore, we found similar pixel contamination at Hangzhou Bay Bridge, suggesting that the impact of large coastal infrastructure on ocean colour data is common, and must therefore be considered in data post-processing.