Search

Satellite-derived ocean colour data provide continuous, daily measurements of global waters and are an essential tool for monitoring these waters in a changing climate. Merging observations from different satellite sensors is necessary for long-term and continuous climate research because the lifetime of these sensors is limited. A key issue in deriving long-term trends from merged ocean colour data is the inconsistency between the spatiotemporal coverage of the different sensor datasets that can lead to spurious multi-year fluctuations or trends in the time series. This study used the merged ocean colour satellite dataset produced by the Ocean Colour Climate Change Initiative (OC-CCI version 6.0) to infer global and local trends in optically active constituents. We applied a novel correction method to the OC-CCI dataset that results in a spatiotemporally consistent dataset, allowing the examination of long-term trends of optically active constituents with greater accuracy. We included sea surface temperature, salinity, and several climate oscillations in our analysis to gain insight into the underlying processes of derived trends. Our results indicate a significant increase in chlorophyll-a concentration in the polar waters, a decrease in chlorophyll-a concentration in some equatorial waters, and point to ocean darkening, predominantly in the polar waters, due to an increase in non-phytoplankton absorption. This study contributes to broader knowledge of global trends of optically active constituents and their relation to a changing environment.

Consistency in a time series of ocean colour satellite data is essential when determining long-term trends and statistics in Essential Climate Variables. For such a long time series, it is necessary to merge ocean colour data sets from different sensors due to the finite life span of the satellites. Although bias corrections have been performed on merged data set products, significant inconsistencies between missions remain. These inconsistencies appear as sudden steps in the time series of these products when a satellite mission is launched into- or removed from orbit. This inter-mission inconsistency is not caused by poor correction of sensor sensitivities but by differences in the ability of a sensor to observe certain waters. This study, based on a data set compiled by the ‘Ocean Colour Climate Change Initiative’ project (OC-CCI), shows that coastal waters, high latitudes, and areas subject to changing cloud cover are most affected by coverage variability between missions. The “Temporal Gap Detection Method” is introduced, which temporally homogenises the observations per-pixel of the time series and consequently minimises the magnitude of the inter-mission inconsistencies. The method presented is suitable to be transferred to other merged satellite-derived data sets that exhibit inconsistencies due to changes in coverage over time. The results provide insights into the correct interpretation of any merged ocean colour time series.

Horizontal patchiness of water quality attributes in lakes substantially influences the ability to accurately determine an average condition of a lake from traditional in situ sampling. Monitoring programmes for lake water quality often rely on water samples from one or few locations but the assumption of representativeness is seldomly tested. Satellite observations can support environmental monitoring by detecting horizontal variability of water quality attributes over entire lakes. This article is a co-submission with Lehmann et al. (2021), who present a method to create a regional calibration of a satellite chlorophyll a algorithm and a spatial analysis of an image time series to detect recurring patchiness. Our method was developed on 13 lakes in the central North Island of New Zealand and this publication makes available the data used in our analysis and the spatial fields of results. These data are immediately valuable for practitioners operating within the region of interest providing a five year archive of synoptic water quality data and spatial fields to help optimize in situ monitoring efforts. In addition, there is value to the wider scientific community as the study lakes are a useful ‘natural lab’ for the development of aquatic remote sensing methods due to the range of trophic conditions and water colour in a single satellite image scene. Together with decades of in situ water quality records, our data is therefore useful for the development and validation of widely applicable methods of water quality retrieval from satellite data.

Horizontal patchiness of water quality attributes in lakes substantially influences the ability to accurately determine an average condition of a lake from traditional in situ sampling. Therefore, spatial variability has to be accounted for in monitoring programmes which aim at determining the states and trends of ecosystem attributes. We used five years of Sentinel-2 Multispectral Instrument (MSI) data and conducted spatial analyses of surface chlorophyll a (Chl) concentration to map its variability and provide concrete recommendations for resource managers to design in situ sampling programmes. First, we developed a regional calibration of Chl predictions by C2RCC, an openly available processor for atmospheric corrections and water constituent retrieval, using in situ data from eleven temperate lakes in the central North Island of Aotearoa New Zealand. Using 93 match-up samples, we re-fitted C2RCC’s partitioning of constituent absorption coefficients to achieve an improved prediction accuracy for Chl (r2 = 0.79, root mean square error = 5.4 mg m−3). The new relationship was applied to all cloud-free images for thirteen regional lakes for further spatial analysis. We found that the medians calculated within areas of different sizes around in situ sampling locations may increase or decrease, illustrating an unpredictable uncertainty of the representativeness of any in situ sample. We went on to summarise five years of spatial variability by assessing each pixel for its tendency to be near the lake median Chl, higher (near the upper quartile) or lower (near the lower quartile). This spatiotemporal analysis revealed recurring patchiness that we converted to an indication of the representativeness of any location in the lake useful for the selection of more representative sites for future monitoring programmes.

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and inter-annual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided.

The emergence of hyperspectral optical satellite sensors for ocean observation provides potential for more detailed information from aquatic ecosystems. The German hyperspectral satellite mission EnMAP (enmap.org) currently in the production phase is supported by a project to explore the capability of using EnMAP data and other future hyperspectral data from space. One task is to identify phytoplankton taxonomic groups. To fulfill this objective, on the basis of laboratory-measured absorption coefficients of phytoplankton cultures (aph(λ)) and corresponding simulated remote sensing reflectance spectra (Rrs(λ)), we examined the performance of spectral fourth-derivative analysis and clustering techniques to differentiate six taxonomic groups. We compared different sources of input data, namely aph(λ), Rrs(λ), and the absorption of water compounds obtained from inversion of the Rrs(λ)) spectra using a quasi-analytical algorithm (QAA). Rrs(λ) was tested as it can be directly obtained from hyperspectral sensors. The last one was tested as expected influences of the spectral features of pure water absorption on Rrs(λ) could be avoided after subtracting it from the inverted total absorption. Results showed that derivative analysis of measured aph(λ) spectra performed best with only a few misclassified cultures. Based on Rrs(λ) spectra, the accuracy of this differentiation decreased but the performance was partly restored if wavelengths of strong water absorption were excluded and chlorophyll concentrations were higher than 1 mg∙m−3. When based on QAA-inverted absorption spectra, the differentiation was less precise due to loss of information at longer wavelengths. This analysis showed that, compared to inverted absorption spectra from restricted inversion models, hyperspectral Rrs(λ) is potentially suitable input data for the differentiation of phytoplankton taxonomic groups in prospective EnMAP applications, though still a challenge at low algal concentrations.

Chlorophyll (chl a) concentration in coastal seas exhibits variability on various spatial and temporal scales. Resuspension of particulate matter can somewhat limit algal growth, but can also enhance productivity because of the intrusion of nutrient-rich pore water from sediments or bottom water layers into the whole water column. This study investigates whether characteristic changes in net phytoplankton growth can be directly linked to resuspension events within the German Bight. Satellite-derived chl a were used to derive spatial patterns of net rates of chl a increase/decrease (NR) in 2003 and 2004. Spatial correlations between NR and mean water column irradiance were analysed. High correlations in space and time were found in most areas of the German Bight (R2 > 0.4), suggesting a tight coupling between light availability and algal growth during spring. These correlations were reduced within a distinct zone in the transition between shallow coastal areas and deeper offshore waters. In summer and autumn, a mismatch was found between phytoplankton blooms (chl a > 6 mg m−3) and spring-tidal induced resuspension events as indicated by bottom velocity, suggesting that there is no phytoplankton resuspension during spring tides. It is instead proposed here that frequent and recurrent spring-tidal resuspension events enhance algal growth by supplying remineralized nutrients. This hypothesis is corroborated by a lag correlation analysis between resuspension events and in-situ measured nutrient concentrations. This study outlines seasonally different patterns in phytoplankton productivity in response to variations in resuspension, which can serve as a reference for modelling coastal ecosystem dynamics.

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

The differences among phytoplankton carbon (Cphy) predictions from six ocean color algorithms are investigated by comparison with in situ estimates of phytoplankton carbon. The common satellite data used as input for the algorithms is the Ocean Color Climate Change Initiative merged product. The matching in situ data are derived from flow cytometric cell counts and per-cell carbon estimates for different types of pico-phytoplankton. This combination of satellite and in situ data provides a relatively large matching dataset (N > 500), which is independent from most of the algorithms tested and spans almost two orders of magnitude in Cphy. Results show that not a single algorithm outperforms any of the other when using all matching data. Concentrating on the oligotrophic regions (Chlorophyll-a concentration, B, less than 0.15 mg Chl m−3), where flow cytometric analysis captures most of the phytoplankton biomass, reveals significant differences in algorithm performance. The bias ranges from −35 to +150% and unbiased root mean squared difference from 5 to 10 mg C m−3 among algorithms, with chlorophyll-based algorithms performing better than the rest. The backscattering-based algorithms produce different results at the clearest waters and these differences are discussed in terms of the different algorithms used for optical particle backscattering coefficient (bbp) retrieval.

Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.

In the present study we investigate the bio-geo-optical boundaries for the possibility to identify dominant phytoplankton groups from hyperspectral ocean color data. A large dataset of simulated remote sensing reflectance spectra, Rrs(λ), was used. The simulation was based on measured inherent optical properties of natural water and measurements of five phytoplankton light absorption spectra representing five major phytoplankton spectral groups. These simulated data, named as C2X data, contain more than 105 different water cases, including cases typical for clearest natural waters as well as for extreme absorbing and extreme scattering waters. For the simulation the used concentrations of chlorophyll a (representing phytoplankton abundance), Chl, are ranging from 0 to 200 mg m−3, concentrations of non-algal particles, NAP, from 0 to 1,500 g m−3, and absorption coefficients of chromophoric dissolved organic matter (CDOM) at 440 nm from 0 to 20 m−1. A second, independent, smaller dataset of simulated Rrs(λ) used light absorption spectra of 128 cultures from six phytoplankton taxonomic groups to represent natural variability. Spectra of this test dataset are compared with spectra from the C2X data in order to evaluate to which extent the five spectral groups can be correctly identified as dominant under different optical conditions. The results showed that the identification accuracy is highly subject to the water optical conditions, i.e., contribution of and covariance in Chl, NAP, and CDOM. The identification in the simulated data is generally effective, except for waters with very low contribution by phytoplankton and for waters dominated by NAP, whereas contribution by CDOM plays only a minor role. To verify the applicability of the presented approach for natural waters, a test using in situ Rrs(λ) dataset collected during a cyanobacterial bloom in Lake Taihu (China) is carried out and the approach predicts blue cyanobacteria to be dominant. This fits well with observation of the blue cyanobacteria Microcystis sp. in the lake. This study provides an efficient approach, which can be promisingly applied to hyperspectral sensors, for identifying dominant phytoplankton spectral groups purely based on Rrs(λ) spectra.

Particulate Organic Carbon (POC) plays a vital role in the ocean carbon cycle. Though relatively small compared with other carbon pools, the POC pool is responsible for large fluxes and is linked to many important ocean biogeochemical processes. The satellite ocean-color signal is influenced by particle composition, size, and concentration and provides a way to observe variability in the POC pool at a range of temporal and spatial scales. To provide accurate estimates of POC concentration from satellite ocean color data requires algorithms that are well validated, with uncertainties characterized. Here, a number of algorithms to derive POC using different optical variables are applied to merged satellite ocean color data provided by the Ocean Color Climate Change Initiative (OC-CCI) and validated against the largest database of in situ POC measurements currently available. The results of this validation exercise indicate satisfactory levels of performance from several algorithms (highest performance was observed from the algorithms of Loisel et al., 2002; Stramski et al., 2008) and uncertainties that are within the requirements of the user community. Estimates of the standing stock of the POC can be made by applying these algorithms, and yield an estimated mixed-layer integrated global stock of POC between 0.77 and 1.3 Pg C of carbon. Performance of the algorithms vary regionally, suggesting that blending of region-specific algorithms may provide the best way forward for generating global POC products.

Chromosomal rearrangements are important drivers in cancer, and their robust detection is essential for diagnosis, prognosis, and treatment selection, particularly for bone and soft tissue tumors. Current diagnostic methods are hindered by limitations, including difficulties with multiplexing targets and poor quality of RNA. A novel targeted DNA-based next-generation sequencing method, formalin-fixed, paraffin-embedded-targeted locus capture (FFPE-TLC), has shown advantages over current diagnostic methods when applied on FFPE lymphomas, including the ability to detect novel rearrangements. We evaluated the utility of FFPE-TLC in bone and soft tissue tumor diagnostics. FFPE-TLC sequencing was successfully applied on noncalcified and decalcified FFPE samples (n = 44) and control samples (n = 19). In total, 58 rearrangements were identified in 40 FFPE tumor samples, including three previously negative samples, and none was identified in the FFPE control samples. In all five discordant cases, FFPE-TLC could identify gene fusions where other methods had failed due to either detection limits or poor sample quality. FFPE-TLC achieved a high specificity and sensitivity (no false positives and negatives). These results indicate that FFPE-TLC is applicable in cancer diagnostics to simultaneously analyze many genes for their involvement in gene fusions. Similar to the observation in lymphomas, FFPE-TLC is a good DNA-based alternative to the conventional methods for detection of rearrangements in bone and soft tissue tumors., (Copyright © 2023 Association for Molecular Pathology and American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

Targeted locus amplification (TLA) allows for the detection of all genetic variation (including structural variation) in a genomic region of interest. As TLA is based on proximity ligation, variants can be linked to each other, thereby enabling allelic phasing and the generation of haplotypes. This allows for the study of genetic variants in an allele-specific manner. Here, we provide a step-by-step protocol for TLA sample preparation and a complete bioinformatics pipeline for the allelic phasing of TLA data. Additionally, to illustrate the protocol, we show the ability of TLA to re-sequence and haplotype the complete cystic fibrosis transmembrane (CFTR) gene (> 200 kb in size) from patient-derived intestinal organoids., (© 2023. The Author(s).)

In routine diagnostic pathology, cancer biopsies are preserved by formalin-fixed, paraffin-embedding (FFPE) procedures for examination of (intra-) cellular morphology. Such procedures inadvertently induce DNA fragmentation, which compromises sequencing-based analyses of chromosomal rearrangements. Yet, rearrangements drive many types of hematolymphoid malignancies and solid tumors, and their manifestation is instructive for diagnosis, prognosis, and treatment. Here, we present FFPE-targeted locus capture (FFPE-TLC) for targeted sequencing of proximity-ligation products formed in FFPE tissue blocks, and PLIER, a computational framework that allows automated identification and characterization of rearrangements involving selected, clinically relevant, loci. FFPE-TLC, blindly applied to 149 lymphoma and control FFPE samples, identifies the known and previously uncharacterized rearrangement partners. It outperforms fluorescence in situ hybridization (FISH) in sensitivity and specificity, and shows clear advantages over standard capture-NGS methods, finding rearrangements involving repetitive sequences which they typically miss. FFPE-TLC is therefore a powerful clinical diagnostics tool for accurate targeted rearrangement detection in FFPE specimens.

We present TaxPhlAn, a new method and bioinformatics pipeline for design and analysis of single-locus sequence typing (SLST) markers to type and profile bacteria beyond the species-level in a complex microbial community background. TaxPhlAn can be applied to any group of phylogenetically-related bacteria, provided reference genomes are available. As TaxPhlAn requires the SLST targets identified to fit the phylogenetic pattern as determined through comprehensive evolutionary reconstruction of input genomes, TaxPhlAn allows for the identification and phylogenetic inference of new biodiversity. Here, we present a clinically relevant case study of high-resolution Staphylococcus profiling on skin of atopic dermatitis (AD) patients. We demonstrate that SLST enables profiling of cutaneous Staphylococcus members at (sub)species level and provides higher resolution than current 16S-based techniques. With the higher discriminative ability provided by our approach, we further show that the presence of Staphylococcus capitis on the skin together with Staphylococcus aureus associates with AD disease.

Lactobacillus delbrueckii subsp. bulgaricus LBB.B5 originates from homemade Bulgarian yogurt and was selected for its ability to form a strong association with Streptococcus thermophilus The genome sequence will facilitate elucidating the genetic background behind the contribution of LBB.B5 to the taste and aroma of yogurt and its exceptional protocooperation with S. thermophilus., (Copyright © 2016 Urshev et al.)