Volcanic ash supply to the surface ocean – remote sensing of biological responses and their wider biogeochemical significance

Transient micronutrient enrichment of the surface ocean can enhance phytoplankton growth rates and alter microbial community structure with an ensuing spectrum of biogeochemical feedbacks. Strong phytoplankton responses to micronutrients supplied by volcanic ash have been reported recently. Here we: (i) synthesize findings from these recent studies; (ii) report the results of a new remote sensing study of ash fertilization; and (iii) calculate theoretical bounds of ash-fertilized carbon export. Our synthesis highlights that phytoplankton responses to ash do not always simply mimic that of iron amendment; the exact mechanisms for this are likely biogeochemically important but are not yet well understood. Inherent optical properties of ash-loaded seawater suggest rhyolitic ash biases routine satellite chlorophyll-a estimation upwards by more than an order of magnitude for waters with 0.5 mg chlorophyll-a m-3. For this reason post-ash-deposition chlorophyll-a changes in oligotrophic waters detected via standard Case 1 (open ocean) algorithms should be interpreted with caution. Remote sensing analysis of historic events with a bias less than a factor of 2 provided limited stand-alone evidence for ash-fertilization. Confounding factors were poor coverage, incoherent ash dispersal, and ambiguity ascribing biomass changes to ash supply over other potential drivers. Using current estimates of iron release and carbon export efficiencies, uncertainty bounds of ash-fertilized carbon export for 3 events are presented. Patagonian iron supply to the Southern Ocean from volcanic eruptions is less than that of windblown dust on thousand year timescales but can dominate supply at shorter timescales. Reducing uncertainties in remote sensing of phytoplankton response and nutrient release from ash are avenues for enabling assessment of the oceanic response to large-scale transient nutrient enrichment

Earth observation tool for monitoring coastal eutrophication

ISECA is an Interreg project running until September 2014 that aims to advance and disseminate scientific knowledge related to eutrophication in the 2Seas area (English Channel and North Sea).The main objective of ISECA is to develop a demonstration prototype of an information system for monitoring eutrophication of coastal waters.This information system combines in-situ, satellite information and models outputs

Detection and Classification of Floating Plastic Litter Using a Vessel-Mounted Video Camera and Deep Learning

Marine plastic pollution is a major environmental concern, with significant ecological, economic, public health and aesthetic consequences. Despite this, the quantity and distribution of marine plastics is poorly understood. Better understanding of the global abundance and distribution of marine plastic debris is vital for global mitigation and policy. Remote sensing methods could provide substantial data to overcome this issue. However, developments have been hampered by the limited availability of in situ data, which are necessary for development and validation of remote sensing methods. Current in situ methods of floating macroplastics (size greater than 1 cm) are usually conducted through human visual surveys, often being costly, time-intensive and limited in coverage. To overcome this issue, we present a novel approach to collecting in situ data using a trained object-detection algorithm to detect and quantify marine macroplastics from video footage taken from vessel-mounted general consumer cameras. Our model was able to successfully detect the presence or absence of plastics from real-world footage with an accuracy of 95.2% without the need to pre-screen the images for horizon or other landscape features, making it highly portable to other environmental conditions. Additionally, the model was able to differentiate between plastic object types with a Mean Average Precision of 68% and an F1-Score of 0.64. Further analysis suggests that a way to improve the separation among object types using only object detection might be through increasing the proportion of the image area covered by the plastic object. Overall, these results demonstrate how low-cost vessel-mounted cameras combined with machine learning have the potential to provide substantial harmonised in situ data of global macroplastic abundance and distribution

Integrative Oncogenomic Analysis of Microarray Data in Hematologic Malignancies

During the last decade, gene expression microarrays and array-based comparative genomic hybridization (array-CGH) have unraveled the complexity of human tumor genomes more precisely and comprehensively than ever before. More recently, the simultaneous assessment of global changes in messenger RNA (mRNA) expression and in DNA copy number through "integrative oncogenomic" analyses has allowed researchers the access to results uncovered through the analysis of one-dimensional data sets, thus accelerating cancer gene discovery. In this chapter, we discuss the major contributions of DNA microarrays to the study of hematological malignancies, focusing on the integrative oncogenomic approaches that correlate genomic and transcriptomic data. We also present the basic aspects of these methodologies and their present and future application in clinical oncology

Emerging Technologies for Remote Sensing of Floating and Submerged Plastic Litter

Most advances in the remote sensing of floating marine plastic litter have been made using passive remote-sensing techniques in the visible (VIS) to short-wave-infrared (SWIR) parts of the electromagnetic spectrum based on the spectral absorption features of plastic surfaces. In this paper, we present developments of new and emerging remote-sensing technologies of marine plastic litter such as passive techniques: fluid lensing, multi-angle polarimetry, and thermal infrared sensing (TIS); and active techniques: light detection and ranging (LiDAR), multispectral imaging detection and active reflectance (MiDAR), and radio detection and ranging (RADAR). Our review of the detection capabilities and limitations of the different sensing technologies shows that each has their own weaknesses and strengths, and that there is not one single sensing technique that applies to all kinds of marine litter under every different condition in the aquatic environment. Rather, we should focus on the synergy between different technologies to detect marine plastic litter and potentially the use of proxies to estimate its presence. Therefore, in addition to further developing remote-sensing techniques, more research is needed in the composition of marine litter and the relationships between marine plastic litter and their proxies. In this paper, we propose a common vocabulary to help the community to translate concepts among different disciplines and techniques

Sensitivity of a Satellite Algorithm for Harmful Algal Bloom Discrimination to the Use of Laboratory Bio-optical Data for Training

Early detection of dense harmful algal blooms (HABs) is possible using ocean colour remote sensing. Some algorithms require a training dataset, usually constructed from satellite images with a priori knowledge of the existence of the bloom. This approach can be limited if there is a lack of in situ observations, coincident with satellite images. A laboratory experiment collected biological and bio-optical data from a culture of Karenia mikimotoi, a harmful phytoplankton dinoflagellate. These data showed characteristic signals in chlorophyll-specific absorption and backscattering coefficients. The bio-optical datafromthecultureandabio-opticalmodelwereusedtoconstructatrainingdatasetfor an existing statistical classifier. MERIS imagery over the European continental shelf were processed with the classifier using different training datasets. The differences in positive ratesofdetectionofK. mikimotoi betweenusinganalgorithmtrainedwithpurelymanually selected areas on satellite images and using laboratory data as training was overall <1%. The difference was higher, <15%, when using modeled optical data rather than laboratorydata,withpotentialforimprovementiflocalaveragechlorophyllconcentrations are used. Using a laboratory-derived training dataset improved the ability of the algorithm to distinguish high turbidity from high chlorophyll concentrations. However, additional in situ observations of non-harmful high chlorophyll blooms in the area would improve testing of the ability to distinguish harmful from non-harmful high chlorophyll blooms. This approach can be expanded to use additional wavelengths, different satellite sensors and different phytoplankton genera

Sensitivity of Modeled CO2 Air–Sea Flux in a Coastal Environment to Surface Temperature Gradients, Surfactants, and Satellite Data Assimilation

This work evaluates the sensitivity of CO2 air–sea gas exchange in a coastal site to four different model system configurations of the 1D coupled hydrodynamic–ecosystem model GOTM–ERSEM, towards identifying critical dynamics of relevance when specifically addressing quantification of air–sea CO2 exchange. The European Sea Regional Ecosystem Model (ERSEM) is a biomass and functional group-based biogeochemical model that includes a comprehensive carbonate system and explicitly simulates the production of dissolved organic carbon, dissolved inorganic carbon and organic matter. The model was implemented at the coastal station L4 (4 nm south of Plymouth, 50°15.00’N, 4°13.02’W, depth of 51 m). The model performance was evaluated using more than 1500 hydrological and biochemical observations routinely collected at L4 through the Western Coastal Observatory activities of 2008—2009. In addition to a reference simulation (A), we ran three distinct experiments to investigate the sensitivity of the carbonate system and modeled air–sea fluxes to (B) the sea-surface temperature (SST) diurnal cycle and thus also the near-surface verticalgradients,(C)biologicalsuppressionofgasexchangeand(D)dataassimilationusingsatellite Earth observation data. The reference simulation captures well the physical environment (simulated SST has a correlation with observations equal to 0.94 with a p > 0.95). Overall, the model captures the seasonal signal in most biogeochemical variables including the air–sea flux of CO2 and primary production and can capture some of the intra-seasonal variability and short-lived blooms. The model correctlyreproducestheseasonalityofnutrients(correlation>0.80forsilicate,nitrateandphosphate), surface chlorophyll-a (correlation > 0.43) and total biomass (correlation > 0.7) in a two year run for 2008–2009. The model simulates well the concentration of DIC, pH and in-water partial pressure of CO2 (pCO2) with correlations between 0.4–0.5. The model result suggest that L4 is a weak net source of CO2 (0.3–1.8 molCm−2 year−1). The results of the three sensitivity experiments indicate that both resolving the temperature profile near the surface and assimilation of surface chlorophyll-a significantlyimpacttheskillofsimulatingthebiogeochemistryatL4andallofthecarbonatechemistry related variables. These results indicate that our forecasting ability of CO2 air–sea flux in shelf seas environments and their impact in climate modeling should consider both model refinements as means of reducing uncertainties and errors in any future climate projections

Spatial structure of in situ reflectance in coastal and inland waters: implications for satellite validation

Validation of satellite-derived aquatic reflectance involves relating meter-scale in situ observations to satellite pixels with typical spatial resolution ∼ 10–100 m within a temporal “match-up window” of an overpass. Due to sub-pixel variation these discrepancies in measurement scale are a source of uncertainty in the validation result. Additionally, validation protocols and statistics do not normally account for spatial autocorrelation when pairing in situ data from moving platforms with satellite pixels. Here, using high-frequency autonomous mobile radiometers deployed on ships, we characterize the spatial structure of in situ Rrs in inland and coastal waters (Lake Balaton, Western English Channel, Tagus Estuary). Using variogram analysis, we partition Rrs variability into spatial and intrinsic (non-spatial) components. We then demonstrate the capacity of mobile radiometers to spatially sample in situ Rrs within a temporal window broadly representative of satellite validation and provide spatial statistics to aid satellite validation practice. At a length scale typical of a medium resolution sensor (300 m) between 5% and 35% (median values across spectral bands and deployments) of the variation in in situ Rrs was due to spatial separation. This result illustrates the extent to which mobile radiometers can reduce validation uncertainty due to spatial discrepancy via sub-pixel sampling. The length scale at which in situ Rrs became spatially decorrelated ranged from ∼ 100–1,000 m. This information serves as a guideline for selection of spatially independent in situ Rrs when matching with a satellite image, emphasizing the need for either downsampling or using modified statistics when selecting data to validate high resolution sensors (sub 100 m pixel size)

In situ correlation between microplastic and suspended particulate matter concentrations in river-estuary systems support proxies for satellite-derived estimates of microplastic flux

Rivers are key pathways for transporting microplastics from land to the oceans, yet microplastic flux estimates remain uncertain. Remote sensing allows repeated broad-scale measurements and can be used to extrapolate limited in situ observations. This study investigated the relationship between suspended particulate matter (SPM), a satellite-observable water quality parameter, and microplastic concentration in a partially mixed estuary (Tamar, UK). Microplastic concentrations ranged from 0.04 to 0.99 microplastics/m3, decreasing downstream. A significant correlation was found between SPM and microplastic concentration over two seasons. This relationship was used to compute a multiyear timeseries of proxy microplastic concentration from satellite imagery and produce estimates of annual proxy microplastic flux. This approach could be applied to investigate microplastic flux in other major rivers worldwide where such a relationship between microplastics and SPM exists. To apply this workflow elsewhere, the establishment of local SPM-to-microplastic relationships from in situ observations and local validation of remote sensing SPM algorithms are essential