scholarly journals Empirical Relationships between Remote-Sensing Reflectance and Selected Inherent Optical Properties in Nordic Sea Surface Waters for the MODIS and OLCI Ocean Colour Sensors

2020 ◽  
Vol 12 (17) ◽  
pp. 2774
Author(s):  
Marta Konik ◽  
Piotr Kowalczuk ◽  
Monika Zabłocka ◽  
Anna Makarewicz ◽  
Justyna Meler ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Field Work ◽  
The Arctic ◽  
Significant Shift ◽  
Form Complex ◽  
Water Properties ◽  
Remote Sensing Reflectance ◽  
Phytoplankton Communities

The Nordic Seas and the Fram Strait regions are a melting pot of a number of water masses characterized by distinct optical water properties. The warm Atlantic Waters transported from the south and the Arctic Waters from the north, combined with the melt waters contributing to the Polar Waters, mediate the dynamic changes of the year-to-year large-scale circulation patterns in the area, which often form complex frontal zones. In the last decade, moreover, a significant shift in phytoplankton phenology in the area has been observed, with a certain northward expansion of temperate phytoplankton communities into the Arctic Ocean which could lead to a deterioration in the performance of remote sensing algorithms. In this research, we exploited the capability of the satellite sensors to monitor those inter-annual changes at basin scales. We propose locally adjusted algorithms for retrieving chlorophyll a concentrations Chla, absorption by particles ap at 443 and 670 nm, and total absorption atot at 443 and 670 nm developed on the basis of intensive field work conducted in 2013–2015. Measured in situ hyper spectral remote sensing reflectance has been used to reconstruct the MODIS and OLCI spectral channels for which the proposed algorithms have been adapted. We obtained MNB ≤ 0.5% for ap(670) and ≤3% for atot(670) and Chla. RMS was ≤30% for most of the retrieved optical water properties except ap(443) and Chla. The mean monthly mosaics of ap(443) computed on the basis of the proposed algorithm were used for reconstructing the spatial and temporal changes of the phytoplankton biomass in 2013–2015. The results corresponded very well with in situ measurements.

scholarly journals Melt Pond Retrieval Based on the LinearPolar Algorithm Using Landsat Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4674
Author(s):  
Yuqing Qin ◽  
Jie Su ◽  
Mingfeng Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Relative Error ◽  
Large Scale ◽  
The Arctic ◽  
Landsat Data ◽  
Melt Pond ◽  
Melt Ponds ◽  
Very High ◽  
Sentinel 2

The formation and distribution of melt ponds have an important influence on the Arctic climate. Therefore, it is necessary to obtain more accurate information on melt ponds on Arctic sea ice by remote sensing. The present large-scale melt pond products, especially the melt pond fraction (MPF), still require verification, and using very high resolution optical satellite remote sensing data is a good way to verify the large-scale retrieval of MPF products. Unlike most MPF algorithms using very high resolution data, the LinearPolar algorithm using Sentinel-2 data considers the albedo of melt ponds unfixed. In this paper, by selecting the best band combination, we applied this algorithm to Landsat 8 (L8) data. Moreover, Sentinel-2 data, as well as support vector machine (SVM) and iterative self-organizing data analysis technique (ISODATA) algorithms, are used as the comparison and verification data. The results show that the recognition accuracy of the LinearPolar algorithm for melt ponds is higher than that of previous algorithms. The overall accuracy and kappa coefficient results achieved by using the LinearPolar algorithm with L8 and Sentinel-2A (S2), the SVM algorithm, and the ISODATA algorithm are 95.38% and 0.88, 94.73% and 0.86, and 92.40%and 0.80, respectively, which are much higher than those of principal component analysis (PCA) and Markus algorithms. The mean MPF (10.0%) obtained from 80 cases from L8 data based on the LinearPolar algorithm is much closer to Sentinel-2 (10.9%) than the Markus (5.0%) and PCA algorithms (4.2%), with a mean MPF difference of only 0.9%, and the correlation coefficients of the two MPFs are as high as 0.95. The overall relative error of the LinearPolar algorithm is 53.5% and 46.4% lower than that of the Markus and PCA algorithms, respectively, and the root mean square error (RMSE) is 30.9% and 27.4% lower than that of the Markus and PCA algorithms, respectively. In the cases without obvious melt ponds, the relative error is reduced more than that of those with obvious melt ponds because the LinearPolar algorithm can identify 100% of dark melt ponds and relatively small melt ponds, and the latter contributes more to the reduction in the relative error of MPF retrieval. With a wider range and longer time series, the MPF from Landsat data are more efficient than those from Sentinel-2 for verifying large-scale MPF products or obtaining long-term monitoring of a fixed area.

scholarly journals Sea ice melt pond fraction estimation from dual-polarisation C-band SAR – Part 2: Scaling in situ to Radarsat-2

2014 ◽  
Vol 8 (1) ◽  
pp. 845-885 ◽  
Author(s):  
R. K. Scharien ◽  
K. Hochheim ◽  
J. Landy ◽  
D. G. Barber
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Temporal Frequency ◽  
Model Performance ◽  
Free Water ◽  
Light Transmittance ◽  
The Arctic ◽  
Ice Melt ◽  
Melt Pond

Abstract. Observed changes in the Arctic have motivated efforts to understand and model its components as an integrated and adaptive system at increasingly finer scales. Sea ice melt pond fraction, an important summer sea ice component affecting surface albedo and light transmittance across the ocean-sea ice–atmosphere interface, is inadequately parameterized in models due to a lack of large scale observations. In this paper, results from a multi-scale remote sensing program dedicated to the retrieval of pond fraction from satellite C-band synthetic aperture radar (SAR) are detailed. The study was conducted on first-year sea (FY) ice in the Canadian Arctic Archipelago during the summer melt period in June 2012. Approaches to retrieve the subscale FY ice pond fraction from mixed pixels in RADARSAT-2 imagery, using in situ, surface scattering theory, and image data are assessed. Each algorithm exploits the dominant effect of high dielectric free-water ponds on the VV/HH polarisation ratio (PR) at moderate to high incidence angles (about 40° and above). Algorithms are applied to four images corresponding to discrete stages of the seasonal pond evolutionary cycle, and model performance is assessed using coincident pond fraction measurements from partitioned aerial photos. A RMSE of 0.07, across a pond fraction range of 0.10 to 0.70, is achieved during intermediate and late seasonal stages. Weak model performance is attributed to wet snow (pond formation) and synoptically driven pond freezing events (all stages), though PR has utility for identification of these events when considered in time series context. Results demonstrate the potential of wide-swath, dual-polarisation, SAR for large-scale observations of pond fraction with temporal frequency suitable for process-scale studies and improvements to model parameterizations.

scholarly journals Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system

2014 ◽  
Vol 11 (13) ◽  
pp. 3547-3602 ◽  
Author(s):  
P. Ciais ◽  
A. J. Dolman ◽  
A. Bombelli ◽  
R. Duren ◽  
A. Peregon ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Cycle ◽  
Fossil Fuel ◽  
The Arctic ◽  
Observation System ◽  
Observation Data ◽  
Current State ◽  
Spatial Coverage

Abstract. A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The paper is addressed to scientists, policymakers, and funding agencies who need to have a global picture of the current state of the (diverse) carbon observations. We identify the current state of carbon observations, and the needs and notional requirements for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion of the ground-based observation networks required to reach the high spatial resolution for CO2 and CH4 fluxes, and for carbon stocks for addressing policy-relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over areas such as the southern oceans, tropical forests, and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote-sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO2 proxy measurements such as radiocarbon in CO2 and carbon-fuel combustion tracers. Additionally, a policy-relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. In addition, uncertainties for each observation data-stream should be assessed. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases interoperable, and on the calibration of each component of the system to agreed-upon international scales.

scholarly journals An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4285 ◽  
Author(s):  
Shubha Sathyendranath ◽  
Robert Brewin ◽  
Carsten Brockmann ◽  
Vanda Brotas ◽  
Ben Calton ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Marine Ecosystem ◽  
Ocean Colour ◽  
Remote Sensing Reflectance ◽  
Resolution Imaging ◽  
In Situ Observations ◽  
Climate Studies ◽  
Reflectance Data

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.

scholarly journals Integrative and comprehensive Understanding on Polar Environments (iCUPE): the concept and initial results

2020 ◽  
Author(s):  
Tuukka Petäjä ◽  
Ella-Maria Duplissy ◽  
Ksenia Tabakova ◽  
Julia Schmale ◽  
Barbara Altstädter ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Natural Resources ◽  
Satellite Remote Sensing ◽  
Integrated Approach ◽  
The Arctic ◽  
Polar Regions ◽  
Comprehensive Understanding ◽  
In Situ Observations ◽  
Initial Results

Abstract. The role of polar regions increases in terms of megatrends such as globalization, new transport routes, demography and use of natural resources consequent effects of regional and transported pollutant concentrations. We set up the ERA-PLANET Strand 4 project iCUPE – integrative and Comprehensive Understanding on Polar Environments to provide novel insights and observational data on global grand challenges with an Arctic focus. We utilize an integrated approach combining in situ observations, satellite remote sensing Earth Observations (EO) and multi-scale modeling to synthesize data from comprehensive long-term measurements, intensive campaigns and satellites to deliver data products, metrics and indicators to the stakeholders concerning the environmental status, availability and extraction of natural resources in the polar areas. The iCUPE work consists of thematic state-of-the-art research and provision of novel data in atmospheric pollution, local sources and transboundary transport, characterization of arctic surfaces and their changes, assessment of concentrations and impacts of heavy metals and persistent organic pollutants and their cycling, quantification of emissions from natural resource extraction and validation and optimization of satellite Earth Observation (EO) data streams. In this paper we introduce the iCUPE project and summarize initial results arising out of integration of comprehensive in situ observations, satellite remote sensing and multiscale modeling in the Arctic context.

scholarly journals Using ship-borne observations of methane isotopic ratio in the Arctic Ocean to understand methane sources in the Arctic

2019 ◽  
Author(s):  
Antoine Berchet ◽  
Isabelle Pison ◽  
Patrick M. Crill ◽  
Brett Thornton ◽  
Philippe Bousquet ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Large Scale ◽  
Transport Model ◽  
Isotopic Ratio ◽  
The Arctic ◽  
Isotopic Ratios ◽  
Air Masses ◽  
Remote Areas ◽  
The Arctic Ocean

Abstract. Due to the large variety and heterogeneity of sources in remote areas hard to document, the Arctic regional methane budget remain very uncertain. In situ campaigns provide valuable data sets to reduce these uncertainties. Here we analyse data from the SWERUS-C3 campaign, on-board the icebreaker Oden, that took place during summer 2014 in the Arctic Ocean along the Northern Siberian and Alaskan shores. Total concentrations of methane, as well as isotopic ratios were measured continuously during this campaign for 35 days in July and August 2014. Using a chemistry-transport model, we link observed concentrations and isotopic ratios to regional emissions and hemispheric transport structures. A simple inversion system helped constraining source signatures from wetlands in Siberia and Alaska and oceanic sources, as well as the isotopic composition of lower stratosphere air masses. The variation in the signature of low stratosphere air masses, due to strongly fractionating chemical reactions in the stratosphere, was suggested to explain a large share of the observed variability in isotopic ratios. These points at required efforts to better simulate large scale transport and chemistry patterns to use isotopic data in remote areas. It is found that constant and homogeneous source signatures for each type of emission in the region (mostly wetlands and oil and gas industry) is not compatible with the strong synoptic isotopic signal observed in the Arctic. A regional gradient in source signatures is highlighted between Siberian and Alaskan wetlands, the later ones having a lighter signatures than the first ones. Arctic continental shelf sources are suggested to be a mixture of methane from a dominant thermogenic origin and a secondary biogenic one, consistent with previous in-situ isotopic analysis of seepage along the Siberian shores.

Remote sensing reflectance in the Great Lakes: In situ measurements, closure analyses, and a forward model

2013 ◽  
Vol 39 ◽  
pp. 137-150 ◽  
Author(s):  
David M. O'Donnell ◽  
Steven W. Effler ◽  
Christopher M. Strait ◽  
Feng Peng ◽  
MaryGail Perkins
Keyword(s):  
Remote Sensing ◽  
Great Lakes ◽  
In Situ Measurements ◽  
Forward Model ◽  
Remote Sensing Reflectance

Heme b distributions through the Atlantic Ocean: in situ identification of iron limited phytoplankton

2020 ◽  
Author(s):  
Evangelia Louropoulou ◽  
Martha Gledhill ◽  
Eric P. Achterberg ◽  
Thomas J. Browning ◽  
David J. Honey ◽  
...  
Keyword(s):  
Atlantic Ocean ◽  
Large Scale ◽  
North Atlantic Ocean ◽  
Iron Status ◽  
Mean Value ◽  
Particulate Material ◽  
South Atlantic ◽  
Phytoplankton Communities ◽  
The Impact

<p>Heme <em>b</em> is an iron-containing cofactor in hemoproteins that participates in the fundamental processes of photosynthesis and respiration in phytoplankton. Heme <em>b</em> concentrations typically decline in waters with low iron concentrations but due to lack of field data, the distribution of heme <em>b</em> in particulate material in the ocean is poorly constrained. Within the framework of the Helmholtz Research School for Ocean System Science and Technology (HOSST) and the GEOTRACES programme, the authors compiled datasets and conducted multidisciplinary research (e.g. chemical oceanography, microbiology, biogeochemical modelling) in order to test heme <em>b</em> as an indicator of <em>in situ</em> iron-limited phytoplankton. This study was initiated in the North Atlantic Ocean and expanded to the under-sampled South Atlantic Ocean for comparison of the results considering the different phytoplankton populations. Here, we report particulate heme <em>b</em> distributions across the Atlantic Ocean (59.9°N to 34.6°S). Heme <em>b</em> concentrations in surface waters ranged from 0.10 to 33.7 pmol L<sup>-1</sup> (median=1.47 pmol L<sup>-1</sup>, n=974) and were highest in regions with a high biomass. The ratio of heme <em>b</em> to particulate organic carbon (POC) exhibited a mean value of 0.44 μmol heme<em> b</em> mol<sup>-1 </sup>POC. We identified the ratio of 0.10 µmol heme <em>b</em> mol<sup>-1</sup> POC as the cut-off between heme <em>b</em> replete and heme <em>b</em> deficient phytoplankton. By this definition, the ratio heme <em>b</em> relative to POC was consistently below 0.10 μmol mol<sup>-1</sup> in areas characterized by low Fe supply; these were the Subtropical South Atlantic gyre and the seasonally iron limited Irminger Basin. Thus, the ratio heme <em>b</em> relative to POC gave a reliable indication of iron limited phytoplankton communities in situ. Furthermore, the comparison of observed and modelled heme <em>b</em> suggested that heme <em>b</em> could account for between 0.17-9.1% of biogenic iron. This range was comparable to previous culturing observations for species with low heme <em>b</em> content and species growing in low Fe (≤0.50 nmol L<sup>-1</sup>) or nitrate culturing media. Our large scale observations of heme<em> b</em> relative to organic matter suggest the impact of changes in iron supply on phytoplankton iron status.</p>

Numerical modeling of mesospheric mountain wave propagation observed in Southern Argentina during the SOUTHTRAC campaign

2020 ◽  
Author(s):  
Tyler Mixa ◽  
Andreas Dörnbrack ◽  
Bernd Kaifler ◽  
Markus Rapp
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Numerical Models ◽  
Airborne Lidar ◽  
Tierra Del Fuego ◽  
Polar Night ◽  
Research Aircraft ◽  
Complex Propagation ◽  
The Antarctic

<p>We present numerical simulations of a deep orographic gravity wave (GW) event observed by the ALIMA airborne lidar on 11-12 September 2019 over Southern Argentina. The measurements are taken from the 2019 SOUTHTRAC Campaign, employing a comprehensive suite of remote sensing and in-situ instruments onboard the HALO research aircraft to study the stratospheric GW hotspot over Tierra del Fuego and the Antarctic Peninsula. Wind conditions on 11-12 September exhibit local and large-scale directional shear from the ground to the polar night jet, creating a complex propagation environment supporting multiple orientations of GW propagation and strong potential for local GW breaking and secondary GW generation. Using high resolution numerical models, we simulate the 3D evolution of the orographic GW field to analyze<span> the remote sensing and in-situ measurements from the event.</span></p>

Particle size distribution of river-suspended sediments determined by in situ measured remote-sensing reflectance

2015 ◽  
Vol 54 (20) ◽  
pp. 6367 ◽  
Author(s):  
Yuanzhi Zhang ◽  
Zhaojun Huang ◽  
Chuqun Chen ◽  
Yijun He ◽  
Tingchen Jiang
Keyword(s):  
Remote Sensing ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Suspended Sediments ◽  
Remote Sensing Reflectance
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