Special Issue "Satellite Derived Global Ocean Product Validation/Evaluation"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: closed (1 February 2020).

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A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. SeungHyun Son
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Guest Editor
Colorado State University, Cooperative Institute for Research in the Atmosphere at NOAA/NESDIS/STAR, NCWCP building, 5830 University Research Court, College Park, MD 20740, USA
Interests: remote sensing; ocean color; bio-optical algorithms; water quality; phytoplankton productivity; human-/climate-induced changes in marine ecosystems
Special Issues and Collections in MDPI journals
Prof. Dr. Trevor Platt
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Guest Editor
Plymouth Marine Laboratory, Plymouth PL1 3DH, UK
Interests: the physiological ecology of marine phytoplankton; structure and function of the marine ecosystem; submarine optics; remote sensing of ocean colour; the ocean carbon cycle and climate change, and the ecological approach to fisheries management
Special Issues and Collections in MDPI journals
Dr. Shubha Sathyendranath
Website
Guest Editor
Plymouth Marine Laboratory, Plymouth PL1 3DH, UK
Interests: ocean colour modelling; spectral characteristics of light penetration underwater; bio-optical properties of phytoplankton; modelling primary production; bio-geochemical cycles in the sea; climate change; biological–physical interactions in the marine system; ecological provinces in the sea; ecological indicators and phytoplankton functional types
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Ocean satellite instruments provide short-term to long-term (hourly to decadal) observations of physical and biogeochemical phenomena and properties in the global ocean at high spatial resolution. Satellite-measured ocean products including sea surface temperature, ocean colour, sea surface salinity, sea surface height, sea surface winds, and sea ice are important data not only for near-real-time ocean monitoring but also for climate data records (CDR) to investigate changes in the ocean environment and manage marine resources for economic, social and environmental benefits. Ocean-observing satellite sensors have been launched recently by international space agencies including NASA, NOAA, ESA and JAXA (e.g., Aquarius, Advanced Microwave Scanning Radiometer 2 (AMSR2), Jason-3, Ocean and Land Colour Instrument (OLCI), Soil Moisture Ocean Salinity (SMOS), Sea and Land Surface Temperature Radiometer (SLSTR), Second Generation Global Imager (SGLI), and Visible Infrared Imaging Radiometer Suite (VIIRS)) and operationally measure the various physical, biological, and biogeochemical variables in the ocean. Validation/evaluation efforts and uncertainty assessments are crucial to providing more accurate satellite-derived ocean products. Validation of the satellite products requires a combination of ground field measurements, instrumented surface sites, inter-satellite comparisons, and research and modeling efforts with robust methodologies.

In this Special Issue, we encourage contributions including, but not limited to, the validation/evaluation of the oceanic radiometric, geophysical and biogeochemical retrievals from various ocean satellite instruments, inter-sensor bias correction, formal error analysis of satellite-observation systems, stability of satellite data and inter-comparison and assimilation of ocean products from multiple sensors.

Dr. SeungHyun Son
Dr. Trevor Platt
Dr. Shubha Sathyendranath
Guest Editors

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Keywords

  • Satellite Remote Sensing
  • Ocean Colour
  • Sea Ice
  • Sea Surface Temperature
  • Sea Surface Height
  • Sea Surface Salinity
  • Validation/Evaluation
  • End-to-end error characterisation
  • Inter-sensor bias correction
  • Stability of satellite data

Published Papers (14 papers)

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Open AccessArticle
Assessment and Improvement of Global Gridded Sea Surface Temperature Datasets in the Yellow Sea Using In Situ Ocean Buoy and Research Vessel Observations
Remote Sens. 2020, 12(5), 759; https://doi.org/10.3390/rs12050759 - 26 Feb 2020
Viewed by 735
Abstract
The sea surface temperature (SST) is essential data for the ocean and atmospheric prediction systems and climate change studies. Five global gridded sea surface temperature products were evaluated with independent in situ SST data of the Yellow Sea (YS) from 2010 to 2013 [...] Read more.
The sea surface temperature (SST) is essential data for the ocean and atmospheric prediction systems and climate change studies. Five global gridded sea surface temperature products were evaluated with independent in situ SST data of the Yellow Sea (YS) from 2010 to 2013 and the sources of SST error were identified. On average, SST from the gridded optimally interpolated level 4 (L4) datasets had a root mean square difference (RMSD) of less than 1 °C compared to the in situ observation data of the YS. However, the RMSD was relatively high (2.3 °C) in the shallow coastal region in June and July and this RMSD was mostly attributed to the large warm bias (>2 °C). The level 3 (L3) SST data were frequently missing in early summer because of frequent sea fog formation and a strong (>1.2 °C/12 km) spatial temperature gradient across the tidal mixing front in the eastern YS. The missing data were optimally interpolated from the SST observation in offshore warm water and warm biased SST climatology in the region. To fundamentally improve the accuracy of the L4 gridded SST data, it is necessary to increase the number of SST observation data in the tidally well mixed region. As an interim solution to the warm bias in the gridded SST datasets in the eastern YS, the SST climatology for the optimal interpolation can be improved based on long-term in situ observation data. To reduce the warm bias in the gridded SST products, two bias correction methods were suggested and compared. Bias correction methods using a simple analytical function and using climatological observation data reduced the RMSD by 19–29% and 37–49%, respectively, in June. Full article
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Open AccessArticle
Estimation of Hourly Sea Surface Salinity in the East China Sea Using Geostationary Ocean Color Imager Measurements
Remote Sens. 2020, 12(5), 755; https://doi.org/10.3390/rs12050755 - 25 Feb 2020
Cited by 2 | Viewed by 823
Abstract
Sea surface salinity (SSS) is an important tracer for monitoring the Changjiang Diluted Water (CDW) extension into Korean coastal regions; however, observing the SSS distribution in near real time is a difficult task. In this study, SSS detection algorithm was developed based on [...] Read more.
Sea surface salinity (SSS) is an important tracer for monitoring the Changjiang Diluted Water (CDW) extension into Korean coastal regions; however, observing the SSS distribution in near real time is a difficult task. In this study, SSS detection algorithm was developed based on the ocean color measurements by Geostationary Ocean Color Imager (GOCI) in high spatial and temporal resolution using multilayer perceptron neural network (MPNN). Among the various combinations of input parameters, combinations with three to six bands of GOCI remote sensing reflectance (Rrs), sea surface temperature (SST), longitude, and latitude were most appropriate for estimating the SSS. According to model validations with the Soil Moisture Active Passive (SMAP) and Ieodo Ocean Research Station (I-ORS) SSS measurements, the coefficient of determination (R2) were 0.81 and 0.92 and the root mean square errors (RMSEs) were 1.30 psu and 0.30 psu, respectively. In addition, a sensitivity analysis revealed the importance of SST and the red-wavelength spectral signal for estimating the SSS. Finally, hourly estimated SSS images were used to illustrate the hourly CDW distribution. With the model developed in this study, the near real-time SSS distribution in the East China Sea (ECS) can be monitored using GOCI and SST data. Full article
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Open AccessArticle
Multi-Sensor Observations of Submesoscale Eddies in Coastal Regions
Remote Sens. 2020, 12(4), 711; https://doi.org/10.3390/rs12040711 - 21 Feb 2020
Viewed by 802
Abstract
The temporal and spatial variation in submesoscale eddies in the coastal region of Lianyungang (China) is studied over a period of nearly two years with high-resolution (0.03°, about 3 km) observations of surface currents derived from high-frequency coastal radars (HFRs). The centers and [...] Read more.
The temporal and spatial variation in submesoscale eddies in the coastal region of Lianyungang (China) is studied over a period of nearly two years with high-resolution (0.03°, about 3 km) observations of surface currents derived from high-frequency coastal radars (HFRs). The centers and boundaries of submesoscale eddies are identified based on a vector geometry (VG) method. A color index (CI) representing MODIS ocean color patterns with a resolution of 500 m is used to compute CI gradient parameters, from which submesoscale features are extracted using a modified eddy-extraction approach. The results show that surface currents derived from HFRs and the CI-derived gradient parameters have the ability to capture submesoscale processes (SPs). The typical radius of an eddy in this region is 2–4 km. Although no significant difference in eddy properties is observed between the HFR-derived current fields and CI-derived gradient parameters, the CI-derived gradient parameters show more detailed eddy structures due to a higher resolution. In general, the HFR-derived current fields capture the eddy form, evolution and dissipation. Meanwhile, the CI-derived gradient parameters show more SPs and fill a gap left by the HFR-derived currents. This study shows that the HFR and CI products have the ability to detect SPs in the ocean and contribute to SP analyses. Full article
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Open AccessArticle
Retrieval of Particulate Backscattering Using Field and Satellite Radiometry: Assessment of the QAA Algorithm
Remote Sens. 2020, 12(1), 77; https://doi.org/10.3390/rs12010077 - 24 Dec 2019
Cited by 4 | Viewed by 796
Abstract
Particulate optical backscattering (bbp) is a crucial parameter for the study of ocean biology and oceanic carbon estimations. In this work, bbp retrieval, by the quasi-analytical algorithm (QAA), is assessed using a large in situ database of matched bbp [...] Read more.
Particulate optical backscattering (bbp) is a crucial parameter for the study of ocean biology and oceanic carbon estimations. In this work, bbp retrieval, by the quasi-analytical algorithm (QAA), is assessed using a large in situ database of matched bbp and remote-sensing reflectance (Rrs). The QAA is also applied to satellite Rrs (ESA OC-CCI project) as well, after their validation against in situ Rrs. Additionally, the effect of Raman Scattering on QAA retrievals is studied. Results show negligible biases above random noise when QAA-derived bbp is compared to in situ bbp. In addition, Rrs from the CCI archive shows good agreement with in situ data. The QAA’s functional form of spectral backscattering slope, as derived from in situ radiometry, is validated. Finally, we show the importance of correcting for Raman Scattering over clear waters prior to semi-analytical retrieval. Overall, this work demonstrates the high efficiency of QAA in the bbp detection in case of both in situ and ocean color data, but it also highlights the necessity to increase the number of observations that are severely under-sampled in respect to others environmental parameters. Full article
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Open AccessArticle
Evaluation of Sentinel-3A Wave Height Observations Near the Coast of Southwest England
Remote Sens. 2019, 11(24), 2998; https://doi.org/10.3390/rs11242998 - 13 Dec 2019
Cited by 9 | Viewed by 1073
Abstract
Due to the smaller ground footprint and higher spatial resolution of the Synthetic Aperture Radar (SAR) mode, altimeter observations from the Sentinel-3 satellites are expected to be overall more accurate in coastal areas than conventional nadir altimetry. The performance of Sentinel-3A in the [...] Read more.
Due to the smaller ground footprint and higher spatial resolution of the Synthetic Aperture Radar (SAR) mode, altimeter observations from the Sentinel-3 satellites are expected to be overall more accurate in coastal areas than conventional nadir altimetry. The performance of Sentinel-3A in the coastal region of southwest England was assessed by comparing SAR mode observations of significant wave height against those of Pseudo Low Resolution Mode (PLRM). Sentinel-3A observations were evaluated against in-situ observations from a network of 17 coastal wave buoys, which provided continuous time-series of hourly values of significant wave height, period and direction. As the buoys are evenly distributed along the coast of southwest England, they are representative of a broad range of morphological configurations and swell conditions against which to assess Sentinel-3 SAR observations. The analysis indicates that SAR observations outperform PLRM within 15 km from the coast. Within that region, regression slopes between SAR and buoy observations are close to the 1:1 relation, and the average root mean square error between the two is 0.46 ± 0.14 m. On the other hand, regression slopes for PLRM observations rapidly deviate from the 1:1 relation, while the average root mean square error increases to 0.84 ± 0.45 m. The analysis did not identify any dependence of the bias between SAR and in-situ observation on the swell period or direction. The validation is based on a synergistic approach which combines satellite and in-situ observations with innovative use of numerical wave model output to help inform the choice of comparison regions. Such an approach could be successfully applied in future studies to assess the performance of SAR observations over other combinations of coastal regions and altimeters. Full article
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Open AccessArticle
Evaluation of HY-2A Scatterometer Ocean Surface Wind Data during 2012–2018
Remote Sens. 2019, 11(24), 2968; https://doi.org/10.3390/rs11242968 - 11 Dec 2019
Cited by 1 | Viewed by 781
Abstract
This study focuses on the evaluation of global Haiyang-2A satellite scatterometer (HSCAT) operational wind products from 2012 to 2018. In order to evaluate HSCAT winds, HSCAT operational wind products were collocated with buoy measurements and rainfall data. Error varieties under different atmospheric stratification [...] Read more.
This study focuses on the evaluation of global Haiyang-2A satellite scatterometer (HSCAT) operational wind products from 2012 to 2018. In order to evaluate HSCAT winds, HSCAT operational wind products were collocated with buoy measurements and rainfall data. Error varieties under different atmospheric stratification and rainfall conditions were taken into consideration. After data quality control, the average bias and root mean square error (RMSE) between buoys and HSCAT data were 0.1 m/s and 1.3 m/s for wind speed, and 1° and 27° for wind direction, respectively. Especially, the varieties of the wind direction difference change a lot under non-neutral atmospheric conditions. HSCAT wind speeds are overestimated with an increasing rainfall rate while wind directions tend to be perpendicular to buoys’. In brief, the HSCAT wind product qualities are not stable during 2012 to 2018, especially for the data in 2015 and 2016. Atmospheric stratification and rain effects should be considered in wind retrieval and marine application. Full article
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Open AccessArticle
Evaluation of Spaceborne GNSS-R Retrieved Ocean Surface Wind Speed with Multiple Datasets
Remote Sens. 2019, 11(23), 2747; https://doi.org/10.3390/rs11232747 - 22 Nov 2019
Cited by 4 | Viewed by 858
Abstract
Spaceborne Global Navigation Satellite Systems-Reflectometry (GNSS-R) can estimate the geophysical parameters by receiving Earth’s surface reflected signals. The CYclone Global Navigation Satellite System (CYGNSS) mission with eight microsatellites launched by NASA in December 2016, which provides an unprecedented opportunity to rapidly acquire ocean [...] Read more.
Spaceborne Global Navigation Satellite Systems-Reflectometry (GNSS-R) can estimate the geophysical parameters by receiving Earth’s surface reflected signals. The CYclone Global Navigation Satellite System (CYGNSS) mission with eight microsatellites launched by NASA in December 2016, which provides an unprecedented opportunity to rapidly acquire ocean surface wind speed globally. In this paper, a refined spaceborne GNSS-R sea surface wind speed retrieval algorithm is presented and validated with the ground surface reference wind speed from numerical weather prediction (NWP) and cross-calibrated multi-platform ocean surface wind vector analysis product (CCMP), respectively. The results show that when the wind speed was less than 20 m/s, the RMS of the GNSS-R retrieved wind could achieve 1.84 m/s in the case where the NWP winds were used as the ground truth winds, while the result was better than the NWP-based retrieved wind speed with an RMS of 1.68 m/s when the CCMP winds were used. The two sets of inversion results were further evaluated by the buoy winds, and the uncertainties from the NWP-derived and CCMP-derived model prediction wind speed were 1.91 m/s and 1.87 m/s, respectively. The accuracy of inversed wind speeds for different GNSS pseudo-random noise (PRN) satellites and types was also analyzed and presented, which showed similar for different PRN satellites and different types of satellites. Full article
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Open AccessArticle
Evaluation of Satellite-Based Algorithms to Retrieve Chlorophyll-a Concentration in the Canadian Atlantic and Pacific Oceans
Remote Sens. 2019, 11(22), 2609; https://doi.org/10.3390/rs11222609 - 07 Nov 2019
Cited by 4 | Viewed by 657
Abstract
Remote-sensing reflectance data collected by ocean colour satellites are processed using bio-optical algorithms to retrieve biogeochemical properties of the ocean. One such important property is the concentration of chlorophyll-a, an indicator of phytoplankton biomass that serves a multitude of purposes in various ocean [...] Read more.
Remote-sensing reflectance data collected by ocean colour satellites are processed using bio-optical algorithms to retrieve biogeochemical properties of the ocean. One such important property is the concentration of chlorophyll-a, an indicator of phytoplankton biomass that serves a multitude of purposes in various ocean science studies. Here, the performance of two generic chlorophyll-a algorithms (i.e., a band ratio one, Ocean Colour X (OCx), and a semi-analytical one, Garver–Siegel Maritorena (GSM)) was assessed against two large in situ datasets of chlorophyll-a concentration collected between 1999 and 2016 in the Northeast Pacific (NEP) and Northwest Atlantic (NWA) for three ocean colour sensors: Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). In addition, new regionally-tuned versions of these two algorithms are presented, which reduced the mean error (mg m−3) of chlorophyll-a concentration modelled by OCx in the NWA from −0.40, −0.58 and −0.45 to 0.037, −0.087 and −0.018 for MODIS, SeaWiFS, and VIIRS respectively, and −0.34 and −0.36 to −0.0055 and −0.17 for SeaWiFS and VIIRS in the NEP. An analysis of the uncertainties in chlorophyll-a concentration retrieval showed a strong seasonal pattern in the NWA, which could be attributed to changes in phytoplankton community composition, but no long-term trends were found for all sensors and regions. It was also found that removing the 443 nm waveband for the OCx algorithms significantly improved the results in the NWA. Overall, GSM performed better than the OCx algorithms in both regions for all three sensors but generated fewer chlorophyll-a retrievals than the OCx algorithms. Full article
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Open AccessArticle
Evaluation of Chlorophyll-a and POC MODIS Aqua Products in the Southern Ocean
Remote Sens. 2019, 11(15), 1793; https://doi.org/10.3390/rs11151793 - 31 Jul 2019
Cited by 4 | Viewed by 1245
Abstract
The Southern Ocean (SO) is highly sensitive to climate change. Therefore, an accurate estimate of phytoplankton biomass is key to being able to predict the climate trajectory of the 21st century. In this study, MODerate resolution Imaging Spectroradiometer (MODIS), on board EOS Aqua [...] Read more.
The Southern Ocean (SO) is highly sensitive to climate change. Therefore, an accurate estimate of phytoplankton biomass is key to being able to predict the climate trajectory of the 21st century. In this study, MODerate resolution Imaging Spectroradiometer (MODIS), on board EOS Aqua spacecraft, Level 2 (nominal 1 km × 1 km resolution) chlorophyll-a (C S a t ) and Particulate Organic Carbon (POC s a t ) products are evaluated by comparison with an in situ dataset from 11 research cruises (2008–2017) to the SO, across multiple seasons, which includes measurements of POC and chlorophyll-a (C i n s i t u ) from both High Performance Liquid Chromatography (C H P L C ) and fluorometry (C F l u o ). Contrary to a number of previous studies, results highlighted good performance of the algorithm in the SO when comparing estimations with HPLC measurements. Using a time window of ±12 h and a mean satellite chlorophyll from a 5 × 5 pixel box centered on the in situ location, the median C S a t :C i n s i t u ratios were 0.89 (N = 46) and 0.49 (N = 73) for C H P L C and C F l u o respectively. Differences between C H P L C and C F l u o were associated with the presence of diatoms containing chlorophyll-c pigments, which induced an overestimation of chlorophyll-a when measured fluorometrically due to a potential overlap of the chlorophyll-a and chlorophyll-c emission spectra. An underestimation of ∼0.13 mg m 3 was observed for the global POC algorithm. This error was likely due to an overestimate of in situ POC i n s i t u measurements from the impact of dissolved organic carbon not accounted for in the blank correction. These results highlight the important implications of different in situ methodologies when validating ocean colour products. Full article
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Open AccessArticle
The Detection and Characterization of Arctic Sea Ice Leads with Satellite Imagers
Remote Sens. 2019, 11(5), 521; https://doi.org/10.3390/rs11050521 - 04 Mar 2019
Cited by 3 | Viewed by 1832
Abstract
Sea ice leads (fractures) play a critical role in the exchange of mass and energy between the ocean and atmosphere in the polar regions. The thinning of Arctic sea ice over the last few decades will likely result in changes in lead distributions, [...] Read more.
Sea ice leads (fractures) play a critical role in the exchange of mass and energy between the ocean and atmosphere in the polar regions. The thinning of Arctic sea ice over the last few decades will likely result in changes in lead distributions, so monitoring their characteristics is increasingly important. Here we present a methodology to detect and characterize sea ice leads using satellite imager thermal infrared window channels. A thermal contrast method is first used to identify possible sea ice lead pixels, then a number of geometric and image analysis tests are applied to build a subset of positively identified leads. Finally, characteristics such as width, length and orientation are derived. This methodology is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) observations for the months of January through April over the period of 2003 to 2018. The algorithm results are compared to other satellite estimates of lead distribution. Lead coverage maps and statistics over the Arctic illustrate spatial and temporal lead patterns. Full article
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Open AccessArticle
Ice Surface Temperature Retrieval from a Single Satellite Imager Band
Remote Sens. 2018, 10(12), 1909; https://doi.org/10.3390/rs10121909 - 29 Nov 2018
Cited by 3 | Viewed by 1200
Abstract
Current methods for estimating the surface temperature of sea and lake ice—the ice surface temperature (IST)—utilize two satellite imager thermal bands (11 and 12 μm) at moderate spatial resolution. These “split-window” or dual-band methods have been shown to have low biases and uncertainties. [...] Read more.
Current methods for estimating the surface temperature of sea and lake ice—the ice surface temperature (IST)—utilize two satellite imager thermal bands (11 and 12 μm) at moderate spatial resolution. These “split-window” or dual-band methods have been shown to have low biases and uncertainties. A single-band algorithm would be useful for satellite imagers that have only the 11 μm band at high resolution, such as the Visible Infrared Imaging Radiometer Suite (VIIRS), or that do not have a fully functional 12 μm band, such as the Thermal Infrared Sensor onboard the Landsat 8. This study presents a method for single-band IST retrievals, and validation of the retrievals using IST measurements from an airborne infrared radiation pyrometer during the NASA IceBridge campaign in the Arctic. Results show that IST with a single thermal band from the VIIRS has comparable performance to IST with the VIIRS dual-band (split-window) method, with a bias of 0.22 K and root-mean-square error of 1.03 K. Full article
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Open AccessTechnical Note
Estimation of the Particulate Organic Carbon to Chlorophyll-a Ratio Using MODIS-Aqua in the East/Japan Sea, South Korea
Remote Sens. 2020, 12(5), 840; https://doi.org/10.3390/rs12050840 - 05 Mar 2020
Cited by 1 | Viewed by 850
Abstract
In recent years, the change of marine environment due to climate change and declining primary productivity have been big concerns in the East/Japan Sea, Korea. However, the main causes for the recent changes are still not revealed clearly. The particulate organic carbon (POC) [...] Read more.
In recent years, the change of marine environment due to climate change and declining primary productivity have been big concerns in the East/Japan Sea, Korea. However, the main causes for the recent changes are still not revealed clearly. The particulate organic carbon (POC) to chlorophyll-a (chl-a) ratio (POC:chl-a) could be a useful indicator for ecological and physiological conditions of phytoplankton communities and thus help us to understand the recent reduction of primary productivity in the East/Japan Sea. To derive the POC in the East/Japan Sea from a satellite dataset, the new regional POC algorithm was empirically derived with in-situ measured POC concentrations. A strong positive linear relationship (R2 = 0.6579) was observed between the estimated and in-situ measured POC concentrations. Our new POC algorithm proved a better performance in the East/Japan Sea compared to the previous one for the global ocean. Based on the new algorithm, long-term POC:chl-a ratios were obtained in the entire East/Japan Sea from 2003 to 2018. The POC:chl-a showed a strong seasonal variability in the East/Japan Sea. The spring and fall blooms of phytoplankton mainly driven by the growth of large diatoms seem to be a major factor for the seasonal variability in the POC:chl-a. Our new regional POC algorithm modified for the East/Japan Sea could potentially contribute to long-term monitoring for the climate-associated ecosystem changes in the East/Japan Sea. Although the new regional POC algorithm shows a good correspondence with in-situ observed POC concentrations, the algorithm should be further improved with continuous field surveys. Full article
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Open AccessLetter
The Difference of Sea Level Variability by Steric Height and Altimetry in the North Pacific
Remote Sens. 2020, 12(3), 379; https://doi.org/10.3390/rs12030379 - 24 Jan 2020
Cited by 1 | Viewed by 741
Abstract
Sea level variability, which is less than ~100 km in scale, is important in upper-ocean circulation dynamics and is difficult to observe by existing altimetry observations; thus, interferometric altimetry, which effectively provides high-resolution observations over two swaths, was developed. However, validating the sea [...] Read more.
Sea level variability, which is less than ~100 km in scale, is important in upper-ocean circulation dynamics and is difficult to observe by existing altimetry observations; thus, interferometric altimetry, which effectively provides high-resolution observations over two swaths, was developed. However, validating the sea level variability in two dimensions is a difficult task. In theory, using the steric method to validate height variability in different pixels is feasible and has already been proven by modelled and altimetry gridded data. In this paper, we use Argo data around a typical mesoscale eddy and altimetry along-track data in the North Pacific to analyze the relationship between steric data and along-track data (SD-AD) at two points, which indicates the feasibility of the steric method. We also analyzed the result of SD-AD by the relationship of the distance of the Argo and the satellite in Point 1 (P1) and Point 2 (P2), the relationship of two Argo positions, the relationship of the distance between Argo positions and the eddy center and the relationship of the wind. The results showed that the relationship of the SD-AD can reach a correlation coefficient of ~0.98, the root mean square deviation (RMSD) was ~1.8 cm, the bias was ~0.6 cm. This proved that it is feasible to validate interferometric altimetry data using the steric method under these conditions. Full article
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Open AccessLetter
Spatio-Temporal Variability of the Habitat Suitability Index for the Todarodes pacificus (Japanese Common Squid) around South Korea
Remote Sens. 2019, 11(23), 2720; https://doi.org/10.3390/rs11232720 - 20 Nov 2019
Cited by 2 | Viewed by 878
Abstract
The climate-induced changes in marine fishery resources in South Korea have been a big concern over the last decades. The climate regime shift has led to not only a change in the dominant fishery resources, but also a decline in fishery landings in [...] Read more.
The climate-induced changes in marine fishery resources in South Korea have been a big concern over the last decades. The climate regime shift has led to not only a change in the dominant fishery resources, but also a decline in fishery landings in several species. The habitat suitability index (HSI) has been widely used to detect and forecast fishing ground formation. In this study, the catch data of the Todarodes pacificus (Japanese Common Squid) and satellite-derived environmental parameters were used to estimate the HSI for the T. pacificus around South Korea. More than 80% of the total catch was found in regions with a sea surface temperature (SST) of 14.91–27.26 °C, sea surface height anomaly (SSHA) of 0.05–0.20 m, chlorophyll-a of 0.32–1.35 mg m−3, and primary production of 480.41–850.18 mg C m−2 d−1. Based on these results, the HSI model for T. pacificus was derived. A strong positive relationship (R2 = 0.9260) was found between the HSI and the fishery landings. The climatological monthly mean HSI from 2002 to 2016 showed several hotspots, coinciding with the spawning and feeding grounds of T. pacificus. This outcome implies that our estimated HSI can yield a reliable prediction of the fishing ground for T. pacificus around South Korea. Furthermore, the approach with the simple HSI model used in this study can be applied elsewhere, and will help us to understand the spatial and temporal distribution of fishery resources. Full article
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