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Remote Sensing of Short-Term Coastal Ocean Processes Enabled from Geostationary Vantage Point

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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 November 2018) | Viewed by 21075

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Special Issue Editors

Dr. Nima Pahlevan

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Guest Editor

NASA/GSFC, Greenbelt, MD 20771, USA
Interests: remote sensing of coastal/inland waters

Prof. Steven Lohrenz

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Guest Editor

Department of Oceanography, University of Massachusetts Dartmouth, 836 South Rodney French Blvd, New Bedford, MA 02744, USA
Interests: ocean biological distributions and productivity

Dr. Yu-Hwan Ahn

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Guest Editor

The Korea Ocean Satellite Center (KOSC), 87, Haean-ro, Sangrok-gu, Ansan Gyeonggi-do, Korea
Interests: remote sensing of short-term processes

Dr. David Antoine

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Guest Editor

Remote Sensing and Satellite Research Group, Department of Imaging and Applied Physics, Curtin University, Building 301, room 146 GPO Box U1987, Perth, WA 6845, Australia
Interests: marine optics

Special Issue Information

Dear Colleagues,

Biological and biogeochemical processes play critical roles in forming and modulating the ecosystems of both open ocean and coastal environments. Observing and monitoring the spatial and temporal changes of these environments are important for maintaining the quality of life for everyone on Earth. Decades of operation of polar-orbiting ocean color missions have demonstrated that Sun-synchronous ocean color missions can provide excellent observations on longer-term (weeks to years) biogeochemical processes, but are unable to detect/monitor short-term (diurnal to a few weeks) processes, such as the dynamics of algae blooms, tidal dynamics, diurnal changes in photosynthesis, etc. Building on the success of ocean color missions and following the recommendations of the U.S. National Research Council, NASA is planning a geostationary ocean color satellite system to fill the temporal gap, with a focus on the coastal regions of the North and South American continents. Due to the unique sampling strategy and sensor-target geometry, as well as the demand to address a wide range of challenging scientific questions, the radiometric sensor for this geostationary cannot simply be a duplicate of the historical sensors, such as SeaWiFS or MODIS. In addition to frequent sampling (every hour or better), a geostationary sensor is expected to enable relatively high-spatial and high-spectral resolution measurements with high signal-to-noise ratios (SNR). Understanding the scientific and societal benefits of geostationary observations, the Republic of Korea launched the first and currently only ocean color sensor in geostationary orbit in June 2010. The Korean Geostationary Ocean Color Imager (GOCI) has demonstrated the value of such observations and greater potential for more capable sensors. To advance the utilization of GOCI data and facilitate the design requirements of future geostationary ocean color sensors, a joint two-week field campaign (the Korea-US Ocean Color (KORUS-OC)) was conducted by the Korea Institute of Ocean Science and Technology (KIOST) and NASA-supported scientists in the East and Yellow Seas under the field of view of GOCI. Data from this field campaign and other independent field efforts will help define limitations geostationary sensors on the retrieval of productivity, biogeochemical properties, changes attributable to tidal and advective processes, and provides key information on satellite specific issues, e.g., impact of atmospheric corrections, image artifacts, view angle and diurnal solar radiance variability on the quality of satellite retrievals. The submitted manuscripts are expected to address questions covering a wide range of topics, including calibration methodologies and radiometric performance analyses, image artifacts, measurement requirements, atmospheric correction, bio-optical and bio-geochemical algorithm developments, temporal and spatial variability of coastal marine organisms and organic/inorganic particles, the evolution of algae blooms, and interdisciplinary science explorations involving complex relationships among short-term coastal ocean physical forcings, aerosols, and marine biology at land-water interface. We encourage submissions from authors working with data from high-frequency in situ platforms, the KORUS-OC effort, present geostationary satellites (GOCI-I, Himawari 8, GOES-R, SEVIRI), or from those involved in defining requirements for the next generation of geostationary missions.

Dr. Nima Pahlevan
Prof. Steven Lohrenz
Dr. Yu-Hwan Ahn
Dr. David Antoine
Guest Editors

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Keywords

Diurnal Coastal Processes
Geostationary Orbit
Bio-Optics
Bio-Geochemistry
Ocean Color
Calibration/Validation

Published Papers (5 papers)

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Research

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19 pages, 5089 KiB

Open AccessArticle

Uncertainties in the Geostationary Ocean Color Imager (GOCI) Remote Sensing Reflectance for Assessing Diurnal Variability of Biogeochemical Processes

by Javier Concha, Antonio Mannino, Bryan Franz and Wonkook Kim

Remote Sens. 2019, 11(3), 295; https://doi.org/10.3390/rs11030295 - 01 Feb 2019

Cited by 23 | Viewed by 3899

Abstract

Short-term (sub-diurnal) biological and biogeochemical processes cannot be fully captured by the current suite of polar-orbiting satellite ocean color sensors, as their temporal resolution is limited to potentially one clear image per day. Geostationary sensors, such as the Geostationary Ocean Color Imager (GOCI) from the Republic of Korea, allow the study of these short-term processes because their orbit permit the collection of multiple images throughout each day for any area within the sensor’s field of regard. Assessing the capability to detect sub-diurnal changes in in-water properties caused by physical and biogeochemical processes characteristic of open ocean and coastal ocean ecosystems, however, requires an understanding of the uncertainties introduced by the instrument and/or geophysical retrieval algorithms. This work presents a study of the uncertainties during the daytime period for an ocean region with characteristically low-productivity with the assumption that only small and undetectable changes occur in the in-water properties due to biogeochemical processes during the daytime period. The complete GOCI mission data were processed using NASA’s SeaDAS/l2gen package. The assumption of homogeneity of the study region was tested using three-day sequences and diurnal statistics. This assumption was found to hold based on the minimal diurnal and day-to-day variability in GOCI data products. Relative differences with respect to the midday value were calculated for each hourly observation of the day in order to investigate what time of the day the variability is greater. Also, the influence of the solar zenith angle in the retrieval of remote sensing reflectances and derived products was examined. Finally, we determined that the uncertainties in water-leaving “remote-sensing” reflectance (R_rs) for the 412, 443, 490, 555, 660 and 680 nm bands on GOCI are 8.05 × 10⁻⁴, 5.49 × 10⁻⁴, 4.48 × 10⁻⁴, 2.51 × 10⁻⁴, 8.83 × 10⁻⁵, and 1.36 × 10⁻⁴ sr⁻¹, respectively, and 1.09 × 10⁻² mg m⁻³ for the chlorophyll-a concentration (Chl-a), 2.09 × 10⁻³ m⁻¹ for the absorption coefficient of chromophoric dissolved organic matter at 412 nm (a_g (412)), and 3.7 mg m⁻³ for particulate organic carbon (POC). These R_rs values can be considered the threshold values for detectable changes of the in-water properties due to biological, physical or biogeochemical processes from GOCI. Full article

(This article belongs to the Special Issue Remote Sensing of Short-Term Coastal Ocean Processes Enabled from Geostationary Vantage Point)

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20 pages, 6262 KiB

Open AccessArticle

Atmospheric Trace Gas (NO₂ and O₃) Variability in South Korean Coastal Waters, and Implications for Remote Sensing of Coastal Ocean Color Dynamics

by Maria Tzortziou, Owen Parker, Brian Lamb, Jay R. Herman, Lok Lamsal, Ryan Stauffer and Nader Abuhassan

Remote Sens. 2018, 10(10), 1587; https://doi.org/10.3390/rs10101587 - 03 Oct 2018

Cited by 31 | Viewed by 4596

Abstract

Coastal environments are highly dynamic, and are characterized by short-term, local-scale variability in atmospheric and oceanic processes. Yet, high-frequency measurements of atmospheric composition, and particularly nitrogen dioxide (NO₂) and ozone (O₃) dynamics, are scarce over the ocean, introducing uncertainties in satellite retrievals of coastal ocean biogeochemistry and ecology. Combining measurements from different platforms, the Korea-US Ocean Color and Air Quality field campaign provided a unique opportunity to capture, for the first time, the strong spatial dynamics and diurnal variability in total column (TC) NO₂ and O₃ over the coastal waters of South Korea. Measurements were conducted using a shipboard Pandora Spectrometer Instrument specifically designed to collect accurate, high-frequency observations from a research vessel, and were combined with ground-based observations at coastal land sites, synoptic satellite imagery, and air-mass trajectory simulations to assess source contributions to atmospheric pollution over the coastal ocean. TCO₃ showed only small (<20%) variability that was driven primarily by larger-scale meteorological processes captured successfully in the relatively coarse satellite imagery from Aura-OMI. In contrast, TCNO₂ over the ocean varied by more than an order of magnitude (0.07–0.92 DU), mostly affected by urban emissions and highly dynamic air mass transport pathways. Diurnal patterns varied widely across the ocean domain, with TCNO₂ in the coastal area of Geoje and offshore Seoul varying by more than 0.6 DU and 0.4 DU, respectively, over a period of less than 3 h. On a polar orbit, Aura-OMI is not capable of detecting these short-term changes in TCNO₂. If unaccounted for in atmospheric correction retrievals of ocean color, the observed variability in TCNO₂ would be misinterpreted as a change in ocean remote sensing reflectance, R_rs, by more than 80% and 40% at 412 and 443 nm, respectively, introducing a significant false variability in retrievals of coastal ocean ecological processes from space. Full article

(This article belongs to the Special Issue Remote Sensing of Short-Term Coastal Ocean Processes Enabled from Geostationary Vantage Point)

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25 pages, 42816 KiB

Open AccessArticle

Diurnal Variation of Light Absorption in the Yellow River Estuary

by Yanling Hao, Tingwei Cui, Vijay P. Singh, Jie Zhang, Ruihong Yu and Wenjing Zhao

Remote Sens. 2018, 10(4), 542; https://doi.org/10.3390/rs10040542 - 02 Apr 2018

Cited by 7 | Viewed by 4393

Abstract

Considering the influence of river discharge and strong winds, the diurnal variability of ocean optical absorption properties in the Yellow River Estuary (YRE) is quantified, using in-situ measurements. The study finds that terrestrial sources due to the Yellow River discharge can cause high diurnal variation of water absorption because of the movement of river plume in the YRE, but such an influence diminishes far away from the Yellow River plume. The diurnal variability of water absorption, affected by strong winds, is found to be strengthened with a rapid increase of particles and colored dissolved organic matter (CDOM) arising from re-suspended sediment induced by wave forcing. The diurnal variability of particle absorption is controlled by non-algal particle absorption in the YRE, and the ratio of non-algal particle absorption (a_NAP) and total particle absorption for most wavelengths is more than 0.56. The diurnal variation of spectral slope of non-algal particle absorption (S_NAP) is found to vary within a narrow range, although large variability in the a_NAP spectrum is observed. The CDOM is correlated negatively with salinity, and such negative correlation becomes weaker with the decreasing influence of riverine input. The spectral slope of CDOM absorption (S_g) may reflect the formation and constituents of CDOM with weak relationship to its concentration, and its relationship with the absorption of CDOM at 440 nm may be associated with the source of CDOM. The value of S_g, which is affected by re-suspended bottom sediment, is much lower than that derived from CDOM affected by Yellow River runoff. Disregarding the absorption of pure water, the diurnal variability of total water absorption stems principally from changes in non-algal particle matter rather than CDOM and Chl-a. By the observations of hourly GOCI (Geostationary Ocean Color Imager) data, the major diurnal variations of remote sensing reflectance at 680 nm are observed in near-coastal waters and the estuary of the Yellow River, which are mainly influenced by the flow discharge of Yellow River and strong winds. Finally, the seasonal differences of diurnal variations of water absorption caused by strong winds and river discharge are determined. Full article

(This article belongs to the Special Issue Remote Sensing of Short-Term Coastal Ocean Processes Enabled from Geostationary Vantage Point)

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20 pages, 5477 KiB

Open AccessArticle

Impacts of Insufficient Observations on the Monitoring of Short- and Long-Term Suspended Solids Variations in Highly Dynamic Waters, and Implications for an Optimal Observation Strategy

by Qu Zhou, Liqiao Tian, Onyx W. H. Wai, Jian Li, Zhaohua Sun and Wenkai Li

Remote Sens. 2018, 10(2), 345; https://doi.org/10.3390/rs10020345 - 23 Feb 2018

Cited by 11 | Viewed by 4479

Abstract

Coastal water regions represent some of the most fragile ecosystems, exposed to both climate change and human activities. While remote sensing provides unprecedented amounts of data for water quality monitoring on regional to global scales, the performance of satellite observations is frequently impeded by revisiting intervals and unfavorable conditions, such as cloud coverage and sun glint. Therefore, it is crucial to evaluate the impacts of varied sampling strategies (time and frequency) and insufficient observations on the monitoring of short-term and long-term tendencies of water quality parameters, such as suspended solids (SS), in highly dynamic coastal waters. Taking advantage of the first high-frequency in situ SS dataset (at 30 min sampling intervals from 2007 to 2008), collected in Deep Bay, China, this paper presents a quantitative analysis of the influences of sampling strategies on the monitoring of SS, in terms of sampling frequency and time of day. Dramatic variations of SS were observed, with standard deviation coefficients of 48.9% and 54.1%, at two fixed stations; in addition, significant uncertainties were revealed, with the average absolute percent difference of approximately 13%, related to sampling frequency and time, using nonlinear optimization and random simulation methods. For a sampling frequency of less than two observations per day, the relative error of SS was higher than 50%, and stabilized at approximately 10%, when at least four or five samplings were conducted per day. The optimal recommended sampling times for SS were at around 9:00, 12:00, 14:00, and 16:00 in Deep Bay. The “pseudo” MODIS SS dataset was obtained from high-frequency in situ SS measurements at 10:30 and 14:00, masked by the temporal gap distribution of MODIS coverage to avoid uncertainties propagated from atmospheric correction and SS models. Noteworthy uncertainties of daily observations from the Terra/Aqua MODIS were found, with mean relative errors of 19.2% and 17.8%, respectively, whereas at the monthly level, the mean relative error of Terra/Aqua MODIS observations was approximately 10.7% (standard deviation of 8.4%). Sensitivity analysis between MODIS coverage and SS relative errors indicated that temporal coverage (the percentage of valid MODIS observations for a month) of more than 70% is required to obtain high-precision SS measurements at a 5% error level. Furthermore, approximately 20% of relative errors were found with the coverage of 30%, which was the average coverage of satellite observations over global coastal waters. These results highlight the need for high-frequency measurements of geostationary satellites like GOCI and multi-source ocean color sensors to capture the dynamic process of coastal waters in both the short and long term. Full article

(This article belongs to the Special Issue Remote Sensing of Short-Term Coastal Ocean Processes Enabled from Geostationary Vantage Point)

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Other

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12 pages, 3191 KiB

Open AccessTechnical Note

Assessing the Potential Benefits of the Geostationary Vantage Point for Generating Daily Chlorophyll-a Maps in the Baltic Sea

by Marco Bellacicco, Daniele Ciani, David Doxaran, Vincenzo Vellucci, David Antoine, Menghua Wang, Fabrizio D’Ortenzio and Salvatore Marullo

Remote Sens. 2018, 10(12), 1944; https://doi.org/10.3390/rs10121944 - 03 Dec 2018

Viewed by 3224

Abstract

Currently, observations from low-Earth orbit (LEO) ocean color sensors represent one of the most used tools to study surface optical and biogeochemical properties of the ocean. LEO observations are available at daily temporal resolution, and are often combined into weekly, monthly, seasonal, and annual averages in order to obtain sufficient spatial coverage. Indeed, daily satellite maps of the main oceanic variables (e.g., surface phytoplankton chlorophyll-a) generally have many data gaps, mainly due to clouds, which can be filled using either Optimal Interpolation or the Empirical Orthogonal Functions approach. Such interpolations, however, may introduce large uncertainties in the final product. Here, our goal is to quantify the potential benefits of having high-temporal resolution observations from a geostationary (GEO) ocean color sensor to reduce interpolation errors in the reconstructed hourly and daily chlorophyll-a products. To this aim, we used modeled chlorophyll-a fields from the Copernicus Marine Environment Monitoring Service’s (CMEMS) Baltic Monitoring and Forecasting Centre (BAL MFC) and satellite cloud observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor (on board the geostationary satellite METEOSAT). The sampling of a GEO was thus simulated by combining the hourly chlorophyll fields and clouds masks, then hourly and daily chlorophyll-a products were generated after interpolation from neighboring valid data using the Multi-Channel Singular Spectral Analysis (M-SSA). Two cases are discussed: (i) A reconstruction based on the typical sampling of a LEO and, (ii) a simulation of a GEO sampling with hourly observations. The results show that the root mean square and interpolation bias errors are significantly reduced using hourly observations. Full article

(This article belongs to the Special Issue Remote Sensing of Short-Term Coastal Ocean Processes Enabled from Geostationary Vantage Point)

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