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

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, Rrs, 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.

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

Remote Sensing ◽

10.3390/rs11030295 ◽

2019 ◽

Vol 11 (3) ◽

pp. 295 ◽

Cited By ~ 4

Author(s):

Javier Concha ◽

Antonio Mannino ◽

Bryan Franz ◽

Wonkook Kim

Keyword(s):

Remote Sensing ◽

Ocean Color ◽

Diurnal Changes ◽

Biogeochemical Processes ◽

Short Term ◽

Study Region ◽

Diurnal Variability ◽

Water Properties ◽

Remote Sensing Reflectance ◽

Polar Orbiting Satellite

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 (Rrs) 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 (ag (412)), and 3.7 mg m−3 for particulate organic carbon (POC). These Rrs values can be considered the threshold values for detectable changes of the in-water properties due to biological, physical or biogeochemical processes from GOCI.

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Assessing Diurnal Variability of Biogeochemical Processes Using the Geostationary Ocean Color Imager (GOCI)

10.20944/preprints201811.0042.v1 ◽

2018 ◽

Author(s):

Javier Concha ◽

Antonio Mannino ◽

Bryan Franz ◽

Wonkook Kim

Keyword(s):

Remote Sensing ◽

Ocean Color ◽

Biogeochemical Processes ◽

Short Term ◽

Diurnal Variability ◽

Water Properties ◽

Water Region ◽

The Republic ◽

Polar Orbiting Satellite ◽

Hourly Observation

Short-term (hours) 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 geostationary orbits permit the collection of multiple images throughout each day. To assess the capability to detect changes in water properties caused by these processes, however, requires an understaning of the uncertainties introduced by the instrument and/or geophysical retrieval algorithms. This work presents a study of the variability during the day over a water region of low-productivity with the assumption that only small changes in the water properties occur during the day over the area of study. The complete GOCI mission data were processed using the SeaDAS/l2gen package. Filtering criteria were applied to assure the quality of the data. Relative differences with respect to the midday value were calculated for each hourly observation of the day. Also, the influence of the solar zenith angle in the retrieval of remote sensing reflectances and derived products was analyzed. We determined that the uncertainties in water-leaving “remote-sensing” reflectance ($R_\text{rs}$) for the 412, 443, 490, 555, 660 and 680 nm bands on GOCI are 8.05$\times10^{-4}$, 5.49$\times10^{-4}$, 4.48$\times10^{-4}$, 2.51$\times10^{-4}$, 8.83$\times10^{-5}$, and 1.36$\times10^{-4}$ sr$^{-1}$, respectively, and 1.09$\times10^{-2}$ mg m$^{-3}$ for the chlorophyll-a concentration (Chl-{\it a}), 2.09$\times10^{-3}$ m$^{-1}$ for the absorption coefficient of chromophoric dissolved organic matter at 412 nm ($a_{\text{g}}(412)$), and 3.7 mg m$^{-3}$ for particulate organic carbon (POC). We consider these to be the floor values for detectable changes in the water properties due to biological, physical or chemical processes.

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Water Environment Remote Sensing Atmospheric Correction of Geostationary Ocean Color Imager Data over Turbid Coastal Waters in the Bohai Sea Using Artificial Neural Networks

Current Science ◽

10.18520/cs/v110/i6/1079-1085 ◽

2016 ◽

Vol 110 (6) ◽

pp. 1079

Author(s):

Liqiao Tian ◽

Qun Zeng ◽

Xiaojuan Tian ◽

Jian Li ◽

Zheng Wang ◽

...

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Artificial Neural Networks ◽

Coastal Waters ◽

Bohai Sea ◽

Ocean Color ◽

Atmospheric Correction ◽

Water Environment ◽

The Bohai Sea ◽

Artificial Neural

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Updated MISR Over-Water Research Aerosol Retrieval Algorithm Part 2: A Multi- Angle Aerosol Retrieval Algorithm for Shallow, Turbid, Oligotrophic, and Eutrophic Waters

10.5194/amt-2018-205 ◽

2018 ◽

Author(s):

James A. Limbacher ◽

Ralph A. Kahn

Keyword(s):

Remote Sensing ◽

Coastal Waters ◽

Ocean Color ◽

Atmospheric Correction ◽

Ocean Surface ◽

Retrieval Algorithm ◽

Biological Productivity ◽

Single View ◽

Aerosol Retrieval ◽

Ångström Exponent

Abstract. Coastal waters serve as transport pathways to the ocean for all agricultural and other runoff from terrestrial sources; they are also some of the most biologically productive on the planet. Estimating the impact coastal waters have on the global carbon budget requires relating satellite-based remote-sensing retrievals of biological productivity (e.g., Chlorophyll-a concentration) to in-situ measurements taken in near-surface waters. The Multi-angle Imaging SpectroRadiometer (MISR) can uniquely constrain the “atmospheric correction” needed to derive ocean color from remote-sensing imagers. Here, we retrieve aerosol amount and type from MISR over all types of water. The primary limitation is an upper bound on aerosol optical depth (AOD), as the algorithm must be able to distinguish the surface. This updated MISR research aerosol retrieval algorithm (RA) also assumes that light reflection by the underlying ocean surface is Lambertian. The RA computes the ocean surface reflectance (Rrs) analytically for a given AOD, aerosol optical model, and wind speed. We provide retrieval examples over shallow, turbid, and eutrophic waters and introduce a productivity/turbidity index (PTI), calculated from retrieved spectral Rrs, that distinguished water types (similar to NDVI over land). We also validate the new algorithm by comparing spectral AOD and Angstrom exponent (ANG) results with 2419 collocated AERosol RObotic NETwork (AERONET) observations. For AERONET 558 nm interpolated AOD 0.20, the ANG RMSE is 0.25 and r = 0.89. Although MISR RA AOD retrieval quality does not appear to be substantially impacted by the presence of turbid water, MISR RA-retrieved Angstrom exponent seems to suffer from increased uncertainty under such conditions. MISR supplements current ocean color sources in regions where sun glint precludes retrievals from single-view-angle instruments. MISR atmospheric correction should also be more robust than that derived from single-view instruments such as MODIS. This is especially true in regions of shallow, turbid, and eutrophic waters, locations where biological productivity can be high, and single-view angle retrieval algorithms struggle to separate atmospheric from oceanic features.

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Updated MISR over-water research aerosol retrieval algorithm – Part 2: A multi-angle aerosol retrieval algorithm for shallow, turbid, oligotrophic, and eutrophic waters

Atmospheric Measurement Techniques ◽

10.5194/amt-12-675-2019 ◽

2019 ◽

Vol 12 (1) ◽

pp. 675-689 ◽

Cited By ~ 9

Author(s):

James A. Limbacher ◽

Ralph A. Kahn

Keyword(s):

Remote Sensing ◽

Coastal Waters ◽

Ocean Color ◽

Atmospheric Correction ◽

Ocean Surface ◽

Retrieval Algorithm ◽

Biological Productivity ◽

Single View ◽

Aerosol Retrieval ◽

Ångström Exponent

Abstract. Coastal waters serve as transport pathways to the ocean for all agricultural and other runoff from terrestrial sources, and many are the sites for upwelling of nutrient-rich, deep water; they are also some of the most biologically productive on Earth. Estimating the impact coastal waters have on the global carbon budget requires relating satellite-based remote-sensing retrievals of biological productivity (e.g., chlorophyll a concentration) to in situ measurements taken in near-surface waters. The Multi-angle Imaging SpectroRadiometer (MISR) can uniquely constrain the “atmospheric correction” needed to derive ocean color from remote-sensing imagers. Here, we retrieve aerosol amount and type from MISR over all types of water. The primary limitation is an upper bound on aerosol optical depth (AOD), as the algorithm must be able to distinguish the surface. This updated MISR research aerosol retrieval algorithm (RA) also assumes that light reflection by the underlying ocean surface is Lambertian. The RA computes the ocean surface reflectance (Rrs) analytically for a given AOD, aerosol optical model, and wind speed. We provide retrieval examples over shallow, turbid, and eutrophic waters and introduce a productivity and turbidity index (PTI), calculated from retrieved spectral Rrs, that distinguished water types (similar to the the normalized difference vegetation index, NDVI, over land). We also validate the new algorithm by comparing spectral AOD and Ångström exponent (ANG) results with 2419 collocated AErosol RObotic NETwork (AERONET) observations. For AERONET 558 nm interpolated AOD < 1.0, the root-mean-square error (RMSE) is 0.04 and linear correlation coefficient is 0.95. For the 502 cloud-free MISR and AERONET collocations with an AERONET AOD > 0.20, the ANG RMSE is 0.25 and r is 0.89. Although MISR RA AOD retrieval quality does not appear to be substantially impacted by the presence of turbid water, the MISR-RA-retrieved Ångström exponent seems to suffer from increased uncertainty under such conditions. MISR supplements current ocean color sources in regions where sunglint precludes retrievals from single-view-angle instruments. MISR atmospheric correction should also be more robust than that derived from single-view instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). This is especially true in regions of shallow, turbid, and eutrophic waters, locations where biological productivity can be high, and single-view-angle retrieval algorithms struggle to separate atmospheric from oceanic features.

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Inversion of multiangular polarimetric measurements over open and coastal ocean waters: a joint retrieval algorithm for aerosol and water-leaving radiance properties

Atmospheric Measurement Techniques ◽

10.5194/amt-12-3921-2019 ◽

2019 ◽

Vol 12 (7) ◽

pp. 3921-3941 ◽

Cited By ~ 6

Author(s):

Meng Gao ◽

Peng-Wang Zhai ◽

Bryan A. Franz ◽

Yongxiang Hu ◽

Kirk Knobelspiesse ◽

...

Keyword(s):

Optical Properties ◽

Coastal Waters ◽

Ocean Color ◽

Atmospheric Correction ◽

Coastal Ocean ◽

High Spectral Resolution ◽

Radiative Transfer Model ◽

Retrieval Algorithm ◽

Transfer Model ◽

Ocean Water

Abstract. Ocean color remote sensing is a challenging task over coastal waters due to the complex optical properties of aerosols and hydrosols. In order to conduct accurate atmospheric correction, we previously implemented a joint retrieval algorithm, hereafter referred to as the Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm, to obtain the aerosol and water-leaving signal simultaneously. The MAPOL algorithm has been validated with synthetic data generated by a vector radiative transfer model, and good retrieval performance has been demonstrated in terms of both aerosol and ocean water optical properties (Gao et al., 2018). In this work we applied the algorithm to airborne polarimetric measurements from the Research Scanning Polarimeter (RSP) over both open and coastal ocean waters acquired in two field campaigns: the Ship-Aircraft Bio-Optical Research (SABOR) in 2014 and the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) in 2015 and 2016. Two different yet related bio-optical models are designed for ocean water properties. One model aligns with traditional open ocean water bio-optical models that parameterize the ocean optical properties in terms of the concentration of chlorophyll a. The other is a generalized bio-optical model for coastal waters that includes seven free parameters to describe the absorption and scattering by phytoplankton, colored dissolved organic matter, and nonalgal particles. The retrieval errors of both aerosol optical depth and the water-leaving radiance are evaluated. Through the comparisons with ocean color data products from both in situ measurements and the Moderate Resolution Imaging Spectroradiometer (MODIS), and the aerosol product from both the High Spectral Resolution Lidar (HSRL) and the Aerosol Robotic Network (AERONET), the MAPOL algorithm demonstrates both flexibility and accuracy in retrieving aerosol and water-leaving radiance properties under various aerosol and ocean water conditions.

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Inversion of multi-angular polarimetric measurements over open and coastal ocean waters: a joint retrieval algorithm for aerosol and water leaving radiance properties

10.5194/amt-2019-67 ◽

2019 ◽

Cited By ~ 2

Author(s):

Meng Gao ◽

Peng-Wang Zhai ◽

Bryan Franz ◽

Yongxiang Hu ◽

Kirk Knobelspiesse ◽

...

Keyword(s):

Optical Properties ◽

Coastal Waters ◽

Ocean Color ◽

Atmospheric Correction ◽

Coastal Ocean ◽

High Spectral Resolution ◽

Radiative Transfer Model ◽

Retrieval Algorithm ◽

Transfer Model ◽

Ocean Water

Abstract. Ocean color remote sensing is a challenging task over coastal waters due to the complex optical properties of aerosols and hydrosols. In order to conduct accurate atmospheric correction, we previously implemented a joint retrieval algorithm to obtain the aerosol and water leaving signal simultaneously. The algorithm has been validated with synthetic data generated by a vector radiative transfer model and good retrieval performance has been demonstrated in terms of both aerosol and ocean water optical properties (Gao et al., 2018). In this work we applied the algorithm to airborne polarimetric measurements from the Research Scanning Polarimeter (RSP) over both open and coastal ocean waters acquired in two field campaigns: the Ship-Aircraft Bio-Optical Research (SABOR) in 2014 and the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) in 2015 and 2016. Two different yet related bio-optical models are designed for ocean water properties. One model aligns with traditional open ocean water bio-optical models that parameterize the ocean optical properties in terms of the concentration of chlorophyll a. The other is a generalized bio-optical model for coastal waters that includes seven free parameters to describe the absorption and scattering by phytoplankton, colored dissolved organic matter and non-algal particles. The retrieval errors of both aerosol optical depth and the water leaving radiance are evaluated. Through the comparisons with ocean color data products from both in situ measurements and the Moderate Resolution Imaging Spectroradiometer (MODIS), and the aerosol product from both the High Spectral Resolution Lidar (HSRL) and the Aerosol Robotic Network (AERONET), our algorithm demonstrates both flexibility and accuracy in retrieving aerosol and water leaving radiance properties under various aerosol and ocean water conditions.

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