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

Mapping Intimacies ◽

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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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 dark water research aerosol retrieval algorithm – Part 1: Coupled 1.1 km ocean surface chlorophyll <i>a</i> retrievals with empirical calibration corrections

Atmospheric Measurement Techniques ◽

10.5194/amt-10-1539-2017 ◽

2017 ◽

Vol 10 (4) ◽

pp. 1539-1555 ◽

Cited By ~ 18

Author(s):

James A. Limbacher ◽

Ralph A. Kahn

Keyword(s):

Storage System ◽

Ocean Color ◽

Atmospheric Correction ◽

Ocean Surface ◽

Single Scattering ◽

Single Scattering Albedo ◽

Retrieval Algorithm ◽

Radiometric Calibration ◽

Color Analysis

Abstract. As aerosol amount and type are key factors in the atmospheric correction required for remote-sensing chlorophyll a concentration (Chl) retrievals, the Multi-angle Imaging SpectroRadiometer (MISR) can contribute to ocean color analysis despite a lack of spectral channels optimized for this application. Conversely, an improved ocean surface constraint should also improve MISR aerosol-type products, especially spectral single-scattering albedo (SSA) retrievals. We introduce a coupled, self-consistent retrieval of Chl together with aerosol over dark water. There are time-varying MISR radiometric calibration errors that significantly affect key spectral reflectance ratios used in the retrievals. Therefore, we also develop and apply new calibration corrections to the MISR top-of-atmosphere (TOA) reflectance data, based on comparisons with coincident MODIS (Moderate Resolution Imaging Spectroradiometer) observations and trend analysis of the MISR TOA bidirectional reflectance factors (BRFs) over three pseudo-invariant desert sites. We run the MISR research retrieval algorithm (RA) with the corrected MISR reflectances to generate MISR-retrieved Chl and compare the MISR Chl values to a set of 49 coincident SeaBASS (SeaWiFS Bio-optical Archive and Storage System) in situ observations. Where Chlin situ < 1.5 mg m−3, the results from our Chl model are expected to be of highest quality, due to algorithmic assumption validity. Comparing MISR RA Chl to the 49 coincident SeaBASS observations, we report a correlation coefficient (r) of 0.86, a root-mean-square error (RMSE) of 0.25, and a median absolute error (MAE) of 0.10. Statistically, a two-sample Kolmogorov–Smirnov test indicates that it is not possible to distinguish between MISR Chl and available SeaBASS in situ Chl values (p > 0.1). We also compare MODIS–Terra and MISR RA Chl statistically, over much broader regions. With about 1.5 million MISR–MODIS collocations having MODIS Chl < 1.5 mg m−3, MISR and MODIS show very good agreement: r = 0. 96, MAE  =  0.09, and RMSE  =  0.15. The new dark water aerosol/Chl RA can retrieve Chl in low-Chl, case I waters, independent of other imagers such as MODIS, via a largely physical algorithm, compared to the commonly applied statistical ones. At a minimum, MISR's multi-angle data should help reduce uncertainties in the MODIS–Terra ocean color retrieval where coincident measurements are made, while also allowing for a more robust retrieval of particle properties such as spectral single-scattering albedo.

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

Remote Sensing ◽

10.3390/rs10101587 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1587 ◽

Cited By ~ 5

Author(s):

Maria Tzortziou ◽

Owen Parker ◽

Brian Lamb ◽

Jay Herman ◽

Lok Lamsal ◽

...

Keyword(s):

Remote Sensing ◽

Satellite Imagery ◽

Coastal Waters ◽

High Frequency ◽

Ocean Color ◽

Atmospheric Correction ◽

Coastal Ocean ◽

Short Term ◽

Diurnal Variability ◽

Air Mass

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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