Sea Salt Aerosol Identification Based on Multispectral Optical Properties and Its Impact on Radiative Forcing over the Ocean

The ground-based measurement of sea salt (SS) aerosol over the ocean requires the massive utilization of satellite-derived aerosol products. In this study, n-order spectral derivatives of aerosol optical depth (AOD) based on wavelength were examined to characterize SS and other aerosol types in term...

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Published in:Remote Sensing
Main Authors: Dwi Atmoko, Tang-Huang Lin
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2022
Subjects:
Online Access:https://doi.org/10.3390/rs14133188
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spelling ftmdpi:oai:mdpi.com:/2072-4292/14/13/3188/ 2023-08-20T03:59:11+02:00 Sea Salt Aerosol Identification Based on Multispectral Optical Properties and Its Impact on Radiative Forcing over the Ocean Dwi Atmoko Tang-Huang Lin agris 2022-07-02 application/pdf https://doi.org/10.3390/rs14133188 EN eng Multidisciplinary Digital Publishing Institute Atmospheric Remote Sensing https://dx.doi.org/10.3390/rs14133188 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 13; Pages: 3188 sea salt aerosol aerosol optical depth (AOD) spectral derivatives particle size complex refractive index normalized derivative aerosol index (NDAI) Text 2022 ftmdpi https://doi.org/10.3390/rs14133188 2023-08-01T05:35:25Z The ground-based measurement of sea salt (SS) aerosol over the ocean requires the massive utilization of satellite-derived aerosol products. In this study, n-order spectral derivatives of aerosol optical depth (AOD) based on wavelength were examined to characterize SS and other aerosol types in terms of their spectral dependence related to their optical properties such as particle size distributions and complex refractive indices. Based on theoretical simulations from the second simulation of a satellite signal in the solar spectrum (6S) model, AOD spectral derivatives of SS were characterized along with other major types including mineral dust (DS), biomass burning (BB), and anthropogenic pollutants (APs). The approach (normalized derivative aerosol index, NDAI) of partitioning aerosol types with intrinsic values of particle size distribution and complex refractive index from normalized first- and second-order derivatives was applied to the datasets from a moderate resolution imaging spectroradiometer (MODIS) as well as by the ground-based aerosol robotic network (AERONET). The results after implementation from multiple sources of data indicated that the proposed approach could be highly effective for identifying and segregating abundant SS from DS, BB, and AP, across an ocean. Consequently, each aerosol’s shortwave radiative forcing and its efficiency could be further estimated in order to predict its impact on the climate. Text Aerosol Robotic Network MDPI Open Access Publishing Remote Sensing 14 13 3188
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic sea salt aerosol
aerosol optical depth (AOD)
spectral derivatives
particle size
complex refractive index
normalized derivative aerosol index (NDAI)
spellingShingle sea salt aerosol
aerosol optical depth (AOD)
spectral derivatives
particle size
complex refractive index
normalized derivative aerosol index (NDAI)
Dwi Atmoko
Tang-Huang Lin
Sea Salt Aerosol Identification Based on Multispectral Optical Properties and Its Impact on Radiative Forcing over the Ocean
topic_facet sea salt aerosol
aerosol optical depth (AOD)
spectral derivatives
particle size
complex refractive index
normalized derivative aerosol index (NDAI)
description The ground-based measurement of sea salt (SS) aerosol over the ocean requires the massive utilization of satellite-derived aerosol products. In this study, n-order spectral derivatives of aerosol optical depth (AOD) based on wavelength were examined to characterize SS and other aerosol types in terms of their spectral dependence related to their optical properties such as particle size distributions and complex refractive indices. Based on theoretical simulations from the second simulation of a satellite signal in the solar spectrum (6S) model, AOD spectral derivatives of SS were characterized along with other major types including mineral dust (DS), biomass burning (BB), and anthropogenic pollutants (APs). The approach (normalized derivative aerosol index, NDAI) of partitioning aerosol types with intrinsic values of particle size distribution and complex refractive index from normalized first- and second-order derivatives was applied to the datasets from a moderate resolution imaging spectroradiometer (MODIS) as well as by the ground-based aerosol robotic network (AERONET). The results after implementation from multiple sources of data indicated that the proposed approach could be highly effective for identifying and segregating abundant SS from DS, BB, and AP, across an ocean. Consequently, each aerosol’s shortwave radiative forcing and its efficiency could be further estimated in order to predict its impact on the climate.
format Text
author Dwi Atmoko
Tang-Huang Lin
author_facet Dwi Atmoko
Tang-Huang Lin
author_sort Dwi Atmoko
title Sea Salt Aerosol Identification Based on Multispectral Optical Properties and Its Impact on Radiative Forcing over the Ocean
title_short Sea Salt Aerosol Identification Based on Multispectral Optical Properties and Its Impact on Radiative Forcing over the Ocean
title_full Sea Salt Aerosol Identification Based on Multispectral Optical Properties and Its Impact on Radiative Forcing over the Ocean
title_fullStr Sea Salt Aerosol Identification Based on Multispectral Optical Properties and Its Impact on Radiative Forcing over the Ocean
title_full_unstemmed Sea Salt Aerosol Identification Based on Multispectral Optical Properties and Its Impact on Radiative Forcing over the Ocean
title_sort sea salt aerosol identification based on multispectral optical properties and its impact on radiative forcing over the ocean
publisher Multidisciplinary Digital Publishing Institute
publishDate 2022
url https://doi.org/10.3390/rs14133188
op_coverage agris
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source Remote Sensing; Volume 14; Issue 13; Pages: 3188
op_relation Atmospheric Remote Sensing
https://dx.doi.org/10.3390/rs14133188
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs14133188
container_title Remote Sensing
container_volume 14
container_issue 13
container_start_page 3188
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