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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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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1774719088130523136 |