A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations : A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations

This study developed a new atmospheric correction algorithm, GeoNEX-AC, that is independent from the traditional use of spectral band ratios but dedicated to exploiting information from the diurnal variability in the hypertemporal geostationary observations. The algorithm starts by evaluating smooth...

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Main Authors: Wang, Weile, Wang, Yujie, Lyapustin, Alexei, Hashimoto, Hirofumi, Park, Taejin, Michaelis, Andrew, Nemani, Ramakrishna
Format: Article in Journal/Newspaper
Language:unknown
Published: MDPI 2022
Subjects:
Aerosol Robotic Network
Online Access:https://dx.doi.org/10.13016/m2npln-ox0b
https://mdsoar.org/handle/11603/24399
id ftdatacite:10.13016/m2npln-ox0b
record_format openpolar
spelling ftdatacite:10.13016/m2npln-ox0b 2023-05-15T13:06:20+02:00 A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations : A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations Wang, Weile Wang, Yujie Lyapustin, Alexei Hashimoto, Hirofumi Park, Taejin Michaelis, Andrew Nemani, Ramakrishna 2022 https://dx.doi.org/10.13016/m2npln-ox0b https://mdsoar.org/handle/11603/24399 unknown MDPI Public Domain Mark 1.0 This is a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law. http://creativecommons.org/publicdomain/mark/1.0/ PDM article CreativeWork 2022 ftdatacite https://doi.org/10.13016/m2npln-ox0b 2022-04-01T16:49:48Z This study developed a new atmospheric correction algorithm, GeoNEX-AC, that is independent from the traditional use of spectral band ratios but dedicated to exploiting information from the diurnal variability in the hypertemporal geostationary observations. The algorithm starts by evaluating smooth segments of the diurnal time series of the top-of-atmosphere (TOA) reflectance to identify clear-sky and snow-free observations. It then attempts to retrieve the Ross-Thick–Li-Sparse (RTLS) surface bi-directional reflectance distribution function (BRDF) parameters and the daily mean atmospheric optical depth (AOD) with an atmospheric radiative transfer model (RTM) to optimally simulate the observed diurnal variability in the clear-sky TOA reflectance. Once the initial RTLS parameters are retrieved after the algorithm’s burn-in period, they serve as the prior information to estimate the AOD levels for the following days and update the surface BRDF information with the new clear-sky observations. This process is iterated through the full time span of the observations, skipping only totally cloudy days or when surface snow is detected. We tested the algorithm over various Aerosol Robotic Network (AERONET) sites and the retrieved results well agree with the ground-based measurements. This study demonstrates that the high-frequency diurnal geostationary observations contain unique information that can help to address the atmospheric correction problem from new directions Article in Journal/Newspaper Aerosol Robotic Network DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
description This study developed a new atmospheric correction algorithm, GeoNEX-AC, that is independent from the traditional use of spectral band ratios but dedicated to exploiting information from the diurnal variability in the hypertemporal geostationary observations. The algorithm starts by evaluating smooth segments of the diurnal time series of the top-of-atmosphere (TOA) reflectance to identify clear-sky and snow-free observations. It then attempts to retrieve the Ross-Thick–Li-Sparse (RTLS) surface bi-directional reflectance distribution function (BRDF) parameters and the daily mean atmospheric optical depth (AOD) with an atmospheric radiative transfer model (RTM) to optimally simulate the observed diurnal variability in the clear-sky TOA reflectance. Once the initial RTLS parameters are retrieved after the algorithm’s burn-in period, they serve as the prior information to estimate the AOD levels for the following days and update the surface BRDF information with the new clear-sky observations. This process is iterated through the full time span of the observations, skipping only totally cloudy days or when surface snow is detected. We tested the algorithm over various Aerosol Robotic Network (AERONET) sites and the retrieved results well agree with the ground-based measurements. This study demonstrates that the high-frequency diurnal geostationary observations contain unique information that can help to address the atmospheric correction problem from new directions
format Article in Journal/Newspaper
author Wang, Weile
Wang, Yujie
Lyapustin, Alexei
Hashimoto, Hirofumi
Park, Taejin
Michaelis, Andrew
Nemani, Ramakrishna
spellingShingle Wang, Weile
Wang, Yujie
Lyapustin, Alexei
Hashimoto, Hirofumi
Park, Taejin
Michaelis, Andrew
Nemani, Ramakrishna
A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations : A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations
author_facet Wang, Weile
Wang, Yujie
Lyapustin, Alexei
Hashimoto, Hirofumi
Park, Taejin
Michaelis, Andrew
Nemani, Ramakrishna
author_sort Wang, Weile
title A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations : A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations
title_short A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations : A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations
title_full A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations : A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations
title_fullStr A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations : A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations
title_full_unstemmed A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations : A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations
title_sort novel atmospheric correction algorithm to exploit the diurnal variability in hypertemporal geostationary observations : a novel atmospheric correction algorithm to exploit the diurnal variability in hypertemporal geostationary observations
publisher MDPI
publishDate 2022
url https://dx.doi.org/10.13016/m2npln-ox0b
https://mdsoar.org/handle/11603/24399
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_rights Public Domain Mark 1.0
This is a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
http://creativecommons.org/publicdomain/mark/1.0/
op_rightsnorm PDM
op_doi https://doi.org/10.13016/m2npln-ox0b
_version_ 1766001508706418688

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