The impact and estimation of uncertainty correlation for multi-angle polarimetric remote sensing of aerosols and ocean color
<p>Multi-angle polarimetric (MAP) measurements contain rich information for characterization of aerosol microphysical and optical properties that can be used to improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple ge...
| 主要な著者: | , , , , , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Copernicus Publications
2023-04-01
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| シリーズ: | Atmospheric Measurement Techniques |
| オンライン･アクセス: | https://amt.copernicus.org/articles/16/2067/2023/amt-16-2067-2023.pdf |
| _version_ | 1827964522183262208 |
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| author | M. Gao M. Gao K. Knobelspiesse B. A. Franz P.-W. Zhai B. Cairns X. Xu J. V. Martins |
| author_facet | M. Gao M. Gao K. Knobelspiesse B. A. Franz P.-W. Zhai B. Cairns X. Xu J. V. Martins |
| author_sort | M. Gao |
| collection | DOAJ |
| description | <p>Multi-angle polarimetric (MAP) measurements contain rich information for characterization of aerosol microphysical and optical properties that can be used to improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere–ocean system, although uncertainty correlation among measurements is generally ignored due to lack of knowledge on its strength and characterization. In this work, we provide a practical framework to evaluate the impact of the angular uncertainty correlation from retrieval results and a method to estimate correlation strength from retrieval fitting residuals. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on neural-network forward models, is used to conduct the retrievals and uncertainty quantification. In addition, we also discuss a flexible approach to include a correlated uncertainty model in the retrieval algorithm. The impact of angular correlation on retrieval uncertainties is discussed based on synthetic Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) and Hyper-Angular Rainbow Polarimeter 2 (HARP2) measurements using a Monte Carlo uncertainty estimation method. Correlation properties are estimated using autocorrelation functions based on the fitting residuals from both synthetic AirHARP and HARP2 data and real AirHARP measurement, with the resulting angular correlation parameters found to be larger than 0.9 and 0.8 for reflectance and degree of linear polarization (DoLP), respectively, which correspond to correlation angles of 10 and 5<span class="inline-formula"><sup>∘</sup></span>. Although this study focuses on angular correlation from HARP instruments, the methodology to study and quantify uncertainty correlation is also applicable to other instruments with angular, spectral, or spatial correlations and can help inform laboratory calibration and characterization of the instrument uncertainty structure.</p> |
| first_indexed | 2024-04-09T17:17:47Z |
| format | Article |
| id | doaj.art-df2c4a54194a4ad6a8d49b6aab2b8419 |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-04-09T17:17:47Z |
| publishDate | 2023-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-df2c4a54194a4ad6a8d49b6aab2b84192023-04-19T12:02:09ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482023-04-01162067208710.5194/amt-16-2067-2023The impact and estimation of uncertainty correlation for multi-angle polarimetric remote sensing of aerosols and ocean colorM. Gao0M. Gao1K. Knobelspiesse2B. A. Franz3P.-W. Zhai4B. Cairns5X. Xu6J. V. Martins7NASA Goddard Space Flight Center, Code 616, Greenbelt, MD 20771, USAScience Systems and Applications, Inc., Greenbelt, MD 20706, USANASA Goddard Space Flight Center, Code 616, Greenbelt, MD 20771, USANASA Goddard Space Flight Center, Code 616, Greenbelt, MD 20771, USAPhysics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USANASA Goddard Institute for Space Studies, New York, NY 10025, USAPhysics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USAPhysics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA<p>Multi-angle polarimetric (MAP) measurements contain rich information for characterization of aerosol microphysical and optical properties that can be used to improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere–ocean system, although uncertainty correlation among measurements is generally ignored due to lack of knowledge on its strength and characterization. In this work, we provide a practical framework to evaluate the impact of the angular uncertainty correlation from retrieval results and a method to estimate correlation strength from retrieval fitting residuals. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on neural-network forward models, is used to conduct the retrievals and uncertainty quantification. In addition, we also discuss a flexible approach to include a correlated uncertainty model in the retrieval algorithm. The impact of angular correlation on retrieval uncertainties is discussed based on synthetic Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) and Hyper-Angular Rainbow Polarimeter 2 (HARP2) measurements using a Monte Carlo uncertainty estimation method. Correlation properties are estimated using autocorrelation functions based on the fitting residuals from both synthetic AirHARP and HARP2 data and real AirHARP measurement, with the resulting angular correlation parameters found to be larger than 0.9 and 0.8 for reflectance and degree of linear polarization (DoLP), respectively, which correspond to correlation angles of 10 and 5<span class="inline-formula"><sup>∘</sup></span>. Although this study focuses on angular correlation from HARP instruments, the methodology to study and quantify uncertainty correlation is also applicable to other instruments with angular, spectral, or spatial correlations and can help inform laboratory calibration and characterization of the instrument uncertainty structure.</p>https://amt.copernicus.org/articles/16/2067/2023/amt-16-2067-2023.pdf |
| spellingShingle | M. Gao M. Gao K. Knobelspiesse B. A. Franz P.-W. Zhai B. Cairns X. Xu J. V. Martins The impact and estimation of uncertainty correlation for multi-angle polarimetric remote sensing of aerosols and ocean color Atmospheric Measurement Techniques |
| title | The impact and estimation of uncertainty correlation for multi-angle polarimetric remote sensing of aerosols and ocean color |
| title_full | The impact and estimation of uncertainty correlation for multi-angle polarimetric remote sensing of aerosols and ocean color |
| title_fullStr | The impact and estimation of uncertainty correlation for multi-angle polarimetric remote sensing of aerosols and ocean color |
| title_full_unstemmed | The impact and estimation of uncertainty correlation for multi-angle polarimetric remote sensing of aerosols and ocean color |
| title_short | The impact and estimation of uncertainty correlation for multi-angle polarimetric remote sensing of aerosols and ocean color |
| title_sort | impact and estimation of uncertainty correlation for multi angle polarimetric remote sensing of aerosols and ocean color |
| url | https://amt.copernicus.org/articles/16/2067/2023/amt-16-2067-2023.pdf |
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