Publication Abstracts

Gao et al. 2021

Gao, M., K. Knobelspiesse, B.A. Franz, P.-W. Zhai, V. Martins, S.P. Burton, B. Cairns, R. Ferrare, M.A. Fenn, O. Hasekamp, Y. Hu, A. Ibrahim, A.M. Sayer, P.J. Werdell, and X. Xu, 2021: Adaptive data screening for multi-angle polarimetric aerosol and ocean color remote sensing accelerated by deep learning. Front. Remote Sens., 2, 757832, doi:10.3389/frsen.2021.757832.

Remote sensing measurements from multi-angle polarimeters (MAPs) contain rich aerosol microphysical property information, and these sensors have been used to perform retrievals in optically complex atmosphere and ocean systems. Previous studies have concluded that, generally, five moderately separated viewing angles in each spectral band provide sufficient accuracy for aerosol property retrievals, with performance gradually saturating as angles are added above that threshold. The Hyper-Angular Rainbow Polarimeter (HARP) instruments provide high angular sampling with a total of 90-120 unique angles across four bands, a capability developed mainly for liquid cloud retrievals. In practice, not all view angles are optimal for aerosol retrievals due to impacts of clouds, sunglint, and other impediments. The many viewing angles of HARP can provide resilience to these effects, if the impacted views are screened from the dataset, as the remaining views may be sufficient for successful analysis. In this study, we discuss how the number of available viewing angles impacts aerosol and ocean color retrieval uncertainties, as applied to two versions of the HARP instrument. AirHARP is an airborne prototype that was deployed in the ACEPOL field campaign, while HARP2 is an instrument in development for the upcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Based on synthetic data, we find that a total of 20-30 angles across all bands (i.e., five to eight viewing angles per band) are sufficient to achieve good retrieval performance. Following from this result, we develop an adaptive multi-angle polarimetric data screening (MAPDS) approach to evaluate data quality by comparing measurements with their best-fitted forward model. The FastMAPOL retrieval algorithm is used to retrieve scene geophysical values, by matching an efficient, deep learning-based, radiative transfer emulator to observations. The data screening method effectively identifies and removes viewing angles affected by thin cirrus clouds and other anomalies, improving retrieval performance. This was tested with AirHARP data, and we found agreement with the High Spectral Resolution Lidar-2 (HSRL-2) aerosol data. The data screening approach can be applied to modern satellite remote sensing missions, such as PACE, where a large amount of multi-angle, hyperspectral, polarimetric measurements will be collected. %C& MAP

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BibTeX Citation

  author={Gao, M. and Knobelspiesse, K. and Franz, B. A. and Zhai, P.-W. and Martins, V. and Burton, S. P. and Cairns, B. and Ferrare, R. and Fenn, M. A. and Hasekamp, O. and Hu, Y. and Ibrahim, A. and Sayer, A. M. and Werdell, P. J. and Xu, X.},
  title={Adaptive data screening for multi-angle polarimetric aerosol and ocean color remote sensing accelerated by deep learning},
  journal={Front. Remote Sens.},

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RIS Citation

ID  - ga06300k
AU  - Gao, M.
AU  - Knobelspiesse, K.
AU  - Franz, B. A.
AU  - Zhai, P.-W.
AU  - Martins, V.
AU  - Burton, S. P.
AU  - Cairns, B.
AU  - Ferrare, R.
AU  - Fenn, M. A.
AU  - Hasekamp, O.
AU  - Hu, Y.
AU  - Ibrahim, A.
AU  - Sayer, A. M.
AU  - Werdell, P. J.
AU  - Xu, X.
PY  - 2021
TI  - Adaptive data screening for multi-angle polarimetric aerosol and ocean color remote sensing accelerated by deep learning
JA  - Front. Remote Sens.
VL  - 2
SP  - 757832
DO  - 10.3389/frsen.2021.757832
ER  -

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