Abstract

Abstract. NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of 2023, will carry a hyperspectral scanning radiometer named the Ocean Color Instrument (OCI) and two multi-angle polarimeters (MAPs): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). The MAP measurements contain rich information on the microphysical properties of aerosols and hydrosols and therefore can be used to retrieve accurate aerosol properties for complex atmosphere and ocean systems. Most polarimetric aerosol retrieval algorithms utilize vector radiative transfer models iteratively in an optimization approach, which leads to high computational costs that limit their usage in the operational processing of large data volumes acquired by the MAP imagers. In this work, we propose a deep neural network (NN) forward model to represent the radiative transfer simulation of coupled atmosphere and ocean systems for applications to the HARP2 instrument and its predecessors. Through the evaluation of synthetic datasets for AirHARP (airborne version of HARP2), the NN model achieves a numerical accuracy smaller than the instrument uncertainties, with a running time of 0.01 s in a single CPU core or 1 ms in a GPU. Using the NN as a forward model, we built an efficient joint aerosol and ocean color retrieval algorithm called FastMAPOL, evolved from the well-validated Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm. Retrievals of aerosol properties and water-leaving signals were conducted on both the synthetic data and the AirHARP field measurements from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign in 2017. From the validation with the synthetic data and the collocated High Spectral Resolution Lidar (HSRL) aerosol products, we demonstrated that the aerosol microphysical properties and water-leaving signals can be retrieved efficiently and within acceptable error. Comparing to the retrieval speed using a conventional radiative transfer forward model, the computational acceleration is 103 times faster with CPU or 104 times with GPU processors. The FastMAPOL algorithm can be used to operationally process the large volume of polarimetric data acquired by PACE and other future Earth-observing satellite missions with similar capabilities.

Highlights

  • Atmospheric aerosols are tiny particles suspended in the atmosphere, such as dust, sea salt, and volcanic ash, that play important roles in air quality (Shiraiwa et al, 2017; Li et al, 2017) and Earth’s climate (Boucher et al, 2013)

  • Accurate knowledge of aerosol optical properties is important for atmospheric correction in ocean color remote sensing, wherein the spectral water-leaving radiances are retrieved by subtracting the contributions of the atmosphere and ocean surface from the spaceborne or airborne measurements made at the top of atmosphere (TOA; Mobley et al, 2016)

  • After training the neural network (NN) model, we evaluated its accuracy using synthetic AirHARP measurements generated from the 1000 simulation cases which have not been used in the training and validation process

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Summary

Introduction

Atmospheric aerosols are tiny particles suspended in the atmosphere, such as dust, sea salt, and volcanic ash, that play important roles in air quality (Shiraiwa et al, 2017; Li et al, 2017) and Earth’s climate (Boucher et al, 2013). The high computational costs of the RT simulations pose great challenges in the operational processing of the large data volumes acquired by the MAP imagers To alleviate this issue, the SPEX team represented the polarimetric reflectance for an open-ocean system using a deep neural network (NN) and coupled it with a radiative transfer model for the atmosphere (Fan et al, 2019). We present a joint retrieval algorithm for aerosol properties and water-leaving signals that uses a deep NN model to replace the radiative transfer forward model for simulation of the polarimetric reflectances This approach is one step further than Fan et al (2019), as both the atmospheric and oceanic radiative transfer processes are represented by the NN. The paper is organized into seven sections: Sect. 2 reviews the retrieval algorithm and its radiative transfer forward model, Sect. 3 discusses the training and accuracy of the NN forward model, Sect. 4. applies the NN forward model to aerosol and water-leaving signal retrievals from the synthetic AirHARP data, Sect. 5. discusses the retrievals on AirHARP field measurements from the ACEPOL campaign, and Sects. 6 and 7 provide discussions and conclusions

Joint aerosol and ocean color retrieval algorithm
Forward model
Remote sensing reflectance
Neural network for forward model
Training data
Neural network training
Neural network accuracy
Neural network model for remote sensing reflectance
Joint retrieval results on synthetic AirHARP measurements
Joint retrieval results on AirHARP measurements from ACEPOL
Findings
Discussion
Conclusions
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