Polarimetry is one of the most promising types of remote sensing for improved characterization of atmospheric aerosol. Indeed, aerosol particles constitute a highly variable atmospheric component characterized by a large number of parameters describing particle sizes, morphologies (including shape and internal structure), absorption and scattering properties, amounts, horizontal and vertical distribution, etc. Reliable monitoring of all these parameters is very challenging, and therefore the aerosol effects on climate and environment are considered to be among the most uncertain factors in climate and environmental research. In this regard, observations that provide both the angular distribution of the scattered atmospheric radiation as well as its polarization state at multiple wavelengths covering the UV–SWIR spectral range carry substantial implicit information on the atmospheric composition. Therefore, high expectations in improving aerosol characterization are associated with detailed passive photopolarimetric observations. The critical need to use space-borne polarimetry for global accurate monitoring of detailed aerosol properties was first articulated in the late 1980s and early 1990s. By now, several orbital instruments have already provided polarization observations from space, and a number of advanced missions are scheduled for launch in the coming years by international and national space agencies. The first and most extensive record of polarimetric imagery was provided by POLDER-I, POLDER-II, and POLDER/PARASOL multi-angle multi-spectral polarization sensors. Polarimetric observations with the POLDER-like design intended for collecting extensive multi-angular multi-spectral measurements will be provided by several instruments, such as the MAI/TG-2, CAPI/TanSat, and DPC/GF-5 sensors recently launched by the Chinese Space Agency. Instruments such as the 3MI/MetOp-SG, MAIA, SpexOne and HARP2 on PACE, POSP, SMAC, PCF, DPC–Lidar, ScanPol and MSIP/Aerosol-UA, MAP/Copernicus CO2 Monitoring, etc. are planned to be launched by different space agencies in the coming decade. The concepts of these future instruments, their technical designs, and the accompanying algorithm development have been tested intensively and analyzed using diverse airborne prototypes. Certain polarimetric capabilities have also been implemented in such satellite sensors as GOME-2/MetOp and SGLI/GCOM-C. A number of aerosol retrieval products have been developed based on the available measurements and successfully used for different scientific applications. However, the completeness and accuracy of aerosol data operationally derived from polarimetry do not yet appear to have reached the accuracy levels implied by theoretical sensitivity studies that analyzed the potential information content of satellite polarimetry. As a result, the dataset provided by MODIS is still most frequently used by the scientific community, yet this sensor has neither polarimetric nor multi-angular capabilities. Admittedly polarimetric multi-angular observations are highly complex and have extra sensitivities to aerosol particle morphology, vertical variability of aerosol properties, polarization of surface reflectance, etc. As such, they necessitate state-of-the-art forward modeling based on first-principles physics which remains rare, and conventional retrieval approaches based on look-up tables turn out to be unsuitable to fully exploit the information implicit in the measurements. Several new-generation retrieval approaches have recently been proposed to address these challenges. These methods use improved forward modeling of atmospheric (polarized) radiances and implement a search in the continuous space of solutions using rigorous statistically optimized inversions. Such techniques provide more accurate retrievals of the main aerosol parameters such as aerosol optical thickness and yield additional parameters such as aerosol absorption. However, the operational implementation of advanced retrieval approaches generally requires a significant extra effort, and the forward-modeling part of such retrievals still needs to be substantially improved. Ground-based passive polarimetric measurements have also been evolving over the past decade. Although polarimetry helps improve aerosol characterization, especially of the fine aerosol mode, the operators of major observational networks such as AERONET remain reluctant to include polarimetric measurements as part of routine retrievals owing to their high complexity and notable increase in effort required to acquire and interpret polarization data. In addition to remote-sensing observations, polarimetric characteristics of aerosol scattering have been measured in situ as well as in the laboratory using polar nephelometers. Such measurements constitute direct observations of single scattering with no contributions from multiple scattering effects and therefore provide unique data for the validation of aerosol optical models and retrieval concepts. This article overviews the above-mentioned polarimetric observations, their history and expected developments, and the state of resulting aerosol products. It also discusses the main achievements and challenges in the exploitation of polarimetry for the improved characterization of atmospheric aerosols.

https://hal.archives-ouvertes.fr/hal-03009287/document

Polarimetric remote sensing of atmospheric aerosols: Instruments, methodologies, results, and perspectives

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a ...

/pdf/new-era-of-air-quality-monitoring-from-space-geostationary-50am51exmi.pdf

New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

The Directional Polarimetric Camera (DPC) is the first Chinese multi-angle polarized earth observation satellite sensor, which will be launched onboard the GaoFen-5 Satellite in Chinese High-resolution Earth Observation Program. GaoFen-5 runs in a sun-synchronous orbit with an inclination angle of 98°, with the 13:30 PM local overpass time and the 2-days revisiting period. DPC employed a charge coupled device detection unit with 512 × 512 effective pixels from the 544 × 512 useful pixels, realizing spatial resolution of 3.3 km under a swath width of 1850 km. Meanwhile, DPC has 3 polarized channels (490, 670 and 865 nm) together with 5 non-polarized bands (443, 565, 763, 765 and 910 nm), and can obtain at least 9 viewing angles by continuously capturing series images over the same target on orbit. Based on the optimal estimation theory and improved bidirectional reflectance distribution function model, an inversion framework for the simultaneous retrieval of aerosol and surface parameters is presented by taking full advantage of available radiometric and polarimetric measurements. The retrieved wavelength-independent fine-mode and coarse-mode aerosol volumes are used to assess the DPC performance on the inversion capability of spectral aerosol optical depth (AOD), from which the Angstrom exponent and fine-mode fraction (FMF) could be further obtained. In addition, based on the synthetic DPC data for various observation geometries, aerosols and surface types, the aerosol inversion capabilities are systematically evaluated, and the information content analysis results show that the aerosol spectral optical properties can be well retrieved over various land surfaces.

Directional Polarimetric Camera (DPC): Monitoring aerosol spectral optical properties over land from satellite observation

. Fine particulate matter with a diameter of less than 2.5  µm ( PM2.5 ) has been used as an important atmospheric
environmental parameter mainly because of its impact on human health. PM2.5 is affected by both natural and anthropogenic factors that
usually have strong diurnal variations. Such information helps toward understanding the causes of air pollution, as well as our adaptation to
it. Most existing PM2.5 products have been derived from polar-orbiting satellites. This study exploits the use of the next-generation
geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) to document the diurnal variation in PM2.5 . Given the huge volume of satellite data,
based on the idea of gradient boosting, a highly efficient tree-based Light Gradient Boosting Machine (LightGBM) method by involving the
spatiotemporal characteristics of air pollution, namely the space-time LightGBM (STLG) model, is developed. An hourly PM2.5 dataset for
China (i.e., ChinaHigh PM2.5 ) at a 5  km spatial resolution is derived based on Himawari-8/AHI aerosol products with additional
environmental variables. Hourly PM2.5 estimates (number of data samples  =  1 415 188) are well correlated with ground measurements in
China (cross-validation coefficient of determination, CV- R2 =  0.85), with a root-mean-square error (RMSE) and mean absolute error (MAE) of
13.62 and 8.49  µg m−3 , respectively. Our model captures well the PM2.5 diurnal variations showing that pollution increases
gradually in the morning, reaching a peak at about 10:00 LT (GMT + 8), then decreases steadily until sunset. The proposed approach outperforms most
traditional statistical regression and tree-based machine-learning models with a much lower computational burden in terms of speed and memory, making
it most suitable for routine pollution monitoring.

/pdf/himawari-8-derived-diurnal-variations-in-ground-level-pm-2-5-19ui7wzhex.pdf

Himawari-8-derived diurnal variations in ground-level PM 2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

/pdf/a-review-of-coarse-mineral-dust-in-the-earth-system-3kd35wpn.pdf

A review of coarse mineral dust in the Earth system

An overview of Sun–Sky Radiometer Observation Network (SONET) measurements in China is presented. Based on observations at 16 distributed SONET sites in China, atmospheric aerosol parameters are acquired via standardization processes of operational measurement, maintenance, calibration, inversion, and quality control implemented since 2010. A climatology study is performed focusing on total columnar atmospheric aerosol characteristics, including optical (aerosol optical depth, AngstrOm exponent, fine-mode fraction, single-scattering albedo), physical (volume particle size distribution), chemical composition (black carbon; brown carbon; fine-mode scattering component, coarse-mode component; and aerosol water), and radiative properties (aerosol radiative forcing and efficiency). Data analyses show that aerosol optical depth is low in the west but high in the east of China. Aerosol composition also shows significant spatial and temporal variations, leading to noticeable diversities in optical and phy...

Comprehensive Study of Optical, Physical, Chemical, and Radiative Properties of Total Columnar Atmospheric Aerosols over China: An Overview of Sun-Sky Radiometer Observation Network (SONET) Measurements

Atmospheric conditions vary significantly in terms of the temporal and spatial scales. Therefore, it is critical to obtain atmospheric parameters synchronized with an image for atmospheric correction based on radiative transfer calculation methods. On 3 July 2020, the high resolution and multimode imaging satellite, Gao Fen Duo Mo (GFDM), which was the first civilian high-resolution remote sensing satellite equipped with the Synchronization Monitoring Atmospheric Corrector (SMAC), was launched. The SMAC is a multispectral and polarization detection device that is used to retrieve atmospheric parameters that are time-synchronized with the image sensor of GFDM in the same field-of-view. On the basis of the atmospheric parameters obtained from the SMAC, a synchronization atmospheric correction (Syn-AC) method is proposed to remove the influence of the atmosphere and the adjacency effects to retrieve the surface reflectance. The Syn-AC method was applied in the experiments of synchronous atmospheric correction for GFDM images, where the surface reflectance retrieved via the Syn-AC method was compared with the field-measured values. In addition, the classical correction method, the FLAASH, was applied in the experiments to compare its performance with that of the Syn-AC method. The results indicated that the image possessed better clarity and contrast with the blurring effect removed, and the multispectral reflectance was in agreement with the field-measured spectral reflectance. The deviations between the reflectance retrievals of Syn-AC and the field-measured values of the selected targets were within 0.0625, representing a higher precision than that of the FLAASH method (the max deviation was 0.2063). For the three sites, the mean relative error of Syn-AC was 19.3%, and the mean relative error of FLAASH was 76.6%. Atmospheric correction based on synchronous atmospheric parameters can improve the quantitative accuracy of remote sensing images, and it is meaningful for remote sensing applications.

/pdf/synchronous-atmospheric-correction-of-high-spatial-2mjefxze.pdf

Synchronous Atmospheric Correction of High Spatial Resolution Images from Gao Fen Duo Mo Satellite

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Comprehensive Study of Optical, Physical, Chemical, and Radiative Properties of Total Columnar Atmospheric Aerosols over China: An Overview of Sun-Sky Radiometer Observation Network (SONET) Measurements

Synchronous Atmospheric Correction of High Spatial Resolution Images from Gao Fen Duo Mo Satellite