Retrieval Of Optical Properties Research Articles

A new spectrally-invariant approach to the remote sensing of inhomogeneous clouds

Current operational satellite retrievals of cloud optical and microphysical properties go back to the Nakajima-King technique developed at the end of the 1980 s. This technique is based on library calculations for plane-parallel homogeneous clouds. It often works well for overcast skies but leads to substantial errors for inhomogeneous and broken cloud fields where the plane-parallel-geometry assumption is no longer valid. The basic concept of a new technique was first introduced in nuclear physics to quantify the critical conditions of reactors. Based on the eigenvalues of the radiative transfer equation, their approach provides a powerful means to parameterize the structure of 3D media. This parameterization was later successfully applied to relate surface reflectance spectra to 3D canopy structure and is known as the spectrally-invariant approximation. The proposed approach adapts this technique to the remote sensing of cloud properties such as droplet single scattering albedo and the average number of scatterings (which are the fundamental parameters in radiative transfer theory), with an emphasis on quantifying the associated errors and uncertainties. This retrieval is free from the plane-parallel homogeneous cloud assumption.

Aerosol and cloud data processing and optical property retrieval algorithms for the spaceborne ACDL/DQ-1

Abstract. The new-generation atmospheric environment monitoring satellite DQ-1, launched successfully in April 2022, carries the Aerosol and Carbon Detection Lidar (ACDL), which is capable of globally profiling aerosol and cloud optical properties with high accuracy. The ACDL/DQ-1 is a high-spectral-resolution lidar (HSRL) that separates molecular backscatter signals using an iodine filter and has 532 nm polarization detection and dual-wavelength detection at 532 and 1064 nm, which can be utilized to derive aerosol optical properties. The methods have been specifically developed for data processing and optical property retrieval according to the specific characteristics of the ACDL system and are introduced in detail in this paper. Considering the different signal characteristics and different background noise behaviors of each channel during daytime and nighttime, the procedures of data pre-processing, denoising process and quality control are applied to the original measurement signals. The aerosol and cloud optical property products of the ACDL/DQ-1, including the total depolarization ratio, backscatter coefficient, extinction coefficient, lidar ratio and color ratio, can be calculated by the retrieval algorithms presented in this paper. Two measurement cases with use of the ACDL/DQ-1 on 27 June 2022 and the global averaged aerosol optical depth (AOD) from 1 June to 4 August 2022 are provided and analyzed, demonstrating the measurement capability of the ACDL/DQ-1.

Technical note: Bimodal parameterizations of in situ ice cloud particle size distributions

Abstract. The cloud particle size distribution (PSD) is a key parameter for the retrieval of microphysical and optical properties from remote-sensing instruments, which in turn are necessary for determining the radiative effect of clouds. Current representations of PSDs for ice clouds rely on parameterizations that were largely based on aircraft in situ measurements where the distribution of small ice crystals were uncertain. This makes current parameterizations deficient to simulate remote-sensing observations sensitive to small ice, such as from lidar and thermal infrared instruments. In this study we fit the in situ PSDs of ice crystals from the JULIA (JÜLich In situ Aircraft data set) database, which consists of 11 campaigns covering the tropics, midlatitudes and the Arctic, consistently processed and considered more robust in their measurements of small ice. For the fitting, we implement an established approach to PSD parameterizations, which consists of finding an adequate set of parameters for a modified gamma function after normalization of both PSD axes. These parameters are constrained to match in situ measurements when predicting microphysical properties from the PSDs, via a cost function minimization method. We selected the ice water content and the ice crystal number concentration, which are currently key parameters for modern satellite retrievals and model microphysics schemes. We found that a bimodal parameterization yields better results than a monomodal one. The bimodal parameterization has a lower spread for almost all ice crystal sizes over the entire range of analyzed temperatures and fits better the observations, especially for particles between 20 and about 110 µm at temperatures between −60 and −20 ∘C. For this temperature range, the root mean square error for the retrieved Nice is reduced from 0.36 to 0.20. This demonstrates a clear advantage to considering the bimodality of PSDs, e.g., for satellite retrievals.

Cloud optical and physical properties retrieval from EarthCARE multi-spectral imager: the M-COP products

Abstract. The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) is the first mission that will provide measurements from active profiling, passive imaging and a broadband radiometer from a single satellite platform. The passive multi-spectral imager (MSI) features four solar and three thermal infrared channels, and has a swath of 150 km and a spatial pixel resolution of 500 m. The MSI observations will provide across-track information on clouds and aerosol to extend the active profiling information into the swath. In this paper, we present the algorithm used for retrieving the cloud optical and physical products (M-COP), specifically cloud optical thickness, effective radius and top height. The algorithm is based on the solar and terrestrial MSI channels within an optimal estimation framework. This framework enables full error propagation given by the uncertainties in measurements and a priori information. The MSI cloud algorithm has been successfully exercised on different imagers and on synthetically generated MSI observations.

Retrieval of aerosol optical properties from GOCI-II observations: Continuation of long-term geostationary aerosol monitoring over East Asia

Since the Geostationary Ocean Color Imager (GOCI) was successfully launched in 2010, the GOCI Yonsei aerosol retrieval (YAER) algorithm has been continuously updated to retrieve hourly aerosol optical properties. GOCI-II has 4 more channels including UV, finer spatial resolution (250 m), and daily full disk coverage as compared to GOCI, and was launched in February 2020, onboard the GEO-KOMPSAT-2B (GK-2B) satellite. In this study, we extended the YAER algorithm to GOCI-II data based on its improved performance in many aspects and present the first results of aerosol optical properties retrieved from GOCI-II data. Utilizing the overlapping period between the GOCI-II and GOCI in geostationary Earth orbit, we present GOCI-II aerosol retrievals for high aerosol-loading cases over East Asia and show that these have a consistent spatial distribution with those from GOCI. Furthermore, GOCI-II provides AOD at an even higher spatial resolution, revealing finer changes in aerosol concentrations. Validation results for one year data show that the GOCI-II AOD has a correlation coefficient of 0.83 and a ratio within the expected error (EE) of 59.4 % when compared with the aerosol robotic network (AERONET) data. We compared statistical metrics for the GOCI and GOCI-II AODs to assess the consistency between the two datasets. In addition, it was found that there is a strong correlation between the two datasets from the comparison of gridded GOCI and GOCI-II AOD products. It is expected that data from GOCI-II will continue long-term aerosol records with high accuracy that can be used to address air-quality issues over East Asia.

Liquid cloud optical property retrieval and associated uncertainties using multi-angular and bispectral measurements of the airborne radiometer OSIRIS

Abstract. In remote sensing applications, clouds are generally characterized by two properties: cloud optical thickness (COT) and effective radius of water–ice particles (Reff), as well as additionally by geometric properties when specific information is available. Most of the current operational passive remote sensing algorithms use a mono-angular bispectral method to retrieve COT and Reff. They are based on pre-computed lookup tables while assuming a homogeneous plane-parallel cloud layer. In this work, we use the formalism of the optimal estimation method, applied to airborne near-infrared high-resolution multi-angular measurements, to retrieve COT and Reff as well as the corresponding uncertainties related to the measurement errors, the non-retrieved parameters, and the cloud model assumptions. The measurements used were acquired by the airborne radiometer OSIRIS (Observing System Including PolaRization in the Solar Infrared Spectrum), developed by the Laboratoire d'Optique Atmosphérique. It provides multi-angular measurements at a resolution of tens of meters, which is very suitable for refining our knowledge of cloud properties and their high spatial variability. OSIRIS is based on the POLDER (POlarization and Directionality of the Earth's Reflectances) concept as a prototype of the future 3MI (Multi-viewing Multi-channel Multi-polarization Imager) planned to be launched on the EUMETSAT-ESA MetOp-SG platform in 2024. The approach used allows the exploitation of all the angular information available for each pixel to overcome the radiance angular effects. More consistent cloud properties with lower uncertainty compared to operational mono-directional retrieval methods (traditional bispectral method) are then obtained. The framework of the optimal estimation method also provides the possibility to estimate uncertainties of different sources. Three types of errors were evaluated: (1) errors related to measurement uncertainties, which reach 6 % and 12 % for COT and Reff, respectively, (2) errors related to an incorrect estimation of the ancillary data that remain below 0.5 %, and (3) errors related to the simplified cloud physical model assuming independent pixel approximation. We show that not considering the in-cloud heterogeneous vertical profiles and the 3D radiative transfer effects leads to an average uncertainty of 5 % and 4 % for COT and 13 % and 9 % for Reff.

Z-Scan theory for thin film measurements: Validation of a model beyond the standard approach using ITO and HfO[formula omitted

The Z-Scan technique is an easy and widespread approach to evaluate the nonlinear optical coefficient of materials. However, the evaluation of the same coefficients for thin films requires complex experimental setups that allow to remove the contributions of the substrate. Here, we propose a simple, yet effective, theoretical approach that allows to include the substrate contribution to the focusing effect when scanning along the propagation axis. The proposed method therefore removes the need of complex experimental setups and paves the way for a simpler retrieval of optical properties of complex nanostructures.

A Comprehensive Review on Study Methods of Aerosol Optical Properties in Different Dimensions

A comprehensive understanding of the optical properties of atmospheric aerosol is essential for a variety of applications, such as optical imaging, optical communication, and remote sensing. In recent years, many theories and numerical simulation methods have been developed to connect aerosol physical-chemical properties to their intrinsic and integral optical properties. Usually, simulations and measurements are intertwined to synergistically attain the retrieval of aerosol optical properties and mitigate or even eliminate the adverse impacts of aerosol during imaging, sensing, or communication. This review covers the fundamental theories of aerosol optical properties, the development of numerical simulations, the instrument-based sampling measurements, and the cutting-edge techniques of remote sensing. Numerical simulations have been progressing from symmetric particles to asymmetric particles over the past two decades, although any simulation method is limited by specific shape and a restricted size parameter range. Thus, this review also examines the most typical advances in aerosol instrumentation that are frequently used to measure the intrinsic optical properties of unknown aerosols. Such obtained properties validate simulations and constitute the basis of integral optical properties. In terms of practical applications, integral optical properties are the most critical knowledge about atmospheric aerosol. Remote sensing measurements, be it ground-based, airborne, or satellite-based, all retrieve integral optical properties of atmospheric aerosol from various perspectives, which are elaborated upon in this review. In conclusion, this review provides an all-encompassing comprehension of aerosol optical properties.

The retrieval of aerosol optical properties based on a random forest machine learning approach: Exploration of geostationary satellite images

Aerosol optical properties are among the most fundamental parameters in atmospheric environmental studies. Satellite aerosols retrievals that are based on deep learning or machine learning approach have been widely discussed in remote sensing studies, but the flexible random forest (RF) model has not received much attention in the retrieval of geostationary satellite, like Himawari-8. Thus, the Himawari-8 aerosol retrieval achieved by RF model requires further investigation and optimization. Based on the radiative transfer equation, this study proposed a RF model driven by a differential operator, which quantifies a simple linear relationship between aerosol optical depth (AOD) and top-of-atmosphere (TOA) reflectance enhancement. The spectral information of aerosols is achieved by independent TOA reflectance comparison between images rather than one result from multiple band synthesis. The method allows simple feature inputs and shows weak dependence on auxiliary data. It also achieves simultaneous retrievals over different surfaces and maintains mathematical correlation between spectral AODs and Angstrom Exponents (AE). The model performance was evaluated using a series of comprehensive temporal and spatial validation analyses. A sample-based tenfold cross-validation (10-CV) shows that the new method can simultaneously improve the estimation of aerosol properties, with considerably high correlation coefficients (R2) of 0.85 for AODs at the 0.50 μm wavelengths, a mean absolute error (MAE) of 0.08, a root mean square error (RMSE) of 0.13 and >70% of the samples fell within the AOD expected error (EE). The high accuracy of the spectral AOD retrievals also exhibits good performance on AE calculations, with at least 2/3 of the samples falling within the EE. The site based 10-CV also evaluates the spatial predictions on AODs at the 0.50 μm wavelength, with R2 of 0.67, MAE of 0.12 and RMSE of 0.18. It also has outperformed the Himawari operational aerosol products and appeared to be comparable to other popular machine learning models with better AE retrievals in some typical regions. Two typical regional pollution cases also highlight the advantages of the new aerosol monitoring approach. The 5 km resolution aerosol retrievals exhibit good spatial coverage and performance when describing the regional pollution levels and types. The proposed method improves the performance of RF in retrieving aerosol properties from geostationary satellites and also offers a new prospective for aerosol remote sensing using machine learning approaches.

Investigation of the effective transmittance of Mach-Zehnder interferometer in the multi-longitudinal-mode high-spectral-resolution lidar

Effective transmittance is an important parameter of Mach-Zehnder interferometer in the retrieval of aerosol optical properties using the multi-longitudinal-mode high-spectral-resolution lidar, which is a function of the complex degree of coherence. In this paper, we retrieve the parameter of the complex degree of coherence for the multi-longitudinal-mode laser from the theoretical analysis, optical simulation and experimental measurements, which are 0.726, 0.678 and 0.453 using the Mach-Zehnder interferometer with the optimal optical path difference of two times of laser optical cavity length (∼1000 mm) and under the light illumination of the high power Nd:YAG laser with the mode number of 101 and mode interval of 300 MHz under the condition of laser linewidth of 1 cm−1 (30 GHz). The preliminary measurement results of aerosol optical properties using the constructing multi-longitudinal-mode high-spectral-resolution lidar show that the difference of the complex degree of coherence retrieved from the presented methods results in the relative error of 40% in the aerosol backscattering coefficient with thin cloud layer and 90% with the presence of dense aerosol layer.

Retrieval of Aerosol Optical Properties over Land Using an Optimized Retrieval Algorithm Based on the Directional Polarimetric Camera

The Directional Polarization Camera (DPC) onboard the Chinese Gaofen-5 satellite, launched in May 2018, has similar specifications as the POLDER-3 instrument. The SRON Remote Sensing of Trace gas and Aerosol Products (RemoTAP) full retrieval algorithm is applied to DPC measurements to retrieve aerosol properties including the total Aerosol Optical Depth (AOD), the fine/coarse mode AOD and the SSA (Single Scattering Albedo). Measurements of the global ground-based AERONET network between December 2019 and April 2020 have been used for the validation of the DPC retrievals. According to the average Fine Mode Fraction (FMF) of the selected AERONET stations, the stations are divided into urban stations (FMF ≥ 0.5) and dust stations (FMF < 0.5). For the total AOD validation, DPC retrievals show better performance over urban stations than over dust stations, with average biases of 0.055 and 0.106, and RMSEs of 0.151 and 0.228, respectively. Regarding the fine mode AOD, the retrieval also performs better over urban stations. Compared with the total AOD validation, both the relatively lower bias (0.021 and 0.065) and the higher Gfrac (Fraction of Good retrieval, 63.8% and 47.3%, respectively) further indicate that DPC performs better when fine mode aerosols dominate. For the land SSA validation, most of our SSA retrievals (~71%) show differences with AERONET SSA retrievals lower than 0.05. Case studies over fire spots and dust over northern China demonstrate the encouraging application potential of DPC aerosol products. The difference between fine and coarse AOD can provide more aerosol source information compared with the total AOD alone. Since the SSA retrievals are particularly sensitive to absorbing fine particles, they can be easily used in the tracking of biomass burning aerosol.

Airborne observations during KORUS-AQ show that aerosol optical depths are more spatially self-consistent than aerosol intensive properties

Abstract. Aerosol particles can be emitted, transported, removed, or transformed, leading to aerosol variability at scales impacting the climate (days to years and over hundreds of kilometers) or the air quality (hours to days and from meters to hundreds of kilometers). We present the temporal and spatial scales of changes in AOD (aerosol optical depth) and aerosol size (using Ångström exponent – AE; fine-mode fraction – FMF) over Korea during the 2016 KORUS-AQ (KORea–US Air Quality) atmospheric experiment. We use measurements and retrievals of aerosol optical properties from airborne instruments for remote sensing (4STAR; Spectrometers for Sky-Scanning Sun-Tracking Atmospheric Research) and in situ (LARGE; NASA Langley Aerosol Research Group Experiment) on board the NASA DC-8 and geostationary satellites (GOCI; Geostationary Ocean Color Imager; Yonsei aerosol retrieval – YAER, version 2) as well as from reanalysis (MERRA-2; Modern-Era Retrospective Analysis for Research and Applications, version 2). Measurements from 4STAR when flying below 1000 m show an average AOD at 501 nm of 0.36 and an average AE of 1.11 with large standard deviation (0.12 and 0.15 for AOD and AE, respectively), likely due to mixing of different aerosol types (fine and coarse mode). The majority of AOD due to fine-mode aerosol is observed at altitudes lower than 2 km. Even though there are large variations, for 18 out of the 20 flight days, the column AOD measurements by 4STAR along the NASA DC-8 flight trajectories match the South Korean regional average derived from GOCI. GOCI-derived FMF, which was found to be slightly low compared to AErosol RObotic NETwork (AERONET) sites (Choi et al., 2018), is lower than 4STAR's observations during KORUS-AQ. Understanding the variability of aerosols helps reduce uncertainties in the aerosol direct radiative effect by quantifying the errors due to interpolating between sparse aerosol observation sites or modeled pixels, potentially reducing uncertainties in the upcoming observational capabilities. We observed that, contrary to the prevalent understanding, AE and FMF are more spatially variable than AOD during KORUS-AQ, even when accounting for potential sampling biases by using Monte Carlo resampling. Averaging between measurements and models for the entire KORUS-AQ period, the reduction in correlation by 15 % is 65.0 km for AOD and shorter at 22.7 km for AE. While there are observational and model differences, the predominant factor influencing spatial–temporal homogeneity is the meteorological period. High spatiotemporal variability occurs during the dynamic period (25–31 May), and low spatiotemporal variability occurs during the blocking pattern (1–7 June). While AOD and FMF / AE are interrelated, the spatial variability and relative variability of these parameters in this study indicate that microphysical processes vary at scales shorter than aerosol concentration processes at which microphysical processes such as aerosol particle formation, growth, and coagulation mostly impact the dominant aerosol size (characterized by, e.g., FMF / AE) and to some degree AOD. In addition to impacting aerosol size, aerosol concentration processes such as aerosol emission, transport, and removal mostly impact the AOD.

Influence of multilayer cloud characteristics on cloud retrieval and estimation of surface downward shortwave radiation

Abstract: There are significant uncertainties in the retrieval accuracy of multilayer clouds with different phase states, leading to bias in the subsequent estimation of the surface downward shortwave radiation (DSR). Single-layer clouds are generally assumed for the retrieval of cloud optical and microphysical properties from satellite measurements, although multilayer clouds often occur in reality. In this article, the impact of multilayer clouds (thin ice clouds overlying lower-level water clouds) on the retrieval of cloud microphysical properties is simulated with the radiative transfer model RSTAR. The simulated results demonstrate the impact of double-layer clouds on the accuracy of retrieval of the cloud parameters and estimation of DSR. To understand the uncertainties of the input parameters, thorough sensitivity tests are simulated by RSTAR in the Results section. As compared with the retrieval results of single-layer clouds when the ice particle model of the upper-layer cloud is assumed to be ellipsoidal, the maximum relative bias in DSR is 0.63% when the COT for the ice cloud is 1.2 and for water cloud is 32.45. When the upper-layer ice cloud is assumed to be a hexagonal column, the maximum relative bias in DSR is 55.34% when the COT for the ice cloud is 2 and for the water cloud is 58.4. In addition, relative bias in DSR tends to increase both with radiance and ice cloud COT for a given radiance. This finding can provide a basis of reference for the estimation accuracy of radiative forcing in the IPCC report and the subsequent enhancement and improvement of retrieval algorithms.

Retrieval of inherent optical properties from multiple aquatic systems using a quasi-analytical algorithm for several water types

The estimation of inherent optical properties (IOPs) from remote sensing measurements is important for the establishment of a physically meaningful relationship to retrieve water quality parameters. However, remote sensing algorithms for the retrieval of IOPs are only reliable at specific study sites. In this context, the primary goal of the present study is to evaluate the use of the proposed single algorithm formulation for the retrieval of IOPs in different inland waters. The secondary goal is to examine the performance of the proposed formulation for different optical water types. The proposed algorithm single algorithm formulation got a normalized Root Mean Square Error (nRMSE) ranging from 4% to 28% for the estimation of the absorption coefficient of colored dissolved matter (aCDM). For the estimation of the absorption coefficient of phytoplankton (aphy) the proposed algorithm got nRMSE values ranging from 10% to 42% for 487 sampling points from 7 different aquatic systems. These results indicate the possibility of retrieving the absorption coefficients of phytoplankton and colored dissolved matter from different aquatic environments. Therefore, the proposed algorithm can be the first step for a unique methodology to estimate IOPs which are important for understanding biogeochemical processes on inland waters.

Numerical Weather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products

The atmospheric molecular number density can be obtained from atmospheric temperature and pressure profiles and is a significant input parameter for the inversion of lidar measurements. When measurements of vertical profiles of temperature and pressure are not available, atmospheric models are typically considered a valid alternative option. This paper investigates the influence of different atmospheric models (forecast and reanalysis) on the retrieval of aerosol optical properties (extinction and backscatter coefficients) by applying Raman and elastic-only methods to lidar measurements, to assess their use in lidar data processing. In general, reanalyzes are more accurate than forecasts, but, typically, they are not delivered in time for allowing near-real-time lidar data analysis. However, near-real-time observation is crucial for real-time monitoring of the environment and meteorological studies. The forecast models used in the paper are provided by the Integrated Forecasting System operated by the European Centre for Medium-Range Weather Forecasts (IFS_ECMWF) and the Global Data Assimilation System (GDAS), whereas the reanalysis model is obtained from the fifth-generation European Centre for Medium-Range Weather Forecasts ReAnalysis v5 (ERA5). The lidar dataset consists of measurements collected from four European Aerosol Research Lidar Network (EARLINET) stations during two intensive measurement campaigns and includes more than 200 cases at wavelengths of 355 nm, 532 nm, and 1064 nm. We present and discuss the results and influence of the forecast and reanalysis models in terms of deviations of the derived aerosol optical properties. The results show that the mean relative deviation in molecular number density is always below ±3%, while larger deviations are shown in the derived aerosol optical properties, and the size of the deviation depends on the retrieval method together with the different wavelengths. In general, the aerosol extinction coefficient retrieval is more dependent on the model used than the aerosol backscatter retrievals are. The larger influence on the extinction retrieval is mainly related to the deviation in the gradient of the temperature profile provided by forecast and reanalysis models rather than the absolute deviation of the molecular number density. We found that deviations in extinction were within ±5%, with a probability of 83% at 355 nm and 60% at 532 nm. Moreover, for aerosol backscatter coefficient retrievals, different models can have a larger impact when the backscatter coefficient is retrieved with the elastic method than when the backscatter coefficient is calculated using the Raman method at both 355 nm and 532 nm. In addition, the atmospheric aerosol load can also influence the deviations in the aerosol extinction and backscatter coefficients, showing a larger impact under low aerosol loading scenarios.

Constrained Retrievals of Aerosol Optical Properties Using Combined Lidar and Imager Measurements During the FIREX-AQ Campaign

Smoke aerosols arise from a variety of regional sources and fuel types dependent on the properties of the fire, leading to spatial variability in smoke composition and optical properties. After emission, these aerosols age and mix in the atmosphere with other aerosol species, such as sulfates, altering the optical, and microphysical properties of the smoke aerosols over time. Thus, lidar ratio (extinction to backscatter ratio) derived from lidar sensors exhibit spatiotemporal variability for smoke. Traditional backscatter lidar processing algorithms employ a signal loss method that utilizes the reduction of signals below and above cloud layers, enabling simultaneous retrievals of both layer-averaged lidar ratio and particulate extinction, which avoids the need for assigning lidar ratios based on layer type as is typically used for backscatter lidar algorithms. In this study, the signal loss method, which is traditionally designed for cloud property retrievals, is attempted for elevated smoke plume property retrievals using NASA’s Cloud Physics Lidar (CPL) observations from the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Good agreement (linear correlation coefficient of 0.67) is found between aerosol optical depth (AOD) derived from the signal loss method and the constrained method, utilizing collocated GOES MAGARA AOD values as constraints for lidar ratio retrievals, for the Williams Flats smoke event. Differences in derived lidar ratios from the signal loss method and the constrained method (13.6 and 7.4%) are found to be smaller than the expected signal loss lidar ratio error estimate of ∼17–23%. A good agreement is also found in lidar ratios derived from this study and from using Differential Absorption Lidar-High Spectral Resolution Lidar (DIAL-HSRL) measurements for the Williams Flats Fire. The lidar ratio statistics of smoke plumes presented in this analysis (51 ± 13 sr) also compare favorably with lidar ratio values found in previous studies; however, they remain lower than the assumed smoke lidar ratio of 70 sr (at 532 nm) used by CALIPSO and CPL, and vary with plume transport distance. These findings suggest lidar ratio is likely to be regionally specific and evolve with plume transport. Thus, innovative methods for simultaneous retrieval of lidar ratio and aerosol extinction, such as the signal loss method proposed in this study, are needed for accurate aerosol retrievals from standard backscatter lidars in the future.

Effects and correctability of pile-up distortion using established figures of merit in time-domain diffuse optics at extreme photon rates

Time-domain diffuse optics (TD-DO) allows one to probe diffusive media with recognized advantages over other working domains but suffers from a poor signal-to-noise ratio (SNR) resulting from the need to build-up the histogram of single-photon arrival times with maximum count rates (CR) of few percent of the laser pulse rate to avoid the so-called “pile-up” distortion. Here we explore the feasibility of TD-DO under severe pile-up conditions with a systematic in-silico/experimental study evaluating the effects and correctability of the distortion by means of shared figures of merit. In-silico, we demonstrate that pile-up correction allows one the retrieval of homogeneous optical properties with average error < 1% up to a CR > 99%, while the optimal CR needed to detect localized perturbation was found to be 83%. Experiments reported here confirm these findings despite exhibiting higher accuracy errors in the retrieval of homogeneous optical properties and higher noise in the detection of localized absorption perturbations, but in line with the state-of-the-art systems. This work validates a new working regime for TD-DO, demonstrating an increase of the SNR at constant acquisition time, but also potentially leading in the future to previously unrealizable measurements of dynamic phenomena or in spatial scanning applications.

Laser imaging nephelometer for aircraft deployment

Abstract. Validation of remote sensing retrievals of aerosol microphysical and optical properties requires in situ measurements of the same properties. We present here an improved imaging nephelometer for measuring the directionality and polarization of light (i.e., polarimetry) scattered at two wavelengths (405 and 660 nm) with high temporal resolution. The instrument was designed for airborne deployment and is capable of ground-based measurements as well. The laser imaging nephelometer (LiNeph) uses two orthogonal detectors with wide-angle lenses and linearly polarized light sources to measure both the phase function, P11(θ), and degree of linear polarization, -P12/P11(θ). In this work, we will describe the instrument function and calibration, as well as data acquisition and reduction. The instrument was first deployed aboard the NASA DC-8 during the 2019 FIREX-AQ campaign. Here, we present field measurements of smoke plumes that show that the LiNeph has sufficient resolution for 0.24 Hz polarimetric measurements at two wavelengths, 405 and 660 nm, at integrated scattering coefficients ranging from 50–8000 Mm−1.

Performance of a 6 × 6 mm2 SiPM Module for Time-Domain Diffuse Optics

In this work we present a detector module based on large-area silicon photomultiplier, whose design has been optimized for time-domain diffuse optics measurements. In this application, to the best of our knowledge, it represents the largest area detector ever demonstrated, with expected breakthrough performance in terms of scattered light harvesting efficiency. It has been characterized using widely adopted protocols for performance assessment of instruments. We demonstrate the superior light harvesting capability and improved depth sensitivity, which can reach 4 cm. Moreover, the retrieval of optical properties has been demonstrated to be linear and with sufficient accuracy in the range corresponding to the human tissues. As a first step toward clinical application, we first performed <i>in-vivo</i> measurement at very large source-detector distances to show the suitability of the realized detector to tackle new challenges like lung or heart optical monitoring as well as imaging of deep cerebral function.

Deep learning in macroscopic diffuse optical imaging.

.SignificanceBiomedical optics system design, image formation, and image analysis have primarily been guided by classical physical modeling and signal processing methodologies. Recently, however, deep learning (DL) has become a major paradigm in computational modeling and has demonstrated utility in numerous scientific domains and various forms of data analysis.AimWe aim to comprehensively review the use of DL applied to macroscopic diffuse optical imaging (DOI).ApproachFirst, we provide a layman introduction to DL. Then, the review summarizes current DL work in some of the most active areas of this field, including optical properties retrieval, fluorescence lifetime imaging, and diffuse optical tomography.ResultsThe advantages of using DL for DOI versus conventional inverse solvers cited in the literature reviewed herein are numerous. These include, among others, a decrease in analysis time (often by many orders of magnitude), increased quantitative reconstruction quality, robustness to noise, and the unique capability to learn complex end-to-end relationships.ConclusionsThe heavily validated capability of DL’s use across a wide range of complex inverse solving methodologies has enormous potential to bring novel DOI modalities, otherwise deemed impractical for clinical translation, to the patient’s bedside.