Vector Radiative Transfer Model Research Articles

Synchronous Retrieval of Wheat Cab and LAI from UAV Remote Sensing: Application of the Optimized Estimation Inversion Framework

Chlorophyll a and b content (Cab) and leaf area index (LAI) are two key parameters of crops, and their quantitative inversions are important for growth monitoring and the field management of wheat. However, due to the close correlation between the spectral signals of these two parameters and the effects of soil and atmospheric conditions, as well as modeling errors, synchronous retrieval of LAI and Cab from remote sensing data is still a challenging task. In a previous study, we introduced the optimal estimation theory and established the inversion framework by coupling the PROSAIL (PROSPECT + SAIL) model with the unified linearized vector radiative transfer model (UNL-VRTM). The framework fully utilizes the simulated radiance spectra for synchronous retrieval of Cab and LAI at the UAV observation scale and has good convergence and self-consistency. In this study, based on this inversion framework, synchronized retrieval of Cab and LAI was carried out by real wheat UAV observation data and validated with the ground-measured data. By comparing with the empirical statistical model constructed by the PROSAIL model and coupled model, least squares support vector machine (LSSVM), and random forest (RF), the proposed method has the highest accuracy of Cab and LAI estimated from UAV multispectral data (for Cab, R2 = 0.835, RMSE = 14.357; for LAI, R2 = 0.892, RMSE = 0.564). Our proposed method enables the fast and efficient estimation of Cab and LAI in multispectral data without prior measurements and training.

Effects of the Earth curvature on Mie-scattering radiances at high solar-sensor geometries based on Monte Carlo simulations.

Given the importance of vector radiative transfer models in ocean color remote sensing and a lack of suitable models capable of analyzing the Earth curvature effects on Mie-scattering radiances, this study presents an enhanced vector radiative transfer model for a spherical shell atmosphere geometry by the Monte Carlo method (MC-SRTM), considering the effects of Earth curvature, different atmospheric conditions, flat sea surface reflectance, polarization, high solar and sensor geometries, altitudes and wavelengths. A Monte Carlo photon transport model was employed to simulate the vector radiative transfer processes and their effects on the top-of-atmosphere (TOA) radiances. The accuracy of the MC-SRTM was verified by comparing its scalar model outputs from Henyey-Greenstein (HG) phase function with the Kattawar-Adams model results, and the mean relative differences were less than 2.75% and 4.33% for asymmetry factors (g-values) of 0.5 and 0.7, respectively. The vector mode results of MC-SRTM for a spherical shell geometry with the Mie-scattering phase matrix were compared with the PCOART-SA model results (from Polarized Coupled Ocean-Atmosphere Radiative Transfer model based on the pseudo-spherical assumption), and the mean relative differences were less than 2.67% when solar zenith angles (SZAs) > 70 ∘ and sensor viewing zenith angles (VZAs) < 60 ∘ for two aerosol models (coastal and tropospheric models). Based on the MC-SRTM, the effects of Earth curvature on TOA radiances at high SZAs and VZAs were analyzed. For pure aerosol atmosphere, the effects of Earth curvature on TOA radiances reached up to 5.36% for SZAs > 70 ∘ and VZAs < 60 ∘ and reduced to less than 2.60% for SZAs < 70 ∘ and VZAs > 60 ∘. The maximum Earth curvature effect of pure aerosol atmosphere was nearly same (10.06%) as that of the ideal molecule atmosphere. The results also showed no statistically significant differences for the aerosol-molecule mixed and pure aerosol atmospheres. Our study demonstrates that there is a need to consider the Earth curvature effects in the atmospheric correction of satellite ocean color data at high solar and sensor geometries.

A neural network approach to the estimation of in-water attenuation to absorption ratios from PACE mission measurements

Introduction: In preparation for the upcoming PACE mission, we explore the feasibility of a neural network-based approach for the conversion of measurements of the degree of linear polarization at the top of the atmosphere as carried out by the HARP2 instrument into estimations of the ratio of attenuation to absorption in the surface layer of the ocean. Polarization has been shown to contain information on the in-water inherent optical properties including the total attenuation coefficient, in contrast with approaches solely based on remote sensing reflectance that are limited to the backscattered fraction of the scattering. In turn, these properties may be further combined with inversion algorithms to retrieve projected values for the optical and physical properties of marine particulates.Methodology: Using bio-optical models to produce synthetic data in quantities sufficient for network training purposes, and with associated polarization values derived from vector radiative transfer modeling, we produce a two-step algorithm that retrieves surface-level polarization first and attenuation-to-absorption ratios second, with each step handled by a separate neural network. The networks use multispectral inputs in terms of the degree of linear polarization from the polarimeter and the remote sensing reflectance from the Ocean Color Instrument that are anticipated to be fully available within the PACE data environment.Result and Discussion: Produce results that compare favorably with expected values, suggesting that a neural network-mediated conversion of remotely sensed polarization into in-water IOPs is viable. A simulation of the PACE orbit and of the HARP2 field of view further shows these results to be robust even over the limited number of data points expected to be available for any given point on Earth’s surface over a single PACE transit.

The PACE-MAPP algorithm: Simultaneous aerosol and ocean polarimeter products using coupled atmosphere-ocean vector radiative transfer

We describe the PACE-MAPP algorithm that simultaneously retrieves aerosol and ocean optical parameters using multiangle and multispectral polarimeter measurements from the SPEXone, Hyper-Angular Rainbow Polarimeter 2 (HARP2), and Ocean Color Instrument (OCI) instruments onboard the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observing system. PACE-MAPP is adapted from the Research Scanning Polarimeter (RSP) Microphysical Aerosol Properties from Polarimetry (RSP-MAPP) algorithm. The PACE-MAPP algorithm uses a coupled vector radiative transfer model such that the atmosphere and ocean are always considered together as one system. Consequently, this physically consistent treatment of the system across the ultraviolet, (UV: 300–400 nm), visible (VIS: 400–700 nm), near-infrared (NIR: 700–1100 nm), and shortwave infrared (SWIR: 1100–2400 nm) spectral bands ensures that negative water-leaving radiances do not occur. PACE-MAPP uses optimal estimation to simultaneously characterize the optical and microphysical properties of the atmosphere’s aerosol and ocean constituents, find the optimal solution, and evaluate the uncertainties of each parameter. This coupled approach, together with multiangle, multispectral polarimeter measurements, enables retrievals of aerosol and water properties across the Earth’s oceans. The PACE-MAPP algorithm provides aerosol and ocean products for both the open ocean and coastal areas and is designed to be accurate, modular, and efficient by using fast neural networks that replace the time-consuming vector radiative transfer calculations in the forward model. We provide an overview of the PACE-MAPP framework and quantify its expected retrieval performance on simulated PACE-like data using a bimodal aerosol model for observations of fine-mode absorbing aerosols and coarse-mode sea salt particles. We also quantify its performance for observations over the ocean of dust-laden scenes using a trimodal aerosol model that incorporates non-spherical coarse-mode dust particles. Lastly, PACE-MAPP’s modular capabilities are described, and we discuss plans to implement a new ocean bio-optical model that uses a mixture of coated and uncoated particles, as well as a thin cirrus model for detecting and correcting for sub-visual ice clouds.

A vector radiative transfer model for simulating the microwave emissivity of sea foam based on matrix operator method

Modeling sea foam emissivity is important for microwave remote sensing of the ocean. Based on the matrix-operator method, we propose a vector radiative transfer model (RTM) for the simulation of wave propagation in the foam layer and the sea foam emissivity is then calculated. The RTM simulates the emissivity of a vertically structured foam layer with various water properties. We assess the model simulations under different conditions. It is found that the foam thickness and the void fraction profile could be critical parameters in modeling foam emissivity. The sea foam emissivity could range from 0.2 to 0.3 in our simulations with varying foam thicknesses and void fraction profiles. Then, comparisons were made to experimental data for validation, and our model achieved a good consistency with the different experimental measurements of three previous studies. And it was also found that the modeled emissivity fits the experimental data well using an exponential profile with a small profile shape-control parameter.

Satellite retrieval of the linear polarization components of the water-leaving radiance in open oceans.

Atmospheric correction (AC) of polarized radiances acquired by polarization satellite sensors, remains a challenge due to the complex radiative transfer processes of the coupled ocean-atmosphere system. In this study, we proposed an innovative polarized AC algorithm built on the near-infrared band (PACNIR) with an emphasis on the retrieval of the linear polarization components of the water-leaving radiance in clear open oceans. This algorithm was based on the black ocean assumption in the near-infrared band and fitted polarized radiance measurements along multiple observation directions with nonlinear optimized processing. Our retrieval algorithm notably inverted the linearly polarized components of the water-leaving radiance and aerosol parameters. Compared with that of the simulated linear polarization components of the water-leaving radiance via the vector radiative transfer model for the studied sea regions, the mean absolute error of the PACNIR-retrieved linearly polarized components (nQw and nUw) exhibited a magnitude of 10-4, while the magnitude of that of the simulated nQw and nUw data was 10-3. Moreover, the PACNIR-retrieved aerosol optical thicknesses at 865 nm exhibited a mean absolute percentage error of approximately 30% relative to in situ values obtained from Aerosol Robotic Network-Ocean Color (AERONET-OC) sites. The PACNIR algorithm could facilitate AC of the polarized data provided by the next generation of multiangle polarization satellite ocean color sensors.

Synchronous Retrieval of LAI and Cab from UAV Remote Sensing: Development of Optimal Estimation Inversion Framework

UAV (unmanned aerial vehicle) remote sensing provides the feasibility of high-throughput phenotype nondestructive acquisition at the field scale. However, accurate remote sensing of crop physicochemical parameters from UAV optical measurements still needs to be further studied. For this purpose, we put forward a crop phenotype inversion framework based on the optimal estimation (OE) theory in this paper, originating from UAV low-altitude hyperspectral/multispectral data. The newly developed unified linearized vector radiative transfer model (UNL-VRTM), combined with the classical PROSAIL model, is used as the forward model, and the forward model was verified by the wheat canopy reflectance data, collected using the FieldSpec Handheld in Qi County, Henan Province. To test the self-consistency of the OE-based framework, we conducted forward simulations for the UAV multispectral sensors (DJI P4 Multispectral) with different observation geometries and aerosol loadings, and a total of 801 sets of validation data were obtained. In addition, parameter sensitivity analysis and information content analysis were performed to determine the contribution of crop parameters to the UAV measurements. Results showed that: (1) the forward model has a strong coupling between vegetation canopy and atmosphere environment, and the modeling process is reasonable. (2) The OE-based inversion framework can make full use of the available radiometric spectral information and had good convergence and self-consistency. (3) The UAV multispectral observations can support the synchronous retrieval of LAI (leaf area index) and Cab (chlorophyll a and b content) based on the proposed algorithm. The proposed inversion framework is expected to be a new way for phenotypic parameter extraction of crops in field environments and had some potential and feasibility for UAV remote sensing.

Estimation of rain parameters for microwave backscattering model using PSO

The intention of the geophysical modelling of rain is to provide a better explanation for the effect of rainfall to the microwave backscattering and thus to interpret radar measurements. However, in the model, physical characteristic of raindrops should be estimated primarily and accurately by considering observation system, measurements and suitable rain rate retrieval algorithms to calculate backscattering coefficients from rainfall. In this study, a geophysical microwave backscattering model of rain type precipitation over sea surface is constructed by using Particle Swarm Optimization (PSO) algorithm in the multilayered Vector Radiative Transfer (VRT) model to estimate vertical profile of rain by using GPM DPR data. Rain column is partition into sublayers and for each sublayer, physical properties of raindrops such as drop radius, water volume fraction or layer thickness are estimated by using PSO to provide the best fit with measurements by searching within certain limits defined by rain rate. Backscattering coefficients from entire rain is provided by the solution of VRT equations via Matrix Doubling Method to consider multilayer effect. Results show that, vertical profile of rain parameters can be estimated accurately for moderate /high rain rates (up to 11–12 mm/h) by using presented model.

Spherical vector radiative transfer model for satellite ocean color remote sensing.

Atmospheric correction is the key step for satellite ocean color remote sensing. However, most of the existing atmospheric correction algorithms do not consider the effects of Earth curvature. In fact, Earth curvature has a significant impact on satellite observation signals under large solar zenith angles or large viewing zenith angles. In this study, based on the Monte Carlo method, a vector radiative transfer model with spherical shell atmosphere geometry (hereafter our SSA-MC model) considering the influence of Earth curvature was established, which can be applied to conditions with high solar zenith angles or high viewing zenith angles. Our SSA-MC model was first compared with the Adams&Kattawar model, and the results show that the mean relative differences are 1.72%, 1.36% and 1.28% for solar zenith angles of 0 ∘, 70.47 ∘ and 84.26 ∘, respectively. Moreover, our SSA-MC model was further validated by more recently benchmarks from Korkin's scalar and vector models, and the results show that the relative differences are mostly less than 0.5% even at extremely high solar zenith angles (84.26 ∘). Then, our SSA-MC model was verified with the Rayleigh scattering radiance calculated by the look-up tables (LUTs) in SeaDAS under low-to-moderate solar or viewing zenith angles, and the results show that the relative differences are less than 1.42% when solar zenith angles are less than 70 ∘ and viewing zenith angles are less than 60 ∘. Our SSA-MC model was also compared with the Polarized Coupled Ocean-Atmosphere Radiative Transfer model based on the pseudo-spherical assumption (PCOART-SA), and the results show that the relative differences are mostly less than 2%. At last, based on our SSA-MC model, the effects of Earth curvature on Rayleigh scattering radiance were analyzed for both high solar zenith angles and high viewing zenith angles. The result shows that the mean relative error between the plane-parallel (PP) geometry and spherical shell atmosphere (SSA) geometry is 0.90% when the solar zenith angle is 60 ∘ and the viewing zenith angle is 60.15 ∘. However, the mean relative error increases with increasing solar zenith angle or viewing zenith angle. When the solar zenith angle is 84 ∘ and the viewing zenith angle is 84.02 ∘, the mean relative error is 4.63%. Thus, Earth curvature should be considered in atmospheric corrections at large solar or viewing zenith angles.

On-Orbit Absolute Radiometric Calibration and Uncertainty Analysis of OCM2 Sensor onboard Oceansat-2 Satellite

ABSTRACT This study describes On-Orbit absolute radiometric calibration for the Ocean Colour Monitor2 (OCM2) onboard Oceansat-2 satellite through a vicarious calibration experiment performed at the Great Rann of Kutch calibration site in Gujarat, India, in February 2022. To achieve accurate and consistent calibration for the OCM2 channels, a reflectance-based calibration method was used which relies on synchronous in-situ measurements of surface reflectance and atmospheric parameters at the time of satellite overpass. In this exercise, the 6 SV (Second Simulation of a Satellite Signal in the Solar Spectrum Vector) radiative transfer (RT) model was used to simulate the Top Of Atmosphere (TOA) spectral radiance for the OCM2 channels. The on-orbit radiometric performance/changes were derived by comparing the 6 SV simulated TOA radiance with those of the OCM2 Level 1B (L1B) data product. The results indicate that the average gain for band 1 to band 8 was found in the range from 0.88 to 1.23 and the relative error from 1.58% to 18.79% for the OCM2 sensor. However, the Root-Mean-Square-Error (RMSE) between the OCM2 measured and the 6 SV simulated TOA radiance data range from 0.17 (µW cm−2 sr−1 nm−1) at 443 nm to 2.04 (µW cm−2 sr−1 nm−1) at 620 nm. Furthermore, we analysed in detail the various uncertainties in this approach emanating from surface reflectance, atmospheric conditions (ozone, water vapour and aerosol optical depth), aerosol-type assumption in the RT model, BRDF, and inherent accuracy of the 6 SV RT model. The overall uncertainty was within 6% estimated using the reflectance-based calibration method.

HY-1C/D CZI Image Atmospheric Correction and Quantifying Suspended Particulate Matter

HY-1C/D both carry a coastal zone imager (CZI) with a spatial resolution of 50 m and a swath width of 950 km, two observations can be achieved in three days when two satellites operating in a network. Accurate atmospheric correction is the basis for quantitative inversion of ocean color parameters using CZI However, atmospheric correction in estuarine and coastal waters with complex optical properties is a challenge due to the band setting of CZI. This paper proposed a novel atmospheric correction algorithm for CZI images applicable to turbid waters in estuarine and coastal zone. The Rayleigh scattering reflectance of CZI was calculated based on a vector radiative transfer model. Next, a semi-empirical radiative transfer model with suspended particle concentration as the parameter is used to model the water-atmosphere coupling. Finally, the parameters of the coupling model are solved by combining a global optimization method based on a genetic algorithm. The results indicate that the CZI-derived remote-sensing reflectance (Rrs) are in good agreement with the quasi-synchronous Landsat-8/9 operational land imager (OLI) derived Rrs in the green and red bands (R2 &gt; 0.96). Validation using in situ data revealed that the RMSE of the CZI-derived Rrs in the green and red bands was 0.0036 sr−1 and 0.0035 sr−1. More importantly, the values and spatial distributions of suspended particulate matter (SPM) estimated by CZI and those estimated by OLI in the Subei Shoal and the Yangtze River Estuary are basically consistent, and the validation using in situ data revealed that the inversion of SPM concentration by CZI was effective (R2 = 0.86, RMSE = 0.0362 g/L), indicating that CZI has great potential and broad application prospects for monitoring the spatial and temporal dynamics of SPM in estuarine and coastal waters. The study results will lay the foundation for further estimating suspended sediment fluxes and carbon fluxes, thus providing data support and scientific basis for promoting resource development, utilization and conservation strategies in estuarine and coastal areas.

Tangent-Linear and Adjoint Models for the Transfer of Polarized Radiation

Abstract This study reports the development of tangent-linear and adjoint models for a vector radiative transfer model called TAMU-VRTM. This vector radiative transfer model is further validated in the case of the atmosphere–ocean coupled system, although previous validation was conducted for single and multiple layers. The TAMU-VRTM and tangent-linear and adjoint models can be applied to remote sensing and data assimilation based on spaceborne and airborne polarimetric observations. The tangent-linear and adjoint models accurately and efficiently compute the derivatives of output Stokes parameters with respect to input variables of the TAMU-VRTM. An inversion algorithm can straightforwardly compute the Jacobian matrix from the derivatives of Stokes parameters using the chain rule. We validate the tangent-linear and adjoint models by comparing them with the finite-difference method, and show that the finite-difference results converge to the tangent-linear and adjoint results. Furthermore, the adjoint model can efficiently compute the derivatives of observables with respect to the scattering phase matrix elements. This capability can be used to evaluate the scattering phase matrix assumed in an inversion algorithm and has potential for applications to inferring scattering phase matrix elements of cloud, aerosol, and hydrosol particles.

Error analysis of multi-scale aerosol optical properties in shortwave radiative transfer

Atmospheric aerosols can significantly affect atmospheric radiative transfer, especially for shortwave radiation. In this paper, the differences between the bin method and the parameterization method for determining the aerosol optical properties using particle size distribution on the effect of solar shortwave radiation have been discussed according to the developed vector radiative transfer model. The aerosol particle size is divided into multiple regions in the bin method and each region has separate optical properties, while the optical properties of the parameterization method are obtained by integrating the entire size distribution and remain constant. Firstly, the accuracy of the model has been verified by Rayleigh scattering and field distribution simulations, and the validation results show that the average errors of normalized radiation intensity, Stokes components Q and U are 1.36%, 0.79%, and 5.82%, respectively. Subsequently, the angular distribution of radiation, the radiation characteristics of different model parameters, and the field distribution of polarization characteristics under the two methods have been calculated, and the relative errors between the methods have been discussed. The results show that the differences between the two methods are significant, and the angular radiation intensity of the bin method is generally larger than that of the parameterization method, with an average error of about 5%. The transmissivity decreases, and reflectivity increases with the increasing SZA (AOD), respectively, but both decrease with the increasing wavelength. For all model parameters, the transmissivity calculated by the bin method is generally smaller than that of the parameterization method, while the reflectivity is larger, indicating that the forward scattering determined by the bin method is weaker and the lateral (backward) scattering is stronger. Besides, the field distribution results show differences in the scattering phase matrix determined by the two methods. This research contributes to the knowledge of climate assessment and target detection.

Optimization Model of Signal-to-Noise Ratio for a Typical Polarization Multispectral Imaging Remote Sensor.

The signal-to-noise ratio (SNR) is an important performance evaluation index of polarization spectral imaging remote sensors. The SNR-estimation method based on the existing remote sensor is not perfect. To improve the SNR of this model, a partial detector check slant direction is presented in this study, and a polarization extinction ratio related to the internal SNR model of a typical multispectral imaging remote sensor is combined with the vector radiative transfer model to construct the atmosphere 6SV–SNR coupling model. The new result is that the central wavelength of the detection spectrum, the observation zenith angle, and the extinction ratio all affect the SNR of the remote sensor, and the SNR increases with the increase in the central wavelength of the detection spectrum. It is proved that the model can comprehensively estimate the SNR of a typical polarization multispectral imaging remote sensor under different detection conditions, and it provides an important basis for the application evaluation of such remote sensors.

VLIDORT-QS: A quasi-spherical vector radiative transfer model

We present a new VLIDORT-based quasi-spherical vector radiative transfer (RT) model (VLIDORT-QS) that was developed for forward-model simulations of long-path measurements in limb- or nadir-viewing spherical-atmosphere configurations. This paper provides a theoretical description of the RT model, in which the single-scatter radiation field is treated accurately in spherical geometry, while the diffuse-scatter source terms at points along the line-of-sight are approximated locally through pseudo-spherical simulations based on the VLIDORT RT code; these multiple scatter source terms are then folded into the source function integration to yield the radiation field at any line-of-sight observation point in the atmosphere. In addition, the VLIDORT-QS model has a complete linearization facility (ability to generate analytic Jacobians of the radiation field with respect to any atmospheric or surface property). Validation of the model has been carried out against a fully-spherical Monte Carlo code, and also against the standard VLIDORT model for nadir-view scenarios with relatively low sphericity effects. A sample application of VLIDORT-QS for a biomass-burning plume scenario is demonstrated. A second paper will present a detailed analysis of the retrieval of ice-cloud properties from high-altitude aircraft measurements taken as part of the NASA Airborne Tropical Tropopause Experiment (ATTREX) campaign, with VLIDORT-QS providing forward-model simulations of the radiation fields and associated Jacobian functions.

The polarimetric characteristics of dust with irregular shapes: evaluation of the spheroid model for single particles

Abstract. In the atmosphere, the dust shapes are various, and a single model is difficult to represent the complex shapes of dust. We proposed a tunable model to represent dust with various shapes. Two tunable parameters were used to represent the effects of the erosion degree and binding forces from the mass center, respectively. Thus, the model can represent various dust shapes by adjusting the tunable parameters. To evaluate the applicability of the single spheroid model in calculating the optical properties of single dust with irregular shapes, the aspect ratios of spheroids were retrieved by best fitting the phase function of dust with irregular shapes. In this work, the optical properties and polarimetric characteristics of irregular dust with a diameter range of 0.2–2.0 µm were investigated. Our findings show that the dust shapes have a substantial impact on the scattering matrix, and sometimes the sign of elements of the scattering matrix could be modified by changing the tunable parameters. The applicability of the spheroid model is significantly affected by the erosion degree and binding forces, and substantial deviations could be observed when the dust diameter is in the range of 0.8–2.0 µm. The F11 relative differences of approximately 100 % between dust with irregular shapes and best-fitted spheroids could be observed in certain scattering angles. The maximum differences in other elements between irregular dust particles and best-fitted spheroids can reach approximately 0.3–0.8. Besides, the signs of F12/F11, F33/F11, F34/F11 and F44/F11 can be modified from negative to positive at some scattering angles if substituting the irregular dust with best-fitted spheroids. As the binding force is small, the deviation of extinction or scattering cross-section generally increases with the erosion degree, and the relative differences between dust with irregular shapes and spheroids can reach approximately 30 % when the erosion degree is large, while the differences are mitigated with the binding force increasing. Besides, with the binding force increasing, the aspect ratio is closer to 1:1. The deviations of the spheroid model in estimating the polarized light were also investigated using the successive-order-of-scattering (SOS) vector radiative transfer (VRT) model. With a diameter (dp) of 0.2 µm, the relative difference of normalized radiance does not exceed 3 %, and the absolute values of the deviation of the polarized bidirectional reflectance factor (PBRF) and the ratio of radiance to polarized intensity (DoLP) are below 0.005 and 0.02, respectively. However, with the particle size increasing, the difference becomes much more substantial. The relative difference of the normalized radiance can exceed 10 %, and the deviations of the PBRF and DoLP can vary in the ranges of −0.015 to 0.025 and −0.05 to 0.15, respectively. Thus, the single spheroid model may lead to non-negligible deviations for estimating the polarimetric characteristics of single dust particles with more complex shapes. In this work, only the optical properties of single particles were considered. In the future, the applicability of an ensemble of spheroidal particles for reproducing the scattering properties and polarimetric characteristics of an ensemble of irregularly shaped dust particles should be further investigated.

Enhancing MAX-DOAS atmospheric state retrievals by multispectral polarimetry – studies using synthetic data

Abstract. Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) is a widely used measurement technique for the remote detection of atmospheric aerosol and trace gases. The technique relies on the analysis of ultra-violet and visible radiation spectra of scattered sunlight (skylight) to obtain information on different atmospheric parameters. From an appropriate set of spectra recorded under different viewing directions (typically a group of observations at different elevation angles) the retrieval of aerosol and trace gas vertical distributions is achieved through numerical inversion methods. It is well known that the polarisation state of skylight is particularly sensitive to atmospheric aerosol content as well as aerosol properties and that polarimetric measurements could therefore provide additional information for MAX-DOAS profile retrievals; however, such measurements have not yet been used for this purpose. To address this issue, we have developed the RAPSODI (Retrieval of Atmospheric Parameters from Spectroscopic Observations using DOAS Instruments) algorithm. In contrast to existing MAX-DOAS algorithms, it can process polarimetric information, and it can retrieve simultaneously profiles of aerosols and various trace gases at multiple wavelengths in a single retrieval step; a further advantage is that it contains a Mie scattering model, allowing for the retrieval of aerosol microphysical properties. The forward-model component in RAPSODI is based on a linearised vector radiative transfer model with Jacobian facilities, and we have used this model to create a database of synthetic measurements in order to carry out sensitivity analyses aimed at assessing the potential of polarimetric MAX-DOAS observations. We find that multispectral polarimetry significantly enhances the sensitivity, particularly to aerosol-related quantities. Assuming typical viewing geometries, the degrees of freedom for signal (DOFS) increase by about 50 % and 70 % for aerosol vertical distributions and aerosol properties, respectively, and by approximately 10 % for trace gas vertical profiles. For an idealised scenario with a horizontally homogeneous atmosphere, our findings predict an improvement in the inversion results' accuracy (root-mean-square deviations to the true values) of about 60 % for aerosol vertical column densities (VCDs) as well as for aerosol surface concentrations and by 40 % for aerosol properties. For trace gas VCDs, very little improvement is apparent, although the accuracy of trace gas surface concentrations improves by about 50 %.

Polarization performance of a polydisperse aerosol atmosphere based on vector radiative transfer model

The effect of aerosol particles in the atmospheric radiative transfer has great uncertainty. A computer code for accurate calculation of the optical properties and vector radiative transfer of polydisperse aerosol system has been written in C++ language. In this paper, the polydisperse aerosol system has been divided into multiple size regions according to the particle size, and the optical properties of each region have been calculated separately and applied in the radiative transfer calculation. The results show that this method is significantly different from the method that is not divided into regions, with an average error of about 5.77%. The radiance mode and polarization mode in different solar zenith angle (SZA, from 30° to 70°), aerosol optical depth (AOD, from 0.2 to 1.0), and three aerosol types have been also calculated. The results also show that the radiative transfer is highly sensitive to all parameters. The polarization distribution but not the degree of polarization (DOP) is affected by SZA. The depolarization effects of aerosol are affected by AOD and particle size distribution. The maximum DOP decreased by 0.2 and 0.3 with the increasing AOD and average particle size, respectively. The distribution of Stokes parameters and atmospheric absorption is mainly affected by the complex refractive index. The Stokes component distribution of water-soluble aerosol is different from dust and soot, and the atmospheric transmissivity from dust to soot decreases about 26%. In addition, the scattering angles of the maximum DOP of the three aerosol types are all greater than 90°, which reflects the important effect of the element P12 on the polarization mode. This research has guiding significance for many fields such as energy utilization, climate prediction, atmospheric remote sensing, and so on.

Nighttime smoke aerosol optical depth over U.S. rural areas: First retrieval from VIIRS moonlight observations

An algorithm for retrieving nighttime aerosol optical depth (AOD) from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) observations of reflected moonlight is presented for rural areas during the western U.S. fire seasons. The algorithm uses the UNified and Linearized Vector Radiative Transfer Model (UNL-VRTM) with newly developed capabilities for considering lunar illuminations. Cloud and fire pixels are screened out by utilizing the radiance from the VIIRS Moderate-resolution Bands (M-Band) and the DNB. Rural and city pixels are classified based on a pre-calculated city light database. The surface spectral reflectance for DNB ranging from 342 to 1107 nm is estimated by a random forest approach, which is trained using the surface spectral reflectance from the existing spectral libraries. For the fire seasons of 2017 and 2020, the nighttime AOD retrieval is shown to play an indispensable role in describing the nonlinear diurnal movement of smoke transport and discerning the source of smoke plumes heretofore observable only in the daytime. The retrieved AOD values show good agreement with spatiotemporally collocated Aerosol Robotic NETwork (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD values, with linear correlation coefficient values of ~0.96/0.95 and ~86%/69% of the AOD pairs falling in an uncertainty envelope of ±(0.085 + 0.10AOD), which is superior to AOD reanalysis from Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). These results affirm the significant potential of nighttime AOD to improve the analysis and forecast of regional to global biomass-burning aerosol distributions, filling a critical gap in the diurnal description of a key element of Earth's climate system.

Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model

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.