Radiative transfer modeling through terrestrial atmosphere and ocean accounting for inelastic processes: Software package SCIATRAN

Since the initiation of the development at the Institute of Environmental Physics (IUP), University of Bremen in 1994, the radiative transfer model SCIATRAN (formerly GOMETRAN) has been continuously improved and new versions have been released. In the course of development, the SCIATRAN software package became capable to simulate radiative transfer processes through an atmosphere-ocean system with a variety of approaches to treat the sphericity of the atmosphere (plane-parallel, pseudo-spherical, approximate spherical and full spherical solutions) in both scalar and vector modes. Supported by a variety of build-in databases and parameterizations, these capabilities made SCIATRAN widely used for various remote sensing applications related to the retrieval of atmospheric trace gases and characteristics of aerosols, clouds and surface. This paper presents an overview of the clouds, aerosol and surface (CAS) databases and models implemented in the SCIATRAN software package (V4.5) and provides some recommendations on their usage. The new implementations offer potential users flexible interface to perform radiative transfer simulations: 1) in the case of multi-layer liquid water, ice and mix-phase clouds; 2) employing typical aerosol type parameterizations (including vertical variability) used in satellite and model communities as well as updated databases; 3) including various surface BRDF and albedo models for land, vegetation, ocean, snow and melt ponds on sea ice. The most recent SCIATRAN version is freely available for students and scientists affiliated at academic facilities after a registration at the website of IUP, University of Bremen: http://www.iup.physik.uni-bremen.de/sciatran.

Investigation of radiative heat transfer in fixed bed biomass furnaces

Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval. If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model’s potential to retrieve LAI is limited by the availability of a priori information on RT model parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types.

In this thesis the work of the author on the modelling of radiative transfer in vegetation canopies and the terrestrial atmosphere is summarized. The activities span a period of more than fifteen years of research in this field carried out at the National Aerospace Laboratory NLR.For the interpretation of optical remote sensing observations of vegetation canopies from satellites or aircraft the use of simulation models can be an important tool, as these models give insight in the relations between vegetation properties and observed remote sensing data.The models discussed here are first presented from a theoretical point of view. An attempt has been made to construct a framework in which all the discussed models, for vegetation as well as for the atmosphere, can be represented. After an introduction on basic radiometric quantities and relations (chapter 2), and a classification of radiative transfer models and solution methods (chapter 3), examples of existing models are discussed in chapter 4.In chapter 5 a new generalized theory of radiative transfer models for azimuthally isotropic media is presented, namely the (N+2)-stream theory. This theory describes radiative transfer in vegetation canopies or atmospheres to any desired numerical accuracy. In the formulation of the analytical solution of this model, which is based on eigenvector decomposition, much attention has been paid to possible numerical problems. Symmetry relations are exploited in order to reduce memory requirements and computation time, and expressions have been found for which the so-called reciprocity relations are automatically fulfilled. The numerical capabilities of this model for simulation of atmospheric radiative transfer are demonstrated in chapter 6.The theory of the vegetation canopy bidirectional reflectance models SAIL and SAILH are discussed in chapters 7 and 8. Both are four-stream models. In SAILH the so-called hot spot effect, which is related with the finite leaf size, is incorporated. After these more theoretic chapters, practical aspects of the radiative transfer models are discussed in the following chapters.In chapter 9 the application of a four-stream atmosphere model to the correction and calibration of Landsat Thematic Mapper images is demonstrated. This atmosphere model has been coupled with the SAILH model into an overall four-stream model of optical remote sensing observations from any altitude. This model is called OSCAR (optical soil-canopyatmosphere radiance) and is presented in chapter 10.Applications of radiative transfer modelling to optical remote sensing problems are illustrated in chapter 11, and chapter 12, summarizes the conclusions of this thesis.

Radiative transfer models (RTMs) are often used to retrieve biophysical parameters from earth observation data. RTMs with multi-temporal and realistic forest representations enable radiative transfer (RT) modeling for real-world dynamic processes. To achieve more realistic RT modeling for dynamic forest processes, this study presents the 3D-explicit reconstruction of a typical temperate deciduous forest in 2015 and 2022. We demonstrate for the first time the potential use of bitemporal 3D-explicit RT modeling from terrestrial laser scanning on the forward modeling and quantitative interpretation of: (1) remote sensing (RS) observations of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and canopy light extinction, and (2) the impact of canopy gap dynamics on light availability of explicit locations. Results showed that, compared to the 2015 scene, the hemispherical-directional reflectance factor (HDRF) of the 2022 forest scene relatively decreased by 3.8% and the leaf FAPAR relatively increased by 5.4%. At explicit locations where canopy gaps significantly changed between the 2015 scene and the 2022 scene, only under diffuse light did the branch damage and closing gap significantly impact ground light availability. This study provides the first bitemporal RT comparison based on the 3D RT modeling, which uses one of the most realistic bitemporal forest scenes as the structural input. This bitemporal 3D-explicit forest RT modeling allows spatially explicit modeling over time under fully controlled experimental conditions in one of the most realistic virtual environments, thus delivering a powerful tool for studying canopy light regimes as impacted by dynamics in forest structure and developing RS inversion schemes on forest structural changes.

Characterizing the thermal infrared spectral effects of optically thin surface dust: Implications for remote-sensing and in situ measurements of the martian surface

The aim is to determine the role and the influence of assumptions concerning both dynamics and radiative transfer in models of winds and mass loss of evolved stars, when the radiative force on dust grains plays a major role in the structuration of the circumstellar envelope of the star. The flow is described successively using three models coupling the grain and the gas dynamics in a self-consistent way with radiative transfer for three different approaches of the dynamics: the Position Coupling, the Momentum Coupling and the Full Problem. A complete radiative transfer including multiple scattering, absorption and thermal emission is taken into account to determine the temperature of dust grains which in turn governs their thermal emission. The medium is not necessarily optically thin. In all cases, numerical iterations couple dynamics with transfer. Thus two codes are used alternately, starting with an initial profile of radiation pressure, until convergence to a self-consistent solution. This emphasizes the importance of the drift velocity between the grains and the gas, and the inertia of dust together with hydrodynamics/transfer coupling. When the medium is optically thick, an opaque zone is located at the base of the wind. This zone governs the whole envelope structure. Finally, the exact number of solutions was determined for the one-fluid model.

A passive and active microwave-vector radiative transfer (PAM-VRT) model

A Fast All-sky Radiation Model for Solar applications (FARMS): Algorithm and performance evaluation

Context. Radiative transfer modelling of expanding stellar envelopes is an important task in their analysis. To account for inhomogeneities and deviations from spherical symmetry, it is necessary to develop a 3 D approach to radiative transfer modelling. Aims. We present a 3 D Monte Carlo code for radiative transfer modelling, which is aimed to calculate the plasma ionisation and excitation state with the statistical equilibrium equations, moreover, to implement photon-matter coupling. As a first step, we present our Monte Carlo radiation transfer routines developed and tested from scratch. Methods. The background model atmosphere (the temperature, density, and velocity structure) can use an arbitrary grid referred to as the modGrid. The radiative transfer was solved using the Monte Carlo method in a Cartesian grid, referred to as the propGrid. This Cartesian grid was created based on the structure of the modGrid; correspondence between these two grids was set at the beginning of the calculations and then kept fixed. The propGrid can be either regular or adaptive; two modes of adaptive grids were tested. The accuracy and calculation speed for different propGrids was analysed. Photon interaction with matter was handled using the Lucy’s macroatom approach. Test calculations using our code were compared with the results obtained by a different Monte Carlo radiative transfer code. Results. Our method and the related code for the 3 D radiative transfer using the Monte Carlo and macroatom methods offer an accurate and reliable solution for the radiative transfer problem, and are especially promising for the inclusion and treatment of 3 D inhomogeneities.

Electromagnetic radiation plays a crucial role in various physical and chemical processes. Hence, almost all astrophysical simulations require some form of radiative transfer model. Despite many innovations in radiative transfer algorithms and their implementation, realistic radiative transfer models remain very computationally expensive, such that one often has to resort to approximate descriptions. The complexity of these models makes it difficult to assess the validity of any approximation and to quantify uncertainties on the model results. This impedes scientific rigour, in particular, when comparing models to observations, or when using their results as input for other models. We present a probabilistic numerical approach to address these issues by treating radiative transfer as a Bayesian linear regression problem. This allows us to model uncertainties on the input and output of the model with the variances of the associated probability distributions. Furthermore, this approach naturally allows us to create reduced-order radiative transfer models with a quantifiable accuracy. These are approximate solutions to exact radiative transfer models, in contrast to the exact solutions to approximate models that are often used. As a first demonstration, we derive a probabilistic version of the method of characteristics, a commonly-used technique to solve radiative transfer problems.

Snow-depth retrieval from passive microwave observations without a priori information is a highly undetermined problem. Achieving accurate snow-depth retrievals requires a priori information on the snowpack properties, such as grain size, density, physical temperature, and stratigraphy. On a practical level, however, retrieval algorithms must consider prior information, while minimizing the dependence on it, as accurate ancillary data are not globally available. In this study, we build on the previously published Bayesian Algorithm for Snow Water Equivalent Estimation (BASE) to retrieve snow depth using an airborne passive microwave data set over the tundra snow in the Eureka region. The method computes the optimal estimates of snow depth, density, grain size, and other variables, given the brightness temperature observations and prior information, using Markov chain Monte Carlo (MCMC). The airborne data set includes passive microwave brightness temperature (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> ) at 18.7 and 36.5 GHz. The in situ measurements of the snow depth provide validation data for 464 sensor footprints. The microwave radiative transfer (RT) model used is the Dense Media RT-Multilayered (DMRT-ML) model. We use a two-layer wind slab and depth hoar assumption based on the local snow cover knowledge from the previous research on the study area. To improve our understanding of the results using the airborne T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> s, the inversion was also applied using the synthetic observations, where T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> s were generated from the RT model. For the case with synthetic observations, the snow-depth RMSE was 0.07 cm. When the airborne T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> s are used, the snow-depth RMSE was 21.8 cm. This discrepancy is due to the large spatial variability in the MagnaProbe snow-depth measurements and the fact that not all physical processes affecting the airborne T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> s are represented in the RT model. Our work verifies the feasibility and applicability of the proposed methodology regionally for the airborne retrievals and reinforces the tractable applicability of a physics-based RT model in the SWE retrievals.

<strong class="journal-contentHeaderColor">Abstract.</strong> Radiative transfer (RT) simulations have long been used to study relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean from which the ocean apparent optical properties (AOPs) can be calculated. For example, inverse models to estimate IOPs from ocean color radiometric measurements have been developed or validated using results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range from the near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development was the generation of the synthetic dataset of seawater IOPs which served as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by probability distributions of IOPs that are consistent with global distributions representative of vast areas of open ocean pelagic environments and coastal regions covering a broad range of optical water types. The generation of the synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column and three simulation scenarios associated with sun zenith angle. Specifically, the simulations were made assuming no inelastic processes, the presence of Raman scattering by water molecules, and the presence of both Raman scattering and fluorescence of chlorophyll-a pigment. Fluorescence of colored dissolved organic matter was omitted from all simulations. For each of these three simulation scenarios, the simulations were made for three sun zenith angles of 0&deg;, 30, and 60&deg; assuming clear skies, standard atmosphere, and wind speed of 5 m s^&minus;1. Thus, overall 29880 RT simulations were performed. The output results of these simulations include the radiance distributions, plane and scalar irradiances, and the whole set of AOPs including the remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines where all optical variables are reported in the spectral range from 350 to 750 nm at 5 nm intervals for different depths between the sea surface and 50 m. The consistency of this new synthetic database has been assessed through comparisons with in situ data and previously developed empirical relationships involving the IOPs and AOPs.

<strong class="journal-contentHeaderColor">Abstract.</strong> Radiative transfer (RT) simulations have long been used to study relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean from which the ocean apparent optical properties (AOPs) can be calculated. For example, inverse models to estimate IOPs from ocean color radiometric measurements have been developed or validated using results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range from the near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development was the generation of the synthetic dataset of seawater IOPs which served as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by probability distributions of IOPs that are consistent with global distributions representative of vast areas of open ocean pelagic environments and coastal regions covering a broad range of optical water types. The generation of the synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column and three simulation scenarios associated with sun zenith angle. Specifically, the simulations were made assuming no inelastic processes, the presence of Raman scattering by water molecules, and the presence of both Raman scattering and fluorescence of chlorophyll-a pigment. Fluorescence of colored dissolved organic matter was omitted from all simulations. For each of these three simulation scenarios, the simulations were made for three sun zenith angles of 0&deg;, 30, and 60&deg; assuming clear skies, standard atmosphere, and wind speed of 5 m s^&minus;1. Thus, overall 29880 RT simulations were performed. The output results of these simulations include the radiance distributions, plane and scalar irradiances, and the whole set of AOPs including the remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines where all optical variables are reported in the spectral range from 350 to 750 nm at 5 nm intervals for different depths between the sea surface and 50 m. The consistency of this new synthetic database has been assessed through comparisons with in situ data and previously developed empirical relationships involving the IOPs and AOPs.

<strong class="journal-contentHeaderColor">Abstract.</strong> Radiative transfer (RT) simulations have long been used to study relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean from which the ocean apparent optical properties (AOPs) can be calculated. For example, inverse models to estimate IOPs from ocean color radiometric measurements have been developed or validated using results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range from the near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development was the generation of the synthetic dataset of seawater IOPs which served as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by probability distributions of IOPs that are consistent with global distributions representative of vast areas of open ocean pelagic environments and coastal regions covering a broad range of optical water types. The generation of the synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column and three simulation scenarios associated with sun zenith angle. Specifically, the simulations were made assuming no inelastic processes, the presence of Raman scattering by water molecules, and the presence of both Raman scattering and fluorescence of chlorophyll-a pigment. Fluorescence of colored dissolved organic matter was omitted from all simulations. For each of these three simulation scenarios, the simulations were made for three sun zenith angles of 0&deg;, 30, and 60&deg; assuming clear skies, standard atmosphere, and wind speed of 5 m s^&minus;1. Thus, overall 29880 RT simulations were performed. The output results of these simulations include the radiance distributions, plane and scalar irradiances, and the whole set of AOPs including the remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines where all optical variables are reported in the spectral range from 350 to 750 nm at 5 nm intervals for different depths between the sea surface and 50 m. The consistency of this new synthetic database has been assessed through comparisons with in situ data and previously developed empirical relationships involving the IOPs and AOPs.