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Current Advances in Radiative Transfer Modeling for Satellite Optical Remote Sensing Applications

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 17414

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Special Issue Editors

Dr. Thomas Ruhtz

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Guest Editor

Freie Universität Berlin, Institut für Weltraumwissenschaften (Institute for Space Sciences), Carl-Heinrich-Becker Weg 6-10, 12165 Berlin, Germany
Interests: remote sensing; light scattering; polarization; retrieval of aerosol and cloud properties; radiative transfer; instrument design and technology
Special Issues, Collections and Topics in MDPI journals

Dr. Rene Preusker

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Freie Universität Berlin, Institut für Weltraumwissenschaften (Institute for Space Sciences), Carl-Heinrich-Becker Weg 6-10, 12165 Berlin, Germany
Interests: radiative transfer; optical remote sensing; atmosphere; clouds; aerosol; ocean; snow; ice; atmospheric radiation; light scattering

Dr. Alexander Kokhanovsky

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Guest Editor

Max Planck Institute for Chemistry, 55128 Mainz, Germany
Interests: cloud remote sensing; aerosol remote sensing; trace gas remote sensing; snow remote sensing; radiative transfer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Most remote sensing applications are based on the interpretation of electromagnetic radiation, either naturally reflected solar radiation, emitted thermal radiation from atmosphere and surface or man-made radiation (Lidar, Radar). The polarimetric, spectral, spatial, angular and temporal signature of the received signals are interpreted with respect to the particular targets. It is obvious, that the quantitative knowledge about the radiative transfer, describing the emergence, propagation and modification of the electromagnetic signal by terrestrial atmosphere is crucial for any remote sensing application, either to extract the desired information or to remove perturbing signals. This special issue will focus on current advances in radiative transfer models (RTMs) and their implementation and usage for spaceborne optical remote sensing applications. Several approaches to relate the measured signals to geophysical properties have evolved in the last decades. They depend on the complexity of the remote sensing problems, on time and computational constrains, on accuracy and precision requirements, on the availability of prior knowledge and last but not least on traditions in a particular research field. The different applications range from simple empirical regressions to more complex non-linear multidimensional optimizations, using RTMs and their Jacobians directly, or indirectly with look up tables or other approximations. Furthermore, it is critical how shortcomings and assumptions of the RTMs are included into the estimation of the retrieved parameter uncertainty and how they are included into a comprehensive validation strategy.

The very broad range of bandwidths of different remote sensing instruments, types and applications leads to a high number of different radiative transfer models(RTMs) dedicated to specific tasks and we would like to compile a description and a status of the current most used and state-of-the-art satellite applications. A number of new satellites and instruments are on the road with higher resolutions and accuracies. We will aim on recent results and descriptions on how RTMs are used to derive specific parameters in satellite remote sensing applications and their validation. The presentation of current radiative transfer models, their extensions and new approaches which will lead to faster results and/or higher accuracies is highly relevant to this special issue.

Dr. Rene Preusker
Dr. Ruhtz Thomas
Dr. Alexander Kokhanovsky
Guest Editors

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Keywords

Radiative transfer modeling

Satellite remote sensing

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17 pages, 5882 KiB

Open AccessArticle

Fast Hyper-Spectral Radiative Transfer Model Based on the Double Cluster Low-Streams Regression Method

by Ana del Águila and Dmitry S. Efremenko

Remote Sens. 2021, 13(3), 434; https://doi.org/10.3390/rs13030434 - 27 Jan 2021

Cited by 2 | Viewed by 1981

Abstract

Fast radiative transfer models (RTMs) are required to process a great amount of satellite-based atmospheric composition data. Specifically designed acceleration techniques can be incorporated in RTMs to simulate the reflected radiances with a fine spectral resolution, avoiding time-consuming computations on a fine resolution grid. In particular, in the cluster low-streams regression (CLSR) method, the computations on a fine resolution grid are performed by using the fast two-stream RTM, and then the spectra are corrected by using regression models between the two-stream and multi-stream RTMs. The performance enhancement due to such a scheme can be of about two orders of magnitude. In this paper, we consider a modification of the CLSR method (which is referred to as the double CLSR method), in which the single-scattering approximation is used for the computations on a fine resolution grid, while the two-stream spectra are computed by using the regression model between the two-stream RTM and the single-scattering approximation. Once the two-stream spectra are known, the CLSR method is applied the second time to restore the multi-stream spectra. Through a numerical analysis, it is shown that the double CLSR method yields an acceleration factor of about three orders of magnitude as compared to the reference multi-stream fine-resolution computations. The error of such an approach is below 0.05%. In addition, it is analysed how the CLSR method can be adopted for efficient computations for atmospheric scenarios containing aerosols. In particular, it is discussed how the precomputed data for clear sky conditions can be reused for computing the aerosol spectra in the framework of the CLSR method. The simulations are performed for the Hartley–Huggins, O

_{2}

A-, water vapour and CO

_{2}

weak absorption bands and five aerosol models from the optical properties of aerosols and clouds (OPAC) database. Full article

(This article belongs to the Special Issue Current Advances in Radiative Transfer Modeling for Satellite Optical Remote Sensing Applications)

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22 pages, 751 KiB

Open AccessArticle

A Proof-of-Concept Algorithm for the Retrieval of Total Column Amount of Trace Gases in a Multi-Dimensional Atmosphere

by Adrian Doicu, Dmitry S. Efremenko and Thomas Trautmann

Remote Sens. 2021, 13(2), 270; https://doi.org/10.3390/rs13020270 - 14 Jan 2021

Viewed by 1834

Abstract

An algorithm for the retrieval of total column amount of trace gases in a multi-dimensional atmosphere is designed. The algorithm uses (i) certain differential radiance models with internal and external closures as inversion models, (ii) the iteratively regularized Gauss–Newton method as a regularization tool, and (iii) the spherical harmonics discrete ordinate method (SHDOM) as linearized radiative transfer model. For efficiency reasons, SHDOM is equipped with a spectral acceleration approach that combines the correlated k-distribution method with the principal component analysis. The algorithm is used to retrieve the total column amount of nitrogen for two- and three-dimensional cloudy scenes. Although for three-dimensional geometries, the computational time is high, the main concepts of the algorithm are correct and the retrieval results are accurate. Full article

(This article belongs to the Special Issue Current Advances in Radiative Transfer Modeling for Satellite Optical Remote Sensing Applications)

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17 pages, 747 KiB

Open AccessArticle

A Spectral Acceleration Approach for the Spherical Harmonics Discrete Ordinate Method

by Adrian Doicu, Dmitry S. Efremenko and Thomas Trautmann

Remote Sens. 2020, 12(22), 3703; https://doi.org/10.3390/rs12223703 - 11 Nov 2020

Cited by 4 | Viewed by 2110

Abstract

A spectral acceleration approach for the spherical harmonics discrete ordinate method (SHDOM) is designed. This approach combines the correlated k-distribution method and some dimensionality reduction techniques applied on the optical parameters of an atmospheric system. The dimensionality reduction techniques used in this study are the linear embedding methods: principal component analysis, locality pursuit embedding, locality preserving projection, and locally embedded analysis. Through a numerical analysis, it is shown that relative to the correlated k-distribution method, PCA in conjunction with a second-order of scattering approximation yields an acceleration factor of 12. This implies that SHDOM equipped with this acceleration approach is efficient enough to perform spectral integration of radiance fields in inhomogeneous multi-dimensional media. Full article

(This article belongs to the Special Issue Current Advances in Radiative Transfer Modeling for Satellite Optical Remote Sensing Applications)

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19 pages, 11008 KiB

Open AccessArticle

Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O₂ A- and CO₂ Bands

by Ana del Águila, Dmitry S. Efremenko, Víctor Molina García and Michael Yu. Kataev

Remote Sens. 2020, 12(8), 1250; https://doi.org/10.3390/rs12081250 - 15 Apr 2020

Cited by 6 | Viewed by 2649

Abstract

Current atmospheric composition sensors provide a large amount of high spectral resolution data. The accurate processing of this data employs time-consuming line-by-line (LBL) radiative transfer models (RTMs). In this paper, we describe a method to accelerate hyperspectral radiative transfer models based on the clustering of the spectral radiances computed with a low-stream RTM and the regression analysis performed for the low-stream and multi-stream RTMs within each cluster. This approach, which we refer to as the Cluster Low-Streams Regression (CLSR) method, is applied for computing the radiance spectra in the O₂ A-band at 760 nm and the CO₂ band at 1610 nm for five atmospheric scenarios. The CLSR method is also compared with the principal component analysis (PCA)-based RTM, showing an improvement in terms of accuracy and computational performance over PCA-based RTMs. As low-stream models, the two-stream and the single-scattering RTMs are considered. We show that the error of this approach is modulated by the optical thickness of the atmosphere. Nevertheless, the CLSR method provides a performance enhancement of almost two orders of magnitude compared to the LBL model, while the error of the technique is below 0.1% for both bands. Full article

(This article belongs to the Special Issue Current Advances in Radiative Transfer Modeling for Satellite Optical Remote Sensing Applications)

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18 pages, 3401 KiB

Open AccessArticle

Nowcasting of Surface Solar Irradiance Using FengYun-4 Satellite Observations over China

by Liwei Yang, Xiaoqing Gao, Zhenchao Li, Dongyu Jia and Junxia Jiang

Remote Sens. 2019, 11(17), 1984; https://doi.org/10.3390/rs11171984 - 22 Aug 2019

Cited by 21 | Viewed by 3903

Abstract

The accurate prediction of surface solar irradiance is of great significance for the generation of photovoltaic power. Surface solar irradiance is affected by many random mutation factors, which means that there are great challenges faced in short-term prediction. In Northwest China, there are abundant solar energy resources and large desert areas, which have broad prospects for the development of photovoltaic (PV) systems. For the desert areas in Northwest China, where meteorological stations are scarce, satellite remote sensing data are extremely precious exploration data. In this paper, we present a model using FY-4A satellite images to forecast (up to 15–180 min ahead) global horizontal solar irradiance (GHI), at a 15 min temporal resolution in desert areas under different sky conditions, and compare it with the persistence model (SP). The spatial resolution of the FY-4A satellite images we used was 1 km × 1 km. Particle image velocimetry (PIV) was used to derive the cloud motion vector (CMV) field from the satellite cloud images. The accuracy of the forecast model was evaluated by the ground observed GHI data. The results showed that the normalized root mean square error (nRMSE) ranged from 18.9% to 21.6% and the normalized mean bias error (nMBE) ranged from 3.2% to 4.9% for time horizons from 15 to 180 min under all sky conditions. Compared with the SP model, the nRMSE value was reduced by about 6%, 8%, and 14% with the time horizons of 60, 120, and 180 min, respectively. Full article

(This article belongs to the Special Issue Current Advances in Radiative Transfer Modeling for Satellite Optical Remote Sensing Applications)

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18 pages, 8069 KiB

Open AccessLetter

The Radiative Transfer Characteristics of the O₂ Infrared Atmospheric Band in Limb-Viewing Geometry

by Weiwei He, Kuijun Wu, Yutao Feng, Di Fu, Zhenwei Chen and Faquan Li

Remote Sens. 2019, 11(22), 2702; https://doi.org/10.3390/rs11222702 - 18 Nov 2019

Cited by 13 | Viewed by 3604

Abstract

The O₂(a¹Δ_g) emission near 1.27 μm provides an important means to remotely sense the thermal characteristics, dynamical features, and compositional structures of the upper atmosphere because of its photochemistry and spectroscopic properties. In this work, an emission–absorption transfer model for limb measurements was developed to calculate the radiation and scattering spectral brightness by means of a line-by-line approach. The nonlocal thermal equilibrium (non-LTE) model was taken into account for accurate calculation of the O₂(a¹Δ_g) emission by incorporating the latest rate constants and spectral parameters. The spherical adding and doubling methods were used in the multiple scattering model. Representative emission and absorption line shapes of the

O_{2} (a^{1} Δ_{g}, υ^{'} = 0) \to O_{2} (X Σ_{g}^{3}, υ^{″} = 0)

band and their spectral behavior varying with altitude were examined. The effects of solar zenith angle, surface albedo, and aerosol loading on the line shapes were also studied. This paper emphasizes the advantage of using infrared atmospheric band for remote sensing of the atmosphere from 20 up to 120 km, a significant region where the strongest coupling between the lower and upper atmosphere occurs. Full article

(This article belongs to the Special Issue Current Advances in Radiative Transfer Modeling for Satellite Optical Remote Sensing Applications)

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