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Relevant bibliographies by topics / Radiative transfer models (RTM)

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Journal articles

Academic literature on the topic 'Radiative transfer models (RTM)'

Author: Grafiati

Published: 11 March 2023

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Journal articles on the topic "Radiative transfer models (RTM)"

1

Oh, Yisok, Jisung Geba Chang, and Maxim Shoshany. "An Improved Radiative Transfer Model for Polarimetric Backscattering from Agricultural Fields at C- and X-Bands." Journal of Electromagnetic Engineering and Science 21, no. 2 (2021): 104–10. http://dx.doi.org/10.26866/jees.2021.21.2.104.

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The first-order vector radiative transfer model (FVRTM) is modified mainly by examining the effects of leaf curvature of vegetation canopies, the higher-order multiple scattering among vegetation scattering particles, and the underlying-surface roughness for forward reflection on radar backscattering from farming fields at C- and X-bands. At first, we collected the backscattering coefficients measured by scatterometers and space-borne synthetic aperture radar (SAR), field-measured ground-truth data sets, and theoretical scattering models for radar backscattering from vegetation fields at micro

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2

del Águila, Ana, Dmitry S. Efremenko, Víctor Molina García, and Michael Yu Kataev. "Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O2 A- and CO2 Bands." Remote Sensing 12, no. 8 (2020): 1250. http://dx.doi.org/10.3390/rs12081250.

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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 radi

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3

Mejia, Felipe A., Ben Kurtz, Keenan Murray, et al. "Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth." Atmospheric Measurement Techniques 9, no. 8 (2016): 4151–65. http://dx.doi.org/10.5194/amt-9-4151-2016.

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Abstract. A method for retrieving cloud optical depth (τc) using a UCSD developed ground-based sky imager (USI) is presented. The radiance red–blue ratio (RRBR) method is motivated from the analysis of simulated images of various τc produced by a radiative transfer model (RTM). From these images the basic parameters affecting the radiance and red–blue ratio (RBR) of a pixel are identified as the solar zenith angle (θ0), τc, solar pixel angle/scattering angle (ϑs), and pixel zenith angle/view angle (ϑz). The effects of these parameters are described and the functions for radiance, Iλτc, θ0, ϑs,

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4

Vicent, Jorge, Jochem Verrelst, Neus Sabater, et al. "Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)." Geoscientific Model Development 13, no. 4 (2020): 1945–57. http://dx.doi.org/10.5194/gmd-13-1945-2020.

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Abstract. Atmospheric radiative transfer models (RTMs) are software tools that help researchers in understanding the radiative processes occurring in the Earth's atmosphere. Given their importance in remote sensing applications, the intercomparison of atmospheric RTMs is therefore one of the main tasks used to evaluate model performance and identify the characteristics that differ between models. This can be a tedious tasks that requires good knowledge of the model inputs/outputs and the generation of large databases of consistent simulations. With the evolution of these software tools, their

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5

Karthikeyan, Lanka, Ming Pan, Dasika Nagesh Kumar, and Eric F. Wood. "Effect of Structural Uncertainty in Passive Microwave Soil Moisture Retrieval Algorithm." Sensors 20, no. 4 (2020): 1225. http://dx.doi.org/10.3390/s20041225.

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Passive microwave sensors use a radiative transfer model (RTM) to retrieve soil moisture (SM) using brightness temperatures (TB) at low microwave frequencies. Vegetation optical depth (VOD) is a key input to the RTM. Retrieval algorithms can analytically invert the RTM using dual-polarized TB measurements to retrieve the VOD and SM concurrently. Algorithms in this regard typically use the τ-ω types of models, which consist of two third-order polynomial equations and, thus, can have multiple solutions. Through this work, we find that uncertainty occurs due to the structural indeterminacy that i

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6

Seidel, F. C., A. A. Kokhanovsky, and M. E. Schaepman. "Fast and simple model for atmospheric radiative transfer." Atmospheric Measurement Techniques Discussions 3, no. 3 (2010): 2225–73. http://dx.doi.org/10.5194/amtd-3-2225-2010.

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Abstract. Radiative transfer models (RTMs) are of utmost importance for quantitative remote sensing, especially for compensating atmospheric perturbation. A persistent trade-off exists between approaches that prefer accuracy at the cost of computational complexity, versus those favouring simplicity at the cost of reduced accuracy. We propose an approach in the latter category, using analytical equations, parameterizations and a correction factor to efficiently estimate the effect of molecular multiple scattering. We discuss the approximations together with an analysis of the resulting performa

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7

Seidel, F. C., A. A. Kokhanovsky, and M. E. Schaepman. "Fast and simple model for atmospheric radiative transfer." Atmospheric Measurement Techniques 3, no. 4 (2010): 1129–41. http://dx.doi.org/10.5194/amt-3-1129-2010.

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Abstract. Radiative transfer models (RTMs) are of utmost importance for quantitative remote sensing, especially for compensating atmospheric perturbation. A persistent trade-off exists between approaches that prefer accuracy at the cost of computational complexity, versus those favouring simplicity at the cost of reduced accuracy. We propose an approach in the latter category, using analytical equations, parameterizations and a correction factor to efficiently estimate the effect of molecular multiple scattering. We discuss the approximations together with an analysis of the resulting performa

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8

Albino, André, Daniele Bortoli, Mouhaydine Tlemçani, Abdeloawahed Hajjaji, and António Joyce. "Sensitivity analysis of atmospheric spectral irradiance model." European Physical Journal Applied Physics 88, no. 1 (2019): 11001. http://dx.doi.org/10.1051/epjap/2019190350.

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Many Radiative Transfer Models (RTM) have been developed to simulate and estimate solar irradiance. Theirs accuracy is well documents in literature nonetheless the effect of the parameters uncertainties on the established models has not been well studied yet. This work focuses on implementing a RTM based on the models found in the literature along with some updates, with the aim to study the sensitivity of the model towards the variations of the input parameters. The parameters studied in this paper are: the day of the year, the solar zenith angle, the local atmospheric pressure, the local tem

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9

del Águila, Ana, and Dmitry S. Efremenko. "Fast Hyper-Spectral Radiative Transfer Model Based on the Double Cluster Low-Streams Regression Method." Remote Sensing 13, no. 3 (2021): 434. http://dx.doi.org/10.3390/rs13030434.

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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.

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10

Abdelbaki, Asmaa, and Thomas Udelhoven. "A Review of Hybrid Approaches for Quantitative Assessment of Crop Traits Using Optical Remote Sensing: Research Trends and Future Directions." Remote Sensing 14, no. 15 (2022): 3515. http://dx.doi.org/10.3390/rs14153515.

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Remote sensing technology allows to provide information about biochemical and biophysical crop traits and monitor their spatiotemporal dynamics of agriculture ecosystems. Among multiple retrieval techniques, hybrid approaches have been found to provide outstanding accuracy, for instance, for the inference of leaf area index (LAI), fractional vegetation cover (fCover), and leaf and canopy chlorophyll content (LCC and CCC). The combination of radiative transfer models (RTMs) and data-driven models creates an advantage in the use of hybrid methods. Through this review paper, we aim to provide sta

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