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Добірка наукової літератури з теми "Radiative transfer models (RTM)"
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Статті в журналах з теми "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.
Повний текст джерелаАнотація:
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 microwaves. Then, these effects on the RTM were examined using the database at the C- and X-bands. Finally, an improved RTM was obtained by adjusting its parameters, mainly related with the leaf curvature, the higher-order multiple scattering, and the underlying-surface small-roughness characteristics, and its accuracy was verified by comparisons between the improved RTM and measurement data sets.
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.
Повний текст джерелаАнотація:
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.
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.
Повний текст джерелаАнотація:
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, ϑz, and RBRτc, θ0, ϑs, ϑz are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τc, where RBR increases with τc up to about τc = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured Iλmeasϑs, ϑz, in addition to RBRmeasϑs, ϑz, to obtain a unique solution for τc. The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement (ARM) program site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min et al. (2003) method for overcast skies. τc values ranged from 0 to 80 with values over 80, being capped and registered as 80. A τc RMSE of 2.5 between the Min et al. (2003) method and the USI are observed. The MWR and USI have an RMSE of 2.2, which is well within the uncertainty of the MWR. The procedure developed here provides a foundation to test and develop other cloud detection algorithms.
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.
Повний текст джерелаАнотація:
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 increase in complexity bears implications for their use in practical applications and model intercomparison. Existing RTM-specific graphical user interfaces are not optimized for performing intercomparison studies of a wide variety of atmospheric RTMs. In this paper, we present the Atmospheric Look-up table Generator (ALG) version 2.0, a new software tool that facilitates generating large databases for a variety of atmospheric RTMs. ALG facilitates consistent and intuitive user interaction to enable the running of model executions and storing of RTM data for any spectral configuration in the optical domain. We demonstrate the utility of ALG in performing intercomparison studies of radiance simulations from broadly used atmospheric RTMs (6SV, MODTRAN, and libRadtran) through global sensitivity analysis. We expect that providing ALG to the research community will facilitate the usage of atmospheric RTMs to a wide range of applications in Earth observation.
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.
Повний текст джерелаАнотація:
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 is inherent in all τ-ω types of models in passive microwave SM retrieval algorithms. In the process, a new analytical solution for concurrent VOD and SM retrieval is presented, along with two widely used existing analytical solutions. All three solutions are applied to a fixed framework of RTM to retrieve VOD and SM on a global scale, using X-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data. Results indicate that, with structural uncertainty, there ensues a noticeable impact on the VOD and SM retrievals. In an era where the sensitivity of retrieval algorithms is still being researched, we believe the structural indeterminacy of RTM identified here would contribute to uncertainty in the soil moisture retrievals.
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.
Повний текст джерелаАнотація:
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 performance and accuracy. The proposed Simple Model for Atmospheric Radiative Transfer (SMART) decreases the calculation time by a factor of more than 25 in comparison to the benchmark RTM~6S on the same infrastructure. The approximative computation of the atmospheric reflectance factor by SMART has an uncertainty ranging from about 5% to 10% for nadir spaceborne and airborne observational conditions. The combination of a large solar zenith angle (SZA) with high aerosol optical depth (AOD) at low wavelengths lead to uncertainties of up to 15%. SMART can be used to simulate the hemispherical conical reflectance factor (HCRF) for spaceborne and airborne sensors, as well as for the retrieval of columnar AOD.
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.
Повний текст джерелаАнотація:
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 performance and accuracy. The proposed Simple Model for Atmospheric Radiative Transfer (SMART) decreases the calculation time by a factor of more than 25 in comparison to the benchmark RTM 6S on the same infrastructure. The relative difference between SMART and 6S is about 5% for spaceborne and about 10% for airborne computations of the atmospheric reflectance function. The combination of a large solar zenith angle (SZA) with high aerosol optical depth (AOD) at low wavelengths lead to relative differences of up to 15%. SMART can be used to simulate the hemispherical conical reflectance factor (HCRF) for spaceborne and airborne sensors, as well as for the retrieval of columnar AOD.
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.
Повний текст джерелаАнотація:
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 temperature, the relative humidity, the height of ozone layer concentration, the ozone concentration, the single scattering albedo, the ground albedo, the Ångström’s exponent and the aerosol optical depth. The sensibility analysis is achieved using the Normalized Root Mean Square Error (NRMSE) as an independent function, calculated with a set of simulated measurements of spectral global solar irradiance and a reference spectrum generated with a group of standard input parameters.
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.
Повний текст джерелаАнотація:
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, O2 A-, water vapour and CO2 weak absorption bands and five aerosol models from the optical properties of aerosols and clouds (OPAC) database.
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.
Повний текст джерелаАнотація:
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 state-of-the-art hybrid retrieval schemes and theoretical frameworks. To achieve this, we reviewed and systematically analyzed publications over the past 22 years. We identified two hybrid-based parametric and hybrid-based nonparametric regression models and evaluated their performance for each variable of interest. From the results of our extensive literature survey, most research directions are now moving towards combining RTM and machine learning (ML) methods in a symbiotic manner. In particular, the development of ML will open up new ways to integrate innovative approaches such as integrating shallow or deep neural networks with RTM using remote sensing data to reduce errors in crop trait estimations and improve control of crop growth conditions in very large areas serving precision agriculture applications.