Atmospheric Transfer Function Research Articles

Restoring the night sky darkness at Observatorio del Teide: First application of the model Illumina version 2

ABSTRACT The propagation of artificial light into real environments is complex. To perform its numerical modelling with accuracy, one must consider hyperspectral properties of the lighting devices and their geographic positions, the hyperspectral properties of the ground reflectance, the size and distribution of small-scale obstacles, the blocking effect of topography, the lamps angular photometry and the atmospheric transfer function (aerosols and molecules). A detailed radiative transfer model can be used to evaluate how a particular change in the lighting infrastructure may affect the sky radiance. In this paper, we use the new version (v2) of the Illumina model to evaluate a night sky restoration plan for the Teide Observatory located on the island of Tenerife, Spain. In the past decades, the sky darkness was severely degraded by growing light pollution on the Tenerife Island. In this work, we use the contribution maps giving the effect of each pixel of the territory to the artificial sky radiance. We exploit the hyperspectral capabilities of Illumina v2 and show how the contribution maps can be integrated over regions or municipalities according to the Johnson–Cousins photometric bands spectral sensitivities. The sky brightness reductions per municipality after a complete shutdown and a conversion to light-emitting diodes are calculated in the Johnson–Cousins B, V, R bands. We found that the conversion of the lighting infrastructure of Tenerife with LED (1800 and 2700 K), according to the conversion strategy in force, would result in a zenith V-band sky brightness reduction of ≈0.3 mag arcsec−2.

Compensation of Oxygen Transmittance Effects for Proximal Sensing Retrieval of Canopy-Leaving Sun-Induced Chlorophyll Fluorescence.

Estimates of Sun–Induced vegetation chlorophyll Fluorescence (SIF) using remote sensing techniques are commonly determined by exploiting solar and/or telluric absorption features. When SIF is retrieved in the strong oxygen (O2) absorption features, atmospheric effects must always be compensated. Whereas correction of atmospheric effects is a standard airborne or satellite data processing step, there is no consensus regarding whether it is required for SIF proximal–sensing measurements nor what is the best strategy to be followed. Thus, by using simulated data, this work provides a comprehensive analysis about how atmospheric effects impact SIF estimations on proximal sensing, regarding: (1) the sensor height above the vegetated canopy; (2) the SIF retrieval technique used, e.g., Fraunhofer Line Discriminator (FLD) family or Spectral Fitting Methods (SFM); and (3) the instrument’s spectral resolution. We demonstrate that for proximal–sensing scenarios compensating for atmospheric effects by simply introducing the O2 transmittance function into the FLD or SFM formulations improves SIF estimations. However, these simplistic corrections still lead to inaccurate SIF estimations due to the multiplication of spectrally convolved atmospheric transfer functions with absorption features. Consequently, a more rigorous oxygen compensation strategy is proposed and assessed by following a classic airborne atmospheric correction scheme adapted to proximal sensing. This approach allows compensating for the O2 absorption effects and, at the same time, convolving the high spectral resolution data according to the corresponding Instrumental Spectral Response Function (ISRF) through the use of an atmospheric radiative transfer model. Finally, due to the key role of O2 absorption on the evaluated proximal–sensing SIF retrieval strategies, its dependency on surface pressure (p) and air temperature (T) was also assessed. As an example, we combined simulated spectral data with p and T measurements obtained for a one–year period in the Hyytiälä Forestry Field Station in Finland. Of importance hereby is that seasonal dynamics in terms of T and p, if not appropriately considered as part of the retrieval strategy, can result in erroneous SIF seasonal trends that mimic those of known dynamics for temperature–dependent physiological responses of vegetation.

Influence of speckle image reconstruction on photometric precision for large solar telescopes

Context. High-resolution observations from large solar telescopes require adaptive optics (AO) systems to overcome image degradation caused by Earth’s turbulent atmosphere. AO corrections are, however, only partial. Achieving near-diffraction limited resolution over a large field of view typically requires post-facto image reconstruction techniques to reconstruct the source image.Aims. This study aims to examine the expected photometric precision of amplitude reconstructed solar images calibrated using models for the on-axis speckle transfer functions and input parameters derived from AO control data. We perform a sensitivity analysis of the photometric precision under variations in the model input parameters for high-resolution solar images consistent with four-meter class solar telescopes.Methods. Using simulations of both atmospheric turbulence and partial compensation by an AO system, we computed the speckle transfer function under variations in the input parameters. We then convolved high-resolution numerical simulations of the solar photosphere with the simulated atmospheric transfer function, and subsequently deconvolved them with the model speckle transfer function to obtain a reconstructed image. To compute the resulting photometric precision, we compared the intensity of the original image with the reconstructed image.Results. The analysis demonstrates that high photometric precision can be obtained for speckle amplitude reconstruction using speckle transfer function models combined with AO-derived input parameters. Additionally, it shows that the reconstruction is most sensitive to the input parameter that characterizes the atmospheric distortion, and sub-2% photometric precision is readily obtained when it is well estimated.

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

Physically-based radiative transfer models (RTMs) help understand the interactions of radiation with vegetation and atmosphere. However, advanced RTMs can be computationally burdensome, which makes them impractical in many real applications, especially when many state conditions and model couplings need to be studied. To overcome this problem, it is proposed to substitute RTMs through surrogate meta-models also named emulators. Emulators approximate the functioning of RTMs through statistical learning regression methods, and can open many new applications because of their computational efficiency and outstanding accuracy. Emulators allow fast global sensitivity analysis (GSA) studies on advanced, computationally expensive RTMs. As a proof-of-concept, three machine learning regression algorithms (MLRAs) were tested to function as emulators for the leaf RTM PROSPECT-4, the canopy RTM PROSAIL, and the computationally expensive atmospheric RTM MODTRAN5. Selected MLRAs were: kernel ridge regression (KRR), neural networks (NN) and Gaussian processes regression (GPR). For each RTM, 500 simulations were generated for training and validation. The majority of MLRAs were excellently validated to function as emulators with relative errors well below 0.2%. The emulators were then put into a GSA scheme and compared against GSA results as generated by original PROSPECT-4 and PROSAIL runs. NN and GPR emulators delivered identical GSA results, while processing speed compared to the original RTMs doubled for PROSPECT-4 and tripled for PROSAIL. Having the emulator-GSA concept successfully tested, for six MODTRAN5 atmospheric transfer functions (outputs), i.e., direct and diffuse at-surface solar irradiance ( E d i f , E d i r ), direct and diffuse upward transmittance ( T d i r , T d i f ), spherical albedo (S) and path radiance ( L 0 ), the most accurate MLRA’s were subsequently applied as emulator into the GSA scheme. The sensitivity analysis along the 400–2500 nm spectral range took no more than a few minutes on a contemporary computer—in comparison, the same analysis in the original MODTRAN5 would have taken over a month. Key atmospheric drivers were identified, which are on the one hand aerosol optical properties, i.e., aerosol optical thickness (AOT), Angstrom coefficient (AMS) and scattering asymmetry variable (G), mostly driving diffuse atmospheric components, E d i f and T d i f ; and those affected by atmospheric scattering, L 0 and S. On the other hand, as expected, AOT, AMS and columnar water vapor (CWV) in the absorption regions mostly drive E d i r and T d i r atmospheric functions. The presented emulation schemes showed very promising results in replacing costly RTMs, and we think they can contribute to the adoption of machine learning techniques in remote sensing and environmental applications.

Lightness constancy: from haze illusion to haze removal

The human visual system processes lightness constancy to estimate reflectance under complex viewing conditions. We focus on the underlying reason the human visual system is conscious of haze, and discuss the single-image haze-removal problem based on lightness constancy from the beginning of haze illusion. Following the basic principle and model of 'atmospheric transfer function', we bring about a simple dehazing paradigm by estimating two values using bilateral filtering. Practical investigation of parameters and design are analyzed in detail. Furthermore, a novel quantitative standard haziness presents itself naturally during the estimation process. Comparative study and objective evaluation demonstrate that the proposed method is fast and effective, yielding high-contrast and vivid haze-free images.

Fast computation of 2D transfer functions from adaptive optics data

AbstractThe use of atmospheric transfer functions is common in image reconstruction techniques such as speckle interferometry to calibrate the Fourier amplitudes of the reconstructed images. Thus, an accurate model is needed to ensure proper photometry in the reconstruction. The situation complicates when adaptive optics (AO) are used during data acquisition. I propose a novel technique to derive two‐dimensional transfer functions from data collected using AO simultaneously with the observations. The technique is capable to compute the relevant transfer functions within a short time for the prevailing atmospheric conditions and AO performance during data acquisition (© 2010 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Field dependent amplitude calibration of adaptive optics supported solar speckle imaging

Adaptive optics supported solar speckle imaging requires the calibration of the source's Fourier amplitudes with the transfer function of atmosphere and optics. We present analytical models for the relevant transfer functions of an adaptive optics systems. The models include the effect of an arbitrary correction as well as anisoplanatism. The proposed models have been compared with observational data using measurements of alpha-Orionis and of the solar surface delivering both a direct and indirect method (using the spectral ratio technique) for validation. We find that measurements and model agree to a satisfactory degree.

Maximum <italic>a posteriori</italic> image and seeing condition estimation from partially coherent two-dimensional light detection and ranging images

Recent developments in staring focal plane technology have spawned significant interest in the application of gated laser radar systems to battlefield remote sensing. Such environments are characterized by rapidly changing atmospheric seeing conditions and significant image distortion caused by long slant-range paths through the most dense regions of the atmosphere. Limited weight, space, and computational resources tend to prohibit the application of adaptive optic systems to mitigate atmospheric image distortion. We demonstrate and validate the use of a fast, iterative, maximum a posteriori (MAP) estimator to estimate both the original target scene and the ensemble-averaged atmospheric optical transfer function parameterized by Fried's seeing parameter. Wide-field-of-view sensor data is simulated to emulate images collected on an experimental test range. Simulated and experimental multiframe motion-compensated average images are deconvolved by the MAP estimator to produce most likely estimates of the truth image as well as the atmospheric seeing condition. For comparison, Fried's seeing parameter is estimated from experimentally collected images using a knife-edge response technique. The MAP estimator is found to yield seeing condition estimates within approximately 6% using simulated speckle images, and within approximately 8% of knife-edge derived truth for a limited set of experimentally collected image data.

Simulation of additive transport from the land surface on the basis of experimental data on heat transfer in the atmospheric surface layer

A method is proposed to study the transport of a passive additive in the atmospheric surface layer with the use of the atmospheric transfer function. This method makes it possible to estimate the spatial distribution of the concentration of a passive additive in the atmospheric surface layer from the additive’s surface source without experimentally determining the vertical profile of the transport coefficient or without resorting to various hypotheses for the character of its behavior. The transfer function, which contains the information on the wind-field structure, can be obtained from simple one-point measurements of surface-and air-temperature fluctuations and from subsequent spectral processing of the data. The effects of the wind-velocity profile and turbulence on the spatial distribution of additive concentration are assessed. This method allows one to simplify experiments during development and verification of the models of atmospheric diffusion. This method may also be useful in emergency situations to predict the propagation of hazardous additives.

Regional mapping of planetary surfaces with imaging spectroscopy

We present a method to determine spectral differences and compositional variability of planetary surfaces when remotely sensed by means of imaging spectroscopy instrumentation. The quantity frequently measured in remote sensing of planetary bodies in the 0.3-2.5 μm spectral range is the reflectance spectrum and it is used to study the mineralogic composition of the surface being sensed. Very often, however, this quantity is difficult to measure owing to lack of knowledge either of atmospheric extinction or of analogous solar spectra. The method we describe allows all the regions in a surface having spectral similarity to be identified and does not require the reflectance spectrum to be measured, taking full advantage of imaging spectroscopy and classification methods. In practice, all the spectra of the image are divided by the spectrum of a pixel (or an average spectrum) taken in the image itself. In this way, both the instrument and the atmospheric transfer functions are eliminated and it is possible to compare the spectra with one another. These new relative spectra are then classified using a clustering algorithm in order to recognize spectral similarities. The final result is a map showing all the regions in the image having similar spectra which can be linked to the mineralogical composition of the surface under study. The method has been applied to Earth-based observations of the Moon but can be equally used with other high spatial imaging spectroscopy data provided by future interplanetary missions.

Atmospheric structure function measurements with a Shack–Hartmann wave-front sensor

The phase structure function has been used as a convenient way to characterize aberrations introduced on optical propagation by the atmosphere. It forms the theoretical basis for the calculation of such things as the long- and short-exposure atmospheric transfer function. The structure function is difficult to measure directly and is usually assumed to follow Kolmogorov statistics. We present here a technique for direct measurement of the structure function through the use of a Shack-Hartmann wave-front sensor. Experiments confirm that the atmosphere behaves according to Kolmogorov theory most of the time. However, some instances of non-Kolmogorov behavior have been noted.

Optical astronomical angular resolution

The authors study the characteristic parameters of the optical resolution in optical astronomy. Relations are established between the parameters of the atmospheric transfer function and those which are measured on a star profile. Then, they compute a statistical series of images with a log-normal distribution function, the deviation of each image from its average position having a Gaussian probability density. The Fourier transform of the image obtained by adding without recentering is the long exposure transfer function of Fried. The Fourier transform of the image obtained by adding short exposures properly centered is the short exposure transfer function An experimental study shows that the standard deviation of the normal law of the logarithm of angular resolution remains constant at Pic-du-Midi Observatory for a fixed wavelength.

The Influence of Oxygen Absorption on Frequencies near 60 GHz: A—Review

Recent progress in the characterisation of the atmospheric propagation of millimeter waves in the oxygen absorption region is reviewed. Absorption by atmospheric gases is dealt briefly and the physical mechanisms behind the absorption is explained. Brief history of the models for describing the complex refractive index of the atmosphere is presented. Using the Ott and Thompson model for the complex refractive index of the atmosphere, the significance of the atmospheric transfer function is discused. Some of the points about the propagation experimental results which are considered of more practical interest are summarised.

Spectral dependence of the optical transfer function of the earth's atmosphere under different meteorological conditions

Spectral dependence of the optical transfer function of the earth's atmosphere under different meteorological conditions

Influence of Aerosol Scattering on Atmospheric Blurring of Surface Features

We investigate the influence of the shape of atmospheric scattering phase functions on blurring of surface detail in images acquired from space. The effects are characterized by computing atmospheric transfer functions and by solving the multidimensional equation of radiative transfer using a Fourier transform method. We predict that increased forward scattering enhances the detectability of atmospheric blurring near reflectance boundaries. Results for off-nadir viewing are also presented.

Composite pictures of recentred short exposures to improve the angular resolution

Measurements of line spread function and atmospheric transfer function's parameters have been made at the two metre telescope of the Pic du Midi Observatory. Their correlation with the classical Fried's parameter r0 are published. For double stars size estimation is made of the coherence patch for image motion and for image blurring: at 14 the correlation is almost perfect. The resolution of a short exposure composite is compared with the resolution of a long exposure. The gain agrees with the theory. A gain in resolution by a factor 2 can be achieved if the telescope is used with optimum aperture 4 r0.

Excitation of acoustic-gravity waves in a realistic thermosphere

The response of the upper atmosphere due to the excitation of acoustic-gravity waves by various sources is studied under realistic thermospheric conditions. Losses due to viscosity, thermal conduction and ion drag as well as the effects of vertical temperature variations and winds are all taken into account. The general solution is obtained in terms of the eight characteristic wave modes that propagate in the atmosphere. An atmospheric transfer function is defined which determines the response of the upper atmosphere due to general sources. Examples of the transfer function are computed for model atmospheres. It is found that the transfer functions are peaked in the wave-number space indicating strong filtering effects, and that their behaviour is affected by the background winds. The transfer functions are then applied to obtain some transient responses of the atmosphere due to excitations. The results are found to differ substantially from those obtained for a lossless, isothermal atmosphere. Implications of the computed results to the interpretation of observed acoustic-gravity wave data are discussed.

Measurements of the Atmospheric Transfer Function at Mauna Kea, Hawaii

Measurements are presented of the long time-averaged transfer function (or mutual coherence function) of the atmosphere, taken at Mauna Kea Observatory, Hawaii, using a coherence interferometer. Over the 10 nights of observation the seeing was variable, ranging from typical widths of the transfer function at the 25 per cent level of approximately 0.23 cycles/arcsec (2.5 cm in the pupil) to approximately 1.00 cycles/arcsec (11.0 cm in the pupil). The latter values correspond to sub-arcsec seeing. The variation with zenith angle and the form of the atmospheric transfer function were briefly investigated.

A Coherence Interferometer for Direct Measurement of the Atmospheric Transfer Function

An interferometer for measuring the atmospheric transfer function has been built and tested. We briefly describe the theory and design of the instrument and also present some preliminary results.