Remote sensing from satellite or airborne platforms of land or sea surfaces in the visible and near infrared is strongly affected by the presence of the atmosphere along the path from Sun to target (surface) to sensor. This paper presents 6S (Second Simulation of the Satellite Signal in the Solar Spectrum), a computer code which can accurately simulate the above problems. The 6S code is an improved version of 5S (Simulation of the Satellite Signal in the Solar Spectrum), developed by the Laboratoire d'Optique Atmospherique ten years ago. The new version now permits calculations of near-nadir (down-looking) aircraft observations, accounting for target elevation, non lambertian surface conditions, and new absorbing species (CH/sub 4/, N/sub 2/O, CO). The computational accuracy for Rayleigh and aerosol scattering effects has been improved by the use of state-of-the-art approximations and implementation of the successive order of scattering (SOS) algorithm. The step size (resolution) used for spectral integration has been improved to 2.5 nm. The goal of this paper is not to provide a complete description of the methods used as that information is detailed in the 6S manual, but rather to illustrate the impact of the improvements between 5S and 6S by examining some typical remote sensing situations. Nevertheless, the 6S code has still limitations. It cannot handle spherical atmosphere and as a result, it cannot be used for limb observations. In addition, the decoupling the authors are using for absorption and scattering effects does not allow to use the code in presence of strong absorption bands.

Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview

Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.

/pdf/vertex-component-analysis-a-fast-algorithm-to-unmix-1egulrpm76.pdf

Vertex component analysis: a fast algorithm to unmix hyperspectral data

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

/pdf/hyperspectral-unmixing-overview-geometrical-statistical-and-2lb11fr9l4.pdf

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Rates and reactions of biogeochemical processes vary in space and time to produce both hot spots and hot moments of elemental cycling. We define biogeochemical hot spots as patches that show disproportionately high reaction rates relative to the surrounding matrix, whereas hot moments are defined as short periods of time that exhibit disproportionately high reaction rates relative to longer intervening time periods. As has been appreciated by ecologists for decades, hot spot and hot moment activity is often enhanced at terrestrial-aquatic interfaces. Using examples from the carbon (C) and nitrogen (N) cycles, we show that hot spots occur where hydrological flowpaths converge with substrates or other flowpaths containing complementary or missing reactants. Hot moments occur when episodic hydrological flowpaths reactivate and/or mobilize accumulated reactants. By focusing on the delivery of specific missing reactants via hydrologic flowpaths, we can forge a better mechanistic understanding of the factors that create hot spots and hot moments. Such a mechanistic understanding is necessary so that biogeochemical hot spots can be identified at broader spatiotemporal scales and factored into quantitative models. We specifically recommend that resource managers incorporate both natural and artificially created biogeochemical hot spots into their plans for water quality management. Finally, we emphasize the needs for further research to assess the potential importance of hot spot and hot moment phenomena in the cycling of different bioactive elements, improve our ability to predict their occurrence, assess their importance in landscape biogeochemistry, and evaluate their utility as tools for resource management.

/pdf/biogeochemical-hot-spots-and-hot-moments-at-the-interface-of-1vpqinkleu.pdf

Biogeochemical Hot Spots and Hot Moments at the Interface of Terrestrial and Aquatic Ecosystems

Acknowledgements 1. Introduction 2. Electromagnetic wave propagation 3. The absorption of light 4. Specular reflection 5. Single particle scattering: perfect spheres 6. Single particle scattering: irregular particles 7. Propagation in a nonuniform medium: the equation of radiative transfer 8. The bidirectional reflectance of a semi-infinite medium 9. The opposition effect 10. A miscellany of bidirectional reflectances and related quantities 11. Integrated reflectances and planetary photometry 12. Photometric effects of large scale roughness 13. Polarization 14. Reflectance spectroscopy 15. Thermal emission and emittance spectroscopy 16. Simultaneous transport of energy by radiation and conduction Appendix A. A brief review of vector calculus Appendix B. Functions of a complex variable Appendix C. The wave equation in spherical coordinates Appendix D. Fraunhoffer diffraction by a circular hole Appendix E. Table of symbols Bibliography Index.

Theory of Reflectance and Emittance Spectroscopy

Introduction The basic expression for the bidirectional reflectance of a semi-infinite medium of isotropically scattering particles was derived in Chapter 8 and is given by equation (8.48). This expression was further refined to include an approximate correction for anisotropic scatterers (8.60) and considerations of the effects of porosity (8.70) and the opposition effect in Chapter 9 (9.47). Variants in different geometries were discussed in Chapter 10. In this chapter we will derive expressions that involve integration of the bidirectional reflectance over one or both hemispheres. The remote sensing of bodies of the solar system has its own nomenclature because of the special problems of astronomical observation. Expressions for quantities commonly encountered in planetary spectrophotometry will be derived. Only media for which the porosity factor K = 1 will be treated. Approximate expressions when K > 1 may be readily obtained by substituting one of the linear approximations (8.54 or 8.55) for the H function in the integrand. Integrated reflectances Biconical reflectances If the source and detector do not occupy negligibly small solid angles as seen from the surface, appropriate expressions for the reflectances may be found by numerically integrating one of the above equations over the angular distribution of the radiance from the source and the angular distribution of the response of the detector. In general, such reflectances will be biconical. However, because they would be specific to each particular system, it would not be particularly useful to discuss biconical reflectances in detail.

Theory of Reflectance and Emittance Spectroscopy: Integrated reflectances and planetary photometry

The near-infrared reflectance of a 73,000 km2 area on Titan changed between July 2004 and March of 2006. The reflectance of the region (latitude 26S, longitude 78W) increased twofold between July 2004 and March-April 2005. It then returned to the July 2004 level by November 2005. By late December 2005 the reflectance had surged upward again to a new maximum. It then declined for the next three months. Detailed analyses indicate that the brightenings are a surface phenomenon, making these the first changes seen on Titan’s surface. The spectral differences between the region and its surroundings rule out the ices of H2O, CO2, and CH4 as possible causes. Remarkably, the change is spectrally consistent with the deposition and removal of ammoniated materials. NH3 has been proposed as a constituent of Titan’s interior but not its surface or atmosphere. This transitory NH3 spectral signature is consistent with occasional effusion events in which juvenile ammonia is brought to the surface. Its decomposition may feed nitrogen to the atmosphere. The size of the region suggests it may exceed the size of the largest active volcanic areas in the solar system.

/pdf/saturn-s-titan-cassini-vims-reports-regional-reflectance-2dp9zxfowr.pdf

Saturn's Titan: Cassini VIMS Reports Regional Reflectance Change Consistent with Surface Activity

The Opposition Effect in Simulated Planetary Regoliths: Circular and Linear Polarization Ratio Change with Phase Angle

The morphology and optical properties of the surface of Mercury resemble that of the moon in remarkable detail, recording a very similar sequence of events; chemical and mineralogical similarity of the outer layers is implied. Mercury is probably a differentiated planet with an iron-rich core. Differentiation is inferred to have occurred very early. No evidence of atmospheric modification of any landform is found. Large-scale scarps and ridges unlike lunar or Martian features may reflect a unique period of planetary compression near the end of heavy bombardment, perhaps related to contraction of the core.

/pdf/television-observations-of-mercury-by-mariner-10-4p8tgjw6j6.pdf

Television observations of Mercury by Mariner 10

Aims. We present a multiband photometric study of the lunar mare surface based on in situ measurements using the Panoramic Camera on board the Chang’E-3 mission. We aim to obtain photometric properties and to investigate the differences in measuring sites in various conditions. Methods. Data were collected with a vast range of phase angles from 0.5 ◦ to 160 ◦ , probing 0–5 ◦ phases to constrain the opposition effect (OE) and phases above 120 ◦ to constrain the phase function. Stray light and spectral corrections were conducted to calibrate the regolith’s bidirectional reflectance distribution function. We employed the Hapke photometric model to describe the regolith’s photometric properties. Results. Phase functions and Hapke parameters at three measuring sites were retrieved. The mare regolith at the measuring sites had a single-scattering albedo (SSA) of 0.17–0.21 for the green channel and exhibited backscattering and a strong OE. The coherent backscatter opposition effect (CBOE) represented the majority of OE, with a proportion of around 80% for pristine regolith. The phase ratio curves were arch-shaped, and the arch positions of the curves were associated with grain size.

Bruce Hapke

Papers

Theory of Reflectance and Emittance Spectroscopy: Integrated reflectances and planetary photometry

Saturn's Titan: Cassini VIMS Reports Regional Reflectance Change Consistent with Surface Activity

The Opposition Effect in Simulated Planetary Regoliths: Circular and Linear Polarization Ratio Change with Phase Angle

Television observations of Mercury by Mariner 10

Spectrophotometry of the lunar regolith using the Chang’E-3 Panoramic Camera (PCAM)