Scattering effects in images, including those related to haze, fog and appearance of clouds, are fundamentally dictated by microphysical characteristics of the scatterers. This work defines and derives recovery of these characteristics, in a three-dimensional (3D) heterogeneous medium. Recovery is based on a novel tomography approach. Multi-view (multi-angular) and multi-spectral data are linked to the underlying microphysics using 3D radiative transfer, accounting for multiple-scattering. Despite the nonlinearity of the tomography model, inversion is enabled using a few approximations that we describe. As a case study, we focus on passive remote sensing of the atmosphere, where scatterer retrieval can benefit modeling and forecasting of weather, climate and pollution.

We seek to sense the three dimensional (3D) volumetric distribution of scatterers in a heterogenous medium. An important case study for such a medium is the atmosphere. Atmospheric contents and their role in Earth's radiation balance have significant uncertainties with regards to scattering components: aerosols and clouds. Clouds, made of water droplets, also lead to local effects as precipitation and shadows. Our sensing approach is computational tomography using passive multi-angular imagery. For light-matter interaction that accounts for multiple-scattering, we use the 3D radiative transfer equation as a forward model. Volumetric recovery by inverting this model suffers from a computational bottleneck on large scales, which include many unknowns. Steps taken make this tomography tractable, without approximating the scattering order or angle range.

We introduce a methodology for separating reflective layers of clouds in Earth remote sensing images. We propose a single-channel layer separation framework and extend it to multispectral layer separation. Efficient alternating minimization and fast operator-splitting methods are used to solve minimization problems. Specifically, we apply our methodology to separate strongly stratified and optically thin upper (cirrus) clouds from optically thick lower convective (cumulus) clouds in atmospheric imagery approximated as additive contributions to the observed signal. After setting up synthetic “truth” scenarios, we evaluate the accuracy of the two-layer separation results while varying the effective opaqueness of each of two types of cloud. We show that multispectral cloud layer separation is consistently more accurate than channel-by-channel cloud layer separation.

In this paper, we introduce methodology for multispectral layer separation. Efficient alternating minimization and fast operator-splitting methods are used to solve minimization problems. Specifically, we apply our methodology to separate strongly stratified and optically thin upper (cirrus) clouds from optically thick lower convective (cumulus) clouds in atmospheric imagery approximated as additive contributions to the observed signal. After setting up synthetic “truth” scenarios, we evaluate the accuracy of the two-layer separation results while varying the effective opaqueness of each of two types of cloud. We show that multispectral cloud layer separation is consistently more accurate than channel-by-channel cloud layer separation.

Physics-based retrievals of atmosphere and/or surface properties are generally multi- or hyperspectral in nature; some use multi-angle information as well. Recently, polarization has been added to the available input from sensors and accordingly modeled with vector radiative transfer (RT). At any rate, a single pixel is processed at a time using a forward RT model predicated on 1-D transport theory. Neighboring pixels are sometimes considered but, generally, just to formulate statistical constraints on the inversion based on spatial context. Herein, we demonstrate the power to be harnessed by adding bona fide multipixel techniques to the mix. We use a forward RT model in 2-D, sufficient for this demonstration and easily extended to 3-D, for the response of a single-wavelength imaging sensor. The data, an image, is used to infer position, size, and opacity of an absorbing atmospheric plume somewhere in a deep valley in the presence of partially known/partially unknown aerosol. We first describe the necessary innovation to speed-up forward multidimensional RT. In spite of its reputation for inefficiency, we use a Monte Carlo (MC) technique. However, the adopted scheme is highly accelerated without loss of accuracy by using “recycled” MC paths. This forward model is then put to work in a novel Bayesian inversion adapted to this kind of RT model where it is straightforward to trade precision and efficiency. Retrievals target the plume properties and the specific amount of aerosol. In spite of the limited number of pixels and low signal-to-noise ratio, there is added value for certain nuclear treaty verification applications.

The Multi-angle Imaging SpectroRadiometer (MISR) instrument has been collecting global Earth data from NASA's Terra satellite since February 2000. With its nine along-track view angles, four visible/near-infrared spectral bands, intrinsic spatial resolution of 275 m, and stable radiometric and geometric calibration, no instrument that combines MISR's attributes has previously flown in space. The more than 10-year (and counting) MISR data record provides unprecedented opportunities for characterizing long-term trends in aerosol, cloud, and surface properties, and includes 3-D textural information conventionally thought to be accessible only to active sensors.

We investigate the effect of broken clouds on the satellite-based retrieval of columnar water vapor using a near-infrared radiance ratio technique. A typical difference between the retrieval using only pixels directly illuminated by the Sun and pixels with mixed illumination containing direct sunlight as well as cloud shadows is found to be within 3%.

We survey the rapid progress of "off-beam" cloud lidar, from inception to validation via laboratory-scale simulations. Cloud observations from ground, aircraft and even space are covered. Finally, we describe future work in this instrument development effort born out of pure theory in the mid-1990s.

Dense isolated (cumulus-type) clouds are not amenable to the standard plane-parallel slab geometry used in operational remote sensing. We show that their opacity can nonetheless be estimated using an alternate geometrical model - the spherical cloud - which is also analytically tractable for photon transport by diffusion. The new retrieval method is illustrated with a cloudy Multispectral Thermal Imager (MTI) scene but could be applied to ground-based sky imagers. The only requirement is to have both dark and bright sides of the cloud in the same scene. Future application to the development of correction schemes for cloud-adjacency effects is discussed.

The independent pixel approximation (IPA), which treats radiative properties of each pixel independently by using standard plane-parallel calculations preserves scale-invariance found in the analyses of the horizontal variability of liquid water in marine stratocumulus clouds. Several studies, however, report a violation of scale-invariance in LANDSAT cloud radiance fields that are much smoother than cloud structure on small scales. This shows a limitation of IPA on small scales: it is unable to simulate the smooth small-scale behavior that is due to the horizontal photon transport. This paper introduces a "nonlocal" independent pixel approximation (NIPA) that extends the IPA by incorporating empirically the smoothing effects of horizontal interpixel fluxes through a convolution product of the IPA and an approximate Green function for radiative transfer. The authors also address the inverse problem of cloud optical depth retrieval from satellite data, showing how NIPA can be used to overcome the limitations of current IPA-based methods at small scales.