The spectral signature of cloud spatial structure in shortwave irradiance.
ABSTRACT: In this paper, we used cloud imagery from a NASA field experiment in conjunction with three-dimensional radiative transfer calculations to show that cloud spatial structure manifests itself as a spectral signature in shortwave irradiance fields - specifically in transmittance and net horizontal photon transport in the visible and near-ultraviolet wavelength range. We found a robust correlation between the magnitude of net horizontal photon transport (H) and its spectral dependence (slope), which is scale-invariant and holds for the entire pixel population of a domain. This was surprising at first given the large degree of spatial inhomogeneity. We prove that the underlying physical mechanism for this phenomenon is molecular scattering in conjunction with cloud spatial structure. On this basis, we developed a simple parameterization through a single parameter ε, which quantifies the characteristic spectral signature of spatial inhomogeneities. In the case we studied, neglecting net horizontal photon transport leads to a local transmittance bias of ±12-19 %, even at the relatively coarse spatial resolution of 20 km. Since three-dimensional effects depend on the spatial context of a given pixel in a nontrivial way, the spectral dimension of this problem may emerge as the starting point for future bias corrections.
Project description:The optical energy emitted by lightning flashes interacts with the surrounding cloud medium through scattering and absorption. The optical signals recorded by space-based lightning imagers describe a convolution of lightning flash energetics and radiative transfer effects in the intervening cloud layer. A thundercloud imaging technique is presented that characterizes cloud regions based on how they are illuminated by lightning. This technique models the spatial distribution of optical energy in radiant lightning pulses to determine whether and to what extent each illuminated cloud pixel behaves like a homogeneous planar cloud layer. A gridded product is constructed that differentiates flashes that illuminate convective cells from stratiform flashes with long horizontal channels and anvil flashes whose optical emissions reflect off of nearby cloud surfaces. Producing this imagery with a rolling 15-min window allows us to visualize changes in convection with a rapid (20 s) update cycle.
Project description:Single-photon counters are single-pixel binary devices that click upon the absorption of a photon but obscure its spectral information, whereas resolving the color of detected photons has been in critical demand for frontier astronomical observation, spectroscopic imaging and wavelength division multiplexed quantum communications. Current implementations of single-photon spectrometers either consist of bulky wavelength-scanning components or have limited detection channels, preventing parallel detection of broadband single photons with high spectral resolutions. Here, we present the first broadband chip-scale single-photon spectrometer covering both visible and infrared wavebands spanning from 600 nm to 2000 nm. The spectrometer integrates an on-chip dispersive echelle grating with a single-element propagating superconducting nanowire detector of ultraslow-velocity for mapping the dispersed photons with high spatial resolutions. The demonstrated on-chip single-photon spectrometer features small device footprint, high robustness with no moving parts and meanwhile offers more than 200 equivalent wavelength detection channels with further scalability.
Project description:Existing multispectral imagers mostly use available array sensors to separately measure 2D data slices in a 3D spatial-spectral data cube. Thus they suffer from low photon efficiency, limited spectrum range and high cost. To address these issues, we propose to conduct multispectral imaging using a single bucket detector, to take full advantage of its high sensitivity, wide spectrum range, low cost, small size and light weight. Technically, utilizing the detector's fast response, a scene's 3D spatial-spectral information is multiplexed into a dense 1D measurement sequence and then demultiplexed computationally under the single pixel imaging scheme. A proof-of-concept setup is built to capture multispectral data of 64 pixels?×?64 pixels?×?10 wavelength bands ranging from 450?nm to 650?nm, with the acquisition time being 1?minute. The imaging scheme holds great potentials for various low light and airborne applications, and can be easily manufactured as production-volume portable multispectral imagers.
Project description:The MODIS Level-2 cloud product (Earth Science Data Set names MOD06 and MYD06 for Terra and Aqua MODIS, respectively) provides pixel-level retrievals of cloud-top properties (day and night pressure, temperature, and height) and cloud optical properties (optical thickness, effective particle radius, and water path for both liquid water and ice cloud thermodynamic phases-daytime only). Collection 6 (C6) reprocessing of the product was completed in May 2014 and March 2015 for MODIS Aqua and Terra, respectively. Here we provide an overview of major C6 optical property algorithm changes relative to the previous Collection 5 (C5) product. Notable C6 optical and microphysical algorithm changes include: (i) new ice cloud optical property models and a more extensive cloud radiative transfer code lookup table (LUT) approach, (ii) improvement in the skill of the shortwave-derived cloud thermodynamic phase, (iii) separate cloud effective radius retrieval datasets for each spectral combination used in previous collections, (iv) separate retrievals for partly cloudy pixels and those associated with cloud edges, (v) failure metrics that provide diagnostic information for pixels having observations that fall outside the LUT solution space, and (vi) enhanced pixel-level retrieval uncertainty calculations. The C6 algorithm changes collectively can result in significant changes relative to C5, though the magnitude depends on the dataset and the pixel's retrieval location in the cloud parameter space. Example Level-2 granule and Level-3 gridded dataset differences between the two collections are shown. While the emphasis is on the suite of cloud optical property datasets, other MODIS cloud datasets are discussed when relevant.
Project description:Spectroscopic stimulated Raman scattering (SRS) imaging generates chemical maps of intrinsic molecules, with no need for prior knowledge. Despite great advances in instrumentation, the acquisition speed for a spectroscopic SRS image stack is fundamentally bounded by the pixel integration time. In this work, we report three-dimensional sparsely sampled spectroscopic SRS imaging that measures ~20% of pixels throughout the stack. In conjunction with related work in low-rank matrix completion (e.g., the Netflix Prize), we develop a regularized non-negative matrix factorization algorithm to decompose the sub-sampled image stack into spectral signatures and concentration maps. This design enables an acquisition speed of 0.8 s per image stack, with 50 frames in the spectral domain and 40,000 pixels in the spatial domain, which is faster than the conventional raster laser-scanning scheme by one order of magnitude. Such speed allows real-time metabolic imaging of living fungi suspended in a growth medium while effectively maintaining the spatial and spectral resolutions. This work is expected to promote broad application of matrix completion in spectroscopic laser-scanning imaging.
Project description:Satellite derived bathymetry (SDB) enables rapid mapping of large coastal areas through measurement of optical penetration of the water column. The resolution of bathymetric mapping and achievable horizontal and vertical accuracies vary but generally, all SDB outputs are constrained by sensor type, water quality and other environmental conditions. Efforts to improve accuracy include physics-based methods (similar to radiative transfer models e.g. for atmospheric/vegetation studies) or detailed in-situ sampling of the seabed and water column, but the spatial component of SDB measurements is often under-utilised in SDB workflows despite promising results suggesting potential to improve accuracy significantly. In this study, a selection of satellite datasets (Landsat 8, RapidEye and Pleiades) at different spatial and spectral resolutions were tested using a log ratio transform to derive bathymetry in an Atlantic coastal embayment. A series of non-spatial and spatial linear analyses were then conducted and their influence on SDB prediction accuracy was assessed in addition to the significance of each model's parameters. Landsat 8 (30?m pixel size) performed relatively weak with the non-spatial model, but showed the best results with the spatial model. However, the highest spatial resolution imagery used - Pleiades (2?m pixel size) showed good results across both non-spatial and spatial models which suggests a suitability for SDB prediction at a higher spatial resolution than the others. In all cases, the spatial models were able to constrain the prediction differences at increased water depths.
Project description:We used a deep-ultraviolet fluorescence mapping spectrometer, coupled to a drill system, to scan from the surface to 105?m depth into the Greenland ice sheet. The scan included firn and glacial ice and demonstrated that the instrument is able to determine small (mm) and large (cm) scale regions of organic matter concentration and discriminate spectral types of organic matter at high resolution. Both a linear point cloud scanning mode and a raster mapping mode were used to detect and localize microbial and organic matter "hotspots" embedded in the ice. Our instrument revealed diverse spectral signatures. Most hotspots were <20?mm in diameter, clearly isolated from other hotspots, and distributed stochastically; there was no evidence of layering in the ice at the fine scales examined (100??m per pixel). The spectral signatures were consistent with organic matter fluorescence from microbes, lignins, fused-ring aromatic molecules, including polycyclic aromatic hydrocarbons, and biologically derived materials such as fulvic acids. In situ detection of organic matter hotspots in ice prevents loss of spatial information and signal dilution when compared with traditional bulk analysis of ice core meltwaters. Our methodology could be useful for detecting microbial and organic hotspots in terrestrial icy environments and on future missions to the Ocean Worlds of our Solar System.
Project description:Spectroscopic single-molecule localization microscopy (sSMLM) was used to achieve simultaneous imaging and spectral analysis of single molecules for the first time. Current sSMLM fundamentally suffers from a reduced photon budget because the photons from individual stochastic emissions are divided into spatial and spectral channels. Therefore, both spatial localization and spectral analysis only use a portion of the total photons, leading to reduced precisions in both channels. To improve the spatial and spectral precisions, we present symmetrically dispersed sSMLM, or SDsSMLM, to fully utilize all photons from individual stochastic emissions in both spatial and spectral channels. SDsSMLM achieved 10-nm spatial and 0.8-nm spectral precisions at a total photon budget of 1000. Compared with the existing sSMLM using a 1:3 splitting ratio between spatial and spectral channels, SDsSMLM improved the spatial and spectral precisions by 42% and 10%, respectively, under the same photon budget. We also demonstrated multicolour imaging of fixed cells and three-dimensional single-particle tracking using SDsSMLM. SDsSMLM enables more precise spectroscopic single-molecule analysis in broader cell biology and material science applications.
Project description:Choroidal melanocytes (HCMs) are melanin-producing cells in the vascular uvea of the human eye (iris, ciliary body and choroid). These cranial neural crest-derived cells migrate to populate a mesodermal microenvironment, and display cellular functions and extracellular interactions that are biologically distinct to skin melanocytes. HCMs (and melanins) are important in normal human eye physiology with roles including photoprotection, regulation of oxidative damage and immune responses. To extend knowledge of cytoplasmic melanins and melanosomes in label-free HCMs, a non-invasive 'fit-free' approach, combining 2-photon excitation fluorescence lifetimes and emission spectral imaging with phasor plot segmentation was applied. Intracellular melanin-mapped FLIM phasors showed a linear distribution indicating that HCM melanins are a ratio of two fluorophores, eumelanin and pheomelanin. A quantitative histogram of HCM melanins was generated by identifying the image pixel fraction contributed by phasor clusters mapped to varying eumelanin/pheomelanin ratio. Eumelanin-enriched dark HCM regions mapped to phasors with shorter lifetimes and longer spectral emission (580-625?nm) and pheomelanin-enriched lighter pigmented HCM regions mapped to phasors with longer lifetimes and shorter spectral emission (550-585?nm). Overall, we demonstrated that these methods can identify and quantitatively profile the heterogeneous eumelanins/pheomelanins within in situ HCMs, and visualize melanosome spatial distributions, not previously reported for these cells.
Project description:Hyperspectral imaging was used to identify and to visualize the coffee bean varieties. Spectral preprocessing of pixel-wise spectra was conducted by different methods, including moving average smoothing (MA), wavelet transform (WT) and empirical mode decomposition (EMD). Meanwhile, spatial preprocessing of the gray-scale image at each wavelength was conducted by median filter (MF). Support vector machine (SVM) models using full sample average spectra and pixel-wise spectra, and the selected optimal wavelengths by second derivative spectra all achieved classification accuracy over 80%. Primarily, the SVM models using pixel-wise spectra were used to predict the sample average spectra, and these models obtained over 80% of the classification accuracy. Secondly, the SVM models using sample average spectra were used to predict pixel-wise spectra, but achieved with lower than 50% of classification accuracy. The results indicated that WT and EMD were suitable for pixel-wise spectra preprocessing. The use of pixel-wise spectra could extend the calibration set, and resulted in the good prediction results for pixel-wise spectra and sample average spectra. The overall results indicated the effectiveness of using spectral preprocessing and the adoption of pixel-wise spectra. The results provided an alternative way of data processing for applications of hyperspectral imaging in food industry.