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Special Issue "Advanced Hyper-Spectral Imaging, Sounding and Applications from Space"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors, Control, and Telemetry".

Deadline for manuscript submissions: 29 February 2020.

Special Issue Editors

Prof. Carmine Serio
E-Mail Website
Guest Editor
School of Engineering, University of Basilicata, 85100 Potenza, Italy
Interests: satellite remote sensing of surface and atmospheric parameters; surface change detection; radiative transfer in cloudy and clear atmosphere; Fourier spectroscopy applied to remote sensing of atmosphere; satellite instruments characterization; climate; global warming and change; inverse problems and dimensionality reduction of data space; satellite retrieval of atmospheric constituents and aerosols; greenhouse gases; air quality
Dr. Claude Camy-Peyret
E-Mail Website
Guest Editor
Institut Pierre-Simon Laplace, UPMC/UVSQ, Paris, France
Interests: infrared molecular spectroscopy; satellite remote sensing; radiative transfer in the infrared; inverse methods for retrieving greenhouse gas concentrations; Fourier transform spectroscopy; infrared instrumentation
Prof. Ryoichi Imasu
E-Mail Website
Guest Editor
Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Japan
Interests: atmospheric physics; greenhouse gas measurement using satellite sensors; retrieval analysis of atmospheric states; source/sink inversion analysis of greenhouse gases; ground-based remote sensing; Fourier transform spectroscopy

Special Issue Information

Dear Colleagues,

Hyper-spectral imagers and sounders have widespread applications in Earth sciences and have become an indispensable tool for the diagnosis and monitoring of natural and anthropogenic environments or ecosystems.

Although ground monitoring networks have regularly expanded over the last 50 years, allowing an assessment of the Earth’s environment on a global scale, they do not provide the necessary spatial and temporal resolutions for a quantitative analysis of the full atmospheric state and of surface processes.

Satellite missions based on hyper-spectral technology will expand in the next few years and will cover a broad range of applications, from meteorology to precision agriculture. Hyper-spectral instrumentation is expected to improve horizontal and vertical resolutions, as well as time sampling of satellite soundings. However, hyper-spectral sounders are enhancing the problem of the massive size of data to be transmitted, processed and stored. New algorithms are expected to be developed in order to address this big data issue, but the processing and the production of level 1 (spectra) and level 2 (geophysical) products should not occur at the expense of using at best the full information content of high spectral/spatial resolution observations.

In this context, the main goal of the present special issue is to provide an update on present (IASI, AIRS, CrIs, GOSAT, GCOM-C, OCO-2, OMI, FY-3D/HIRAS, FY-4A, TANSAT, MODIS, SEVIRI to name a few), and upcoming satellite missions (e.g., MTG-IRS, IASI-NG, MicroCarb, GeoCARB, Sentinel programs) with an emphasis on hyper-spectral instrumentation, performance characterization, development of algorithms for level 1 and 2 data processing, inter-comparison/validation, and applications in specific fields, such as:

  1. Meteorology and clouds
  2. Climate and green-house gases
  3. Air quality
  4. Remote sensing of surface properties.

Research papers on these aspects and topics are solicited, which should address the improvements expected from hyper-spectral technologies. Studies dealing with state-of-the-art and new concept methodologies/strategies to fully exploit information content are welcome.

Prof. Carmine Serio
Dr. Claude Camy-Peyret
Prof. Ryoichi Imasu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Hyperspectral imaging
  • Hyperspectral sounding
  • Instrument characterization and calibration
  • Radiative transfer
  • Forward modelling
  • Satellite remote sensing
  • Meteorology and cloud physics
  • Atmospheric chemistry
  • Climate and greenhouse gases
  • Air quality
  • Surface properties
  • Inverse problems in the context of remote sensing
  • Dimensionality reduction
  • Big data challenge in remote sensing

Published Papers (6 papers)

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Research

Open AccessArticle
Spectral Representation via Data-Guided Sparsity for Hyperspectral Image Super-Resolution
Sensors 2019, 19(24), 5401; https://doi.org/10.3390/s19245401 - 07 Dec 2019
Abstract
Hyperspectral imaging is capable of acquiring the rich spectral information of scenes and has great potential for understanding the characteristics of different materials in many applications ranging from remote sensing to medical imaging. However, due to hardware limitations, the existed hyper-/multi-spectral imaging devices [...] Read more.
Hyperspectral imaging is capable of acquiring the rich spectral information of scenes and has great potential for understanding the characteristics of different materials in many applications ranging from remote sensing to medical imaging. However, due to hardware limitations, the existed hyper-/multi-spectral imaging devices usually cannot obtain high spatial resolution. This study aims to generate a high resolution hyperspectral image according to the available low resolution hyperspectral and high resolution RGB images. We propose a novel hyperspectral image superresolution method via non-negative sparse representation of reflectance spectra with a data guided sparsity constraint. The proposed method firstly learns the hyperspectral dictionary from the low resolution hyperspectral image and then transforms it into the RGB one with the camera response function, which is decided by the physical property of the RGB imaging camera. Given the RGB vector and the RGB dictionary, the sparse representation of each pixel in the high resolution image is calculated with the guidance of a sparsity map, which measures pixel material purity. The sparsity map is generated by analyzing the local content similarity of a focused pixel in the available high resolution RGB image and quantifying the spectral mixing degree motivated by the fact that the pixel spectrum of a pure material should have sparse representation of the spectral dictionary. Since the proposed method adaptively adjusts the sparsity in the spectral representation based on the local content of the available high resolution RGB image, it can produce more robust spectral representation for recovering the target high resolution hyperspectral image. Comprehensive experiments on two public hyperspectral datasets and three real remote sensing images validate that the proposed method achieves promising performances compared to the existing state-of-the-art methods. Full article
(This article belongs to the Special Issue Advanced Hyper-Spectral Imaging, Sounding and Applications from Space)
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Open AccessArticle
Preflight Spectral Calibration of Airborne Shortwave Infrared Hyperspectral Imager with Water Vapor Absorption Characteristics
Sensors 2019, 19(10), 2259; https://doi.org/10.3390/s19102259 - 16 May 2019
Cited by 1
Abstract
Due to the strong absorption of water vapor at wavelengths of 1350–1420 nm and 1820–1940 nm, under normal atmospheric conditions, the actual digital number (DN) response curve of a hyperspectral imager deviates from the Gaussian shape, which leads to a decrease in the [...] Read more.
Due to the strong absorption of water vapor at wavelengths of 1350–1420 nm and 1820–1940 nm, under normal atmospheric conditions, the actual digital number (DN) response curve of a hyperspectral imager deviates from the Gaussian shape, which leads to a decrease in the calibration accuracy of an instrument’s spectral response functions (SRF). The higher the calibration uncertainty of SRF, the worse the retrieval accuracy of the spectral characteristics of the targets. In this paper, an improved spectral calibration method based on a monochromator and the spectral absorptive characteristics of water vapor in the laboratory is presented. The water vapor spectral calibration method (WVSCM) uses the difference function to calculate the intrinsic DN response functions of the spectral channels located in the absorptive wavelength range of water vapor and corrects the wavelength offset of the monochromator via the least-square procedure to achieve spectral calibration throughout the full spectral responsive range of the hyper-spectrometer. The absolute spectral calibration uncertainty is ±0.125 nm. We validated the effectiveness of the WVSCM with two tunable semiconductor lasers, and the spectral wavelength positions calibrated by lasers and the WVSCM showed a good degree of consistency. Full article
(This article belongs to the Special Issue Advanced Hyper-Spectral Imaging, Sounding and Applications from Space)
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Open AccessArticle
Dust Aerosol Detection by the Modified CO2 Slicing Method
Sensors 2019, 19(7), 1615; https://doi.org/10.3390/s19071615 - 04 Apr 2019
Cited by 1
Abstract
Dust aerosols, which have diverse and strong influences on the environment, must be monitored. Satellite data are effective for monitoring atmospheric conditions globally. In this work, the modified CO2 slicing method, a cloud detection technique using thermal infrared data from space, was [...] Read more.
Dust aerosols, which have diverse and strong influences on the environment, must be monitored. Satellite data are effective for monitoring atmospheric conditions globally. In this work, the modified CO2 slicing method, a cloud detection technique using thermal infrared data from space, was applied to GOSAT data to detect the dust aerosol layer height. The results were compared using lidar measurements. Comparison of horizontal distributions found for northern Africa during summer revealed that both the relative frequencies of the low level aerosol layer from the slicing method and the dust frequencies of CALIPSO are high in northern coastal areas. Comparisons of detected layer top heights using collocated data with CALIPSO and ground-based lidar consistently showed high detection frequencies of the lower level aerosol layer, although the slicing method sometimes produces overestimates. This tendency is significant over land. The main causes of this tendency might be uncertainty of the surface skin temperature and a temperature inversion layer in the atmosphere. The results revealed that obtaining the detailed behavior of dust aerosols using the modified slicing method alone is difficult. Full article
(This article belongs to the Special Issue Advanced Hyper-Spectral Imaging, Sounding and Applications from Space)
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Open AccessArticle
SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval
Sensors 2019, 19(7), 1532; https://doi.org/10.3390/s19071532 - 29 Mar 2019
Cited by 1
Abstract
Timely processing of observations from multi-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to real [...] Read more.
Timely processing of observations from multi-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to real time processing of infrared radiances. The new radiative transfer model improves computational time by a factor of ≈7 compared to the previous versions and makes it possible to process SEVIRI data at nearly real time. The new forward model has been applied for the retrieval of surface parameters. Although the scheme can be applied for the simultaneous retrieval of temperature and emissivity, the paper mostly focuses on emissivity. The inverse scheme relies on a Kalman filter approach, which allows us to exploit a sequential processing of SEVIRI observations. Based on the new forward model, the paper also presents a validation retrieval performed with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. Furthermore, a comparison with IASI (Infrared Atmospheric Sounder Interferometer) emissivity retrievals has been performed as well. It has been found that the retrieved emissivities are in good agreement with each other and with in situ observations, i.e., average differences are generally well below 0.01. Full article
(This article belongs to the Special Issue Advanced Hyper-Spectral Imaging, Sounding and Applications from Space)
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Open AccessArticle
Optimization of the Photon Path Length Probability Density Function-Simultaneous (PPDF-S) Method and Evaluation of CO2 Retrieval Performance Under Dense Aerosol Conditions
Sensors 2019, 19(5), 1262; https://doi.org/10.3390/s19051262 - 12 Mar 2019
Cited by 1
Abstract
The photon path length probability density function-simultaneous (PPDF-S) algorithm is effective for retrieving column-averaged concentrations of carbon dioxide (XCO2) and methane (XCH4) from Greenhouse gases Observing Satellite (GOSAT) spectra in Short Wavelength InfraRed (SWIR). Using this method, light-path modification [...] Read more.
The photon path length probability density function-simultaneous (PPDF-S) algorithm is effective for retrieving column-averaged concentrations of carbon dioxide (XCO2) and methane (XCH4) from Greenhouse gases Observing Satellite (GOSAT) spectra in Short Wavelength InfraRed (SWIR). Using this method, light-path modification attributable to light reflection/scattering by atmospheric clouds/aerosols is represented by the modification of atmospheric transmittance according to PPDF parameters. We optimized PPDF parameters for a more accurate XCO2 retrieval under aerosol dense conditions based on simulation studies for various aerosol types and surface albedos. We found a more appropriate value of PPDF parameters referring to the vertical profile of CO2 concentration as a measure of a stable solution. The results show that the constraint condition of a PPDF parameter that represents the light reflectance effect by aerosols is sufficiently weak to affect XCO2 adversely. By optimizing the constraint, it was possible to obtain a stable solution of XCO2. The new optimization was applied to retrieval analysis of the GOSAT data measured in Western Siberia. First, we assumed clear sky conditions and retrieved XCO2 from GOSAT data obtained near Yekaterinburg in the target area. The retrieved XCO2 was validated through a comparison with ground-based Fourier Transform Spectrometer (FTS) measurements made at the Yekaterinburg observation site. The validation results showed that the retrieval accuracy was reasonable. Next, we applied the optimized method to dense aerosol conditions when biomass burning was active. The results demonstrated that optimization enabled retrieval, even under smoky conditions, and that the total number of retrieved data increased by about 70%. Furthermore, the results of the simulation studies and the GOSAT data analysis suggest that atmospheric aerosol types that affected CO2 analysis are identifiable by the PPDF parameter value. We expect that we will be able to suggest a further improved algorithm after the atmospheric aerosol types are identified. Full article
(This article belongs to the Special Issue Advanced Hyper-Spectral Imaging, Sounding and Applications from Space)
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Open AccessArticle
A Prediction-Based Spatial-Spectral Adaptive Hyperspectral Compressive Sensing Algorithm
Sensors 2018, 18(10), 3289; https://doi.org/10.3390/s18103289 - 30 Sep 2018
Abstract
In order to improve the performance of storage and transmission of massive hyperspectral data, a prediction-based spatial-spectral adaptive hyperspectral compressive sensing (PSSAHCS) algorithm is proposed. Firstly, the spatial block size of hyperspectral images is adaptively obtained according to the spatial self-correlation coefficient. Secondly, [...] Read more.
In order to improve the performance of storage and transmission of massive hyperspectral data, a prediction-based spatial-spectral adaptive hyperspectral compressive sensing (PSSAHCS) algorithm is proposed. Firstly, the spatial block size of hyperspectral images is adaptively obtained according to the spatial self-correlation coefficient. Secondly, a k-means clustering algorithm is used to group the hyperspectral images. Thirdly, we use a local means and local standard deviations (LMLSD) algorithm to find the optimal image in the group as the key band, and the non-key bands in the group can be smoothed by linear prediction. Fourthly, the random Gaussian measurement matrix is used as the sampling matrix, and the discrete cosine transform (DCT) matrix serves as the sparse basis. Finally, the stagewise orthogonal matching pursuit (StOMP) is used to reconstruct the hyperspectral images. The experimental results show that the proposed PSSAHCS algorithm can achieve better evaluation results—the subjective evaluation, the peak signal-to-noise ratio, and the spatial autocorrelation coefficient in the spatial domain, and spectral curve comparison and correlation between spectra-reconstructed performance in the spectral domain—than those of single spectral compression sensing (SSCS), block hyperspectral compressive sensing (BHCS), and adaptive grouping distributed compressive sensing (AGDCS). PSSAHCS can not only compress and reconstruct hyperspectral images effectively, but also has strong denoise performance. Full article
(This article belongs to the Special Issue Advanced Hyper-Spectral Imaging, Sounding and Applications from Space)
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