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Advanced Hyper-Spectral Imaging, Sounding and Applications from Space

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

Deadline for manuscript submissions: closed (15 April 2020) | Viewed by 35815

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Special Issue Editors

Prof. Dr. Carmine Serio

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Guest Editor

School of Engineering, University of Basilicata, 85100 Potenza, Italy
Interests: satellite remote sensing of surface and atmospheric parameters; land 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
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Dr. Claude Camy-Peyret

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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:

Meteorology and clouds
Climate and green-house gases
Air quality
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

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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

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Published Papers (10 papers)

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Research

30 pages, 19295 KiB

Open AccessArticle

Optimal Interpolation for Infrared Products from Hyperspectral Satellite Imagers and Sounders

by Italia De Feis, Guido Masiello and Angela Cersosimo

Sensors 2020, 20(8), 2352; https://doi.org/10.3390/s20082352 - 21 Apr 2020

Cited by 11 | Viewed by 3202

Abstract

Thermal infrared remote sensing measurements have greatly improved in terms of spectral, spatial, and temporal resolution. These improvements are producing a clearer picture of the land surface and Earth atmospheric composition than ever before. Nevertheless, the analysis of this big quantity of data presents important challenges due to incomplete temporal and spatial recorded information. The aim of the present paper is to discuss a methodology to retrieve missing values of some interesting geophysical variables on a spatial field retrieved from spatially scattered infrared satellite observations in order to yield level 3, regularly gridded, data. The technique is based on a 2-Dimensional (2D) Optimal Interpolation (OI) scheme and is derived from the broad class of Kalman filter or Bayesian estimation theory. The goodness of the approach has been tested on 15-min temporal resolution Spinning Enhanced Visible and Infrared Imager (SEVIRI) emissivity and surface temperature (ST) products over South Italy (land and sea), on Infrared Atmospheric Sounding Interferometer (IASI) atmospheric ammonia (

N H_{3}

) concentration over North Italy and carbon monoxide (

C O

), sulfur dioxide (

S O_{2}

) and

N H_{3}

concentrations over China. All these gases affect air quality. Moreover, sea surface temperature (SST) retrievals have been compared with gridded data from MODIS (Moderate-resolution Imaging Spectroradiometer) observations. For gases concentration we have considered data from 3 different emission inventories, that is, Emissions Database for Global Atmospheric Research v3.4.2 (EDGARv3.4.2), the Regional Emission inventory in ASiav3.1 (REASv3.1) and MarcoPolov0.1, plus an independent study. The results show the efficacy of the proposed strategy to better capture the daily cycle for surface parameters and to detect hotspots of severe emissions from gas sources affecting air quality such as

C O

and

N H_{3}

and, therefore, to yield valuable information on the variability of gas concentration to complete ground stations measurements. Full article

(This article belongs to the Special Issue Advanced Hyper-Spectral Imaging, Sounding and Applications from Space)

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24 pages, 20016 KiB

Open AccessArticle

Cloud Detection: An Assessment Study from the ESA Round Robin Exercise for PROBA-V

by Umberto Amato, Anestis Antoniadis and Maria Francesca Carfora

Sensors 2020, 20(7), 2090; https://doi.org/10.3390/s20072090 - 8 Apr 2020

Cited by 1 | Viewed by 2677

Abstract

A Round Robin exercise was implemented by ESA to compare different classification methods in detecting clouds from images taken by the PROBA-V sensor. A high-quality dataset of 1350 reflectances and Clear/Cloudy corresponding labels had been prepared by ESA in the framework of the exercise. Motivated by both the experience acquired by one of the authors in this exercise and the availability of such a reliable annotated dataset, we present a full assessment of the methodology proposed therein. Our objective is also to investigate specific issues related to cloud detection when remotely sensed images comprise only a few spectral bands in the visible and near-infrared. For this purpose, we consider a bunch of well-known classification methods. First, we demonstrate the feasibility of using a training dataset semi-automatically obtained from other accurate algorithms. In addition, we investigate the effect of ancillary information, e.g., surface type or climate, on accuracy. Then we compare the different classification methods using the same training dataset under different configurations. We also perform a consensus analysis aimed at estimating the degree of mutual agreement among classification methods in detecting Clear or Cloudy sky conditions. Full article

(This article belongs to the Special Issue Advanced Hyper-Spectral Imaging, Sounding and Applications from Space)

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19 pages, 3399 KiB

Open AccessArticle

Characterization of the Observational Covariance Matrix of Hyper-Spectral Infrared Satellite Sensors Directly from Measured Earth Views

by Carmine Serio, Guido Masiello, Pietro Mastro and David C. Tobin

Sensors 2020, 20(5), 1492; https://doi.org/10.3390/s20051492 - 9 Mar 2020

Cited by 10 | Viewed by 3286

Abstract

The observational covariance matrix, whose diagonal square root is currently named radiometric noise, is one of the most important elements to characterize a given instrument. It determines the precision of measurements and their possible spectral inter-correlation. The characterization of this matrix is currently performed with blackbody targets of known temperature and is, therefore, an output of the calibration unit of the instrument system. We developed a methodology that can estimate the observational covariance matrix directly from calibrated Earth-scene observations. The technique can complement the usual analysis based on onboard blackbody calibration and is, therefore, a useful back up to check the overall quality of the calibration unit. The methodology was exemplified by application to three satellite Fourier transform spectrometers: IASI (Infrared Atmospheric Sounder Interferometer), CrIS (Cross-Track Infrared Sounder), and HIRAS (Hyperspectral Infrared Atmospheric Sounder). It was shown that these three instruments are working as expected based on the pre-flight and in-flight characterization of the radiometric noise. However, for all instruments, the analysis of the covariance matrix reveals extra correlation among channels, especially in the short wave spectral regions. Full article

(This article belongs to the Special Issue Advanced Hyper-Spectral Imaging, Sounding and Applications from Space)

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27 pages, 7988 KiB

Open AccessArticle

On the Use of Weighted Least-Squares Approaches for Differential Interferometric SAR Analyses: The Weighted Adaptive Variable-lEngth (WAVE) Technique

by Francesco Falabella, Carmine Serio, Giovanni Zeni and Antonio Pepe

Sensors 2020, 20(4), 1103; https://doi.org/10.3390/s20041103 - 18 Feb 2020

Cited by 12 | Viewed by 3224

Abstract

This paper concentrates on the study of the Weighted Least-squares (WLS) approaches for the generation of ground displacement time-series through Differential Interferometric SAR (DInSAR) methods. Usually, within the DInSAR framework, the Weighted Least-squares (WLS) techniques have principally been applied for improving the performance of the phase unwrapping operations as well as for better conveying the inversion of sequences of unwrapped interferograms to generate ground displacement maps. In both cases, the identification of low-coherent areas, where the standard deviation of the phase is high, is requested. In this paper, a WLS method that extends the usability of the Multi-Temporal InSAR (MT-InSAR) Small Baseline Subset (SBAS) algorithm in regions with medium-to-low coherence is presented. In particular, the proposed method relies on the adaptive selection and exploitation, pixel-by-pixel, of the medium-to-high coherent interferograms, only, so as to discard the noisy phase measurements. The selected interferometric phase values are then inverted by solving a WLS optimization problem. Noteworthy, the adopted, pixel-dependent selection of the “good” interferograms to be inverted may lead the available SAR data to be grouped into several disjointed subsets, which are then connected, exploiting the Weighted Singular Value Decomposition (WSVD) method. However, in some critical noisy regions, it may also happen that discarding of the incoherent interferograms may lead to rejecting some SAR acquisitions from the generated ground displacement time-series, at the cost of the reduced temporal sampling of the data measurements. Thus, variable-length ground displacement time-series are generated. The mathematical framework of the developed technique, which is named Weighted Adaptive Variable-lEngth (WAVE), is detailed in the manuscript. The presented experiments have been carried out by applying the WAVE technique to a SAR dataset acquired by the COSMO-SkyMed (CSK) sensors over the Basilicata region, Southern Italy. A cross-comparison analysis between the conventional and the WAVE method has also been provided. Full article

(This article belongs to the Special Issue Advanced Hyper-Spectral Imaging, Sounding and Applications from Space)

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18 pages, 3864 KiB

Open AccessArticle

Spectral Representation via Data-Guided Sparsity for Hyperspectral Image Super-Resolution

by Xian-Hua Han, Yongqing Sun, Jian Wang, Boxin Shi, Yinqiang Zheng and Yen-Wei Chen

Sensors 2019, 19(24), 5401; https://doi.org/10.3390/s19245401 - 7 Dec 2019

Cited by 2 | Viewed by 2805

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 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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12 pages, 3550 KiB

Open AccessArticle

Preflight Spectral Calibration of Airborne Shortwave Infrared Hyperspectral Imager with Water Vapor Absorption Characteristics

by Honglin Liu, Dong Zhang and Yueming Wang

Sensors 2019, 19(10), 2259; https://doi.org/10.3390/s19102259 - 16 May 2019

Cited by 8 | Viewed by 3597

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 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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12 pages, 5307 KiB

Open AccessArticle

Dust Aerosol Detection by the Modified CO₂ Slicing Method

by Yu Someya, Ryoichi Imasu and Kei Shiomi

Sensors 2019, 19(7), 1615; https://doi.org/10.3390/s19071615 - 4 Apr 2019

Cited by 5 | Viewed by 3190

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 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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18 pages, 2478 KiB

Open AccessArticle

SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval

by Guido Masiello, Carmine Serio, Sara Venafra, Laurent Poutier and Frank-M. Göttsche

Sensors 2019, 19(7), 1532; https://doi.org/10.3390/s19071532 - 29 Mar 2019

Cited by 9 | Viewed by 4574

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 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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23 pages, 8312 KiB

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

by Chisa Iwasaki, Ryoichi Imasu, Andrey Bril, Sergey Oshchepkov, Yukio Yoshida, Tatsuya Yokota, Vyacheslav Zakharov, Konstantin Gribanov and Nikita Rokotyan

Sensors 2019, 19(5), 1262; https://doi.org/10.3390/s19051262 - 12 Mar 2019

Cited by 3 | Viewed by 4554

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 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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17 pages, 9386 KiB

Open AccessArticle

A Prediction-Based Spatial-Spectral Adaptive Hyperspectral Compressive Sensing Algorithm

by Ping Xu, Bingqiang Chen, Lingyun Xue, Jingcheng Zhang and Lei Zhu

Sensors 2018, 18(10), 3289; https://doi.org/10.3390/s18103289 - 30 Sep 2018

Cited by 11 | Viewed by 3750

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, 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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