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Hyperspectral Remote Sensing of the Earth
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Hyperspectral Remote Sensing of the Earth

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

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 24175

Special Issue Editor

Prof. Dr. Eyal Ben-Dor

E-Mail Website
Guest Editor
The Remote Sensing Laboratory, Tel-Aviv University, Tel-Aviv, Israel
Interests: soil spectroscopy; hyperspectral remote sensing; remote sensing of the environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hyperspectral remote sensing (HRS), or imaging spectroscopy (IS), has become a very popular technology since NASA’s first HRS sensor (AIS) in 1983 proved its remarkable capability to distinguish between several minerals from airborne domains. Today's sensors are easy to use, come in many sizes (grams to kilograms), and suit many platforms, from UAVs to satellites. Thus, many users are exploiting HRS information in many disciplines, ranging from basic science to commercial applications. In general, the significant advantage of this technology is that it provides spatial and spectral information simultaneously, while improving our understanding of the remote environment in general, and the Earth in particular. HRS technology is well accepted in the remote-sensing arena as an innovative tool for many applications, in geology, ecology, pedology, limnology, and atmospheric sciences, among others, especially in cases where other remote-sensing means have failed or cannot obtain additional information. While the development of these innovative approaches has taken place for the last 15 years, mostly by scientists, the power of HRS/IR technology is now being implemented, slowly but surely, for many potential end-users, such as decision-makers, farmers, environment watchers in both the private and governmental sectors, city planners, stockholders, and others. The HRS discipline is currently very active—commercial sensors are being built and sold, orbital sensors are in advanced planning phases by the leading space agencies, people are becoming more educated on the topic, national and international funds are being directed to studying and using this technology, and interest from the private sector is on the rise. The aim of this Special Issue is to gather all types of papers dealing with HRS technology dedicated to Earth sensing from any distance, platform, or spectral region, while covering new, original, and innovative topics.

 

Topics include, but are not limited to, the following:


  • Orbital HRS sensors
  • Performance of ground HRS sensors
  • Atmosphere applications, including new techniques for atmospheric correction
  • Soil spectral analyses and spatial mapping
  • Vegetation and forest applications: phenology and chemical monitoring
  • Raw material mapping based on spectral information
  • Use of spectral libraries to refine HRS data
  • UAV platforms and sensors
  • HRS data simulation
  • Novel sensing materials and principles
  • Inland water monitoring
  • Applications for sustainable agriculture: indoor and outdoor sensors
  • Longwave and midwave infrared HRS sensors and applications
  • CubeSat and HRS sensors
  • Data mining approach for HRS data
  • Standards and protocols for HRS data acquisition
  • Assessing radiometric and spectral uncertainties of HRS data
  • Direct and vicarious calibration practices of HRS sensors

Submitted articles should not have been previously published or be currently under review by other journals or conferences/symposia/workshops. Papers previously published as part of conference/workshop proceedings can be considered for publication in the Special Issue, provided that they are modified to contain at least 40% new content. The authors of such submissions must clearly indicate how the journal version of their paper has been extended, in a separate letter to the guest editors, at the time of submission. Moreover, authors must acknowledge their previous paper in the manuscript and resolve any potential copyright issues prior to submission.

We are looking forward to your exciting papers!

Prof. Eyal Ben-Dor
Guest Editor

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 submissions that pass pre-check are 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 2600 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 Remote Sensing
  • Spectral Imaging
  • Earth Surface
  • Sensors
  • Applications

Published Papers (6 papers)

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20 pages, 4123 KiB  
Article
A Lightweight 1-D Convolution Augmented Transformer with Metric Learning for Hyperspectral Image Classification
by Xiang Hu, Wenjing Yang, Hao Wen, Yu Liu and Yuanxi Peng
Sensors 2021, 21(5), 1751; https://doi.org/10.3390/s21051751 - 03 Mar 2021
Cited by 25 | Viewed by 3862
Abstract
Hyperspectral image (HSI) classification is the subject of intense research in remote sensing. The tremendous success of deep learning in computer vision has recently sparked the interest in applying deep learning in hyperspectral image classification. However, most deep learning methods for hyperspectral image [...] Read more.
Hyperspectral image (HSI) classification is the subject of intense research in remote sensing. The tremendous success of deep learning in computer vision has recently sparked the interest in applying deep learning in hyperspectral image classification. However, most deep learning methods for hyperspectral image classification are based on convolutional neural networks (CNN). Those methods require heavy GPU memory resources and run time. Recently, another deep learning model, the transformer, has been applied for image recognition, and the study result demonstrates the great potential of the transformer network for computer vision tasks. In this paper, we propose a model for hyperspectral image classification based on the transformer, which is widely used in natural language processing. Besides, we believe we are the first to combine the metric learning and the transformer model in hyperspectral image classification. Moreover, to improve the model classification performance when the available training samples are limited, we use the 1-D convolution and Mish activation function. The experimental results on three widely used hyperspectral image data sets demonstrate the proposed model’s advantages in accuracy, GPU memory cost, and running time. Full article
(This article belongs to the Special Issue Hyperspectral Remote Sensing of the Earth)
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20 pages, 1491 KiB  
Article
Retrieval of Hyperspectral Information from Multispectral Data for Perennial Ryegrass Biomass Estimation
by Gustavo Togeiro de Alckmin, Lammert Kooistra, Richard Rawnsley, Sytze de Bruin and Arko Lucieer
Sensors 2020, 20(24), 7192; https://doi.org/10.3390/s20247192 - 15 Dec 2020
Cited by 4 | Viewed by 2172
Abstract
The use of spectral data is seen as a fast and non-destructive method capable of monitoring pasture biomass. Although there is great potential in this technique, both end users and sensor manufacturers are uncertain about the necessary sensor specifications and achievable accuracies in [...] Read more.
The use of spectral data is seen as a fast and non-destructive method capable of monitoring pasture biomass. Although there is great potential in this technique, both end users and sensor manufacturers are uncertain about the necessary sensor specifications and achievable accuracies in an operational scenario. This study presents a straightforward parametric method able to accurately retrieve the hyperspectral signature of perennial ryegrass (Lolium perenne) canopies from multispectral data collected within a two-year period in Australia and the Netherlands. The retrieved hyperspectral data were employed to generate optimal indices and continuum-removed spectral features available in the scientific literature. For performance comparison, both these simulated features and a set of currently employed vegetation indices, derived from the original band values, were used as inputs in a random forest algorithm and accuracies of both methods were compared. Our results have shown that both sets of features present similar accuracies (root mean square error (RMSE) ≈490 and 620 kg DM/ha) when assessed in cross-validation and spatial cross-validation, respectively. These results suggest that for pasture biomass retrieval solely from top-of-canopy reflectance (ranging from 550 to 790 nm), better performing methods do not rely on the use of hyperspectral or, yet, in a larger number of bands than those already available in current sensors. Full article
(This article belongs to the Special Issue Hyperspectral Remote Sensing of the Earth)
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19 pages, 3251 KiB  
Article
A Hyperspectral Bidirectional Reflectance Model for Land Surface
by Qiguang Yang, Xu Liu and Wan Wu
Sensors 2020, 20(16), 4456; https://doi.org/10.3390/s20164456 - 10 Aug 2020
Cited by 9 | Viewed by 3827
Abstract
A hyperspectral bidirectional reflectance (HSBR) model for land surface has been developed in this work. The HSBR model includes a very diverse land surface bidirectional reflectance distribution function (BRDF) database with ~40,000 spectra. The BRDF database is saved as Ross-Li parameters, which can [...] Read more.
A hyperspectral bidirectional reflectance (HSBR) model for land surface has been developed in this work. The HSBR model includes a very diverse land surface bidirectional reflectance distribution function (BRDF) database with ~40,000 spectra. The BRDF database is saved as Ross-Li parameters, which can generate hyperspectral reflectance spectra at different sensor and solar observation geometries. The HSBR model also provides an improved method for generating hyperspectral surface reflectance using multiband satellite measurements. It is shown that the land surface reflective spectrum can be easily simulated using BRDF parameters or reflectance at few preselected wavelengths. The HSBR model is validated using the U.S. Geological Survey (USGS) vegetation database and the AVIRIS reflectance product. The simulated reflective spectra fit the measurements very well with standard deviations normally smaller than 0.01 in the unit of reflectivity. The HSBR model could be used to significantly improve the quality of the reflectance products of satellite and airborne sensors. It also plays important role for intercalibration among space-based instruments and other land surface related applications. Full article
(This article belongs to the Special Issue Hyperspectral Remote Sensing of the Earth)
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23 pages, 8013 KiB  
Article
Temperature and Emissivity Inversion Accuracy of Spectral Parameter Changes and Noise of Hyperspectral Thermal Infrared Imaging Spectrometers
by Honglan Shao, Chengyu Liu, Chunlai Li, Jianyu Wang and Feng Xie
Sensors 2020, 20(7), 2109; https://doi.org/10.3390/s20072109 - 08 Apr 2020
Cited by 8 | Viewed by 2265
Abstract
The emergence of hyperspectral thermal infrared imaging spectrometers makes it possible to retrieve both the land surface temperature (LST) and the land surface emissivity (LSE) simultaneously. However, few articles focus on the problem of how the instrument’s spectral parameters and instrument noise level [...] Read more.
The emergence of hyperspectral thermal infrared imaging spectrometers makes it possible to retrieve both the land surface temperature (LST) and the land surface emissivity (LSE) simultaneously. However, few articles focus on the problem of how the instrument’s spectral parameters and instrument noise level affect the LST and LSE inversion errors. In terms of instrument development, this article simulated three groups of hyperspectral thermal infrared data with three common spectral parameters and each group of data includes tens of millions of simulated radiances of 1525 emissivity curves with 17 center wavelength shift ratios, 6 full width at half maximum (FWHM) change ratios and 6 noise equivalent differential temperatures (NEDTs) under 15 atmospheric conditions with 6 object temperatures, inverted them by two temperature and emissivity separation methods (ISSTES and ARTEMISS), and analyzed quantitatively the effects of the spectral parameters change and noise of an instrument on the LST and LSE inversion errors. The results show that: (1) center wavelength shifts and noise affect the inversion errors strongly, while FWHM changes affect them weakly; (2) the LST and LSE inversion errors increase with the center wavelength shift ratio in a quadratic function and increase with FWHM change ratio slowly and linearly for both the inversion methods, however they increase with NEDT in an S-curve for ISSTES while they increase with NEDT slightly and linearly for ARTEMISS. During the design and development of a hyperspectral thermal infrared instrument, it is highly recommended to keep the potential center wavelength shift within 1 band and keep NEDT within 0.1K (corresponding LST error < 1K and LSE error < 0.015) for normal applications and within 0.03K (corresponding LST error < 0.5K and LSE error < 0.01) for better application effect and level. Full article
(This article belongs to the Special Issue Hyperspectral Remote Sensing of the Earth)
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21 pages, 7934 KiB  
Article
The Solar-Induced Chlorophyll Fluorescence Imaging Spectrometer (SIFIS) Onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1): Specifications and Prospects
by Shanshan Du, Liangyun Liu, Xinjie Liu, Xinwei Zhang, Xianlian Gao and Weigang Wang
Sensors 2020, 20(3), 815; https://doi.org/10.3390/s20030815 - 03 Feb 2020
Cited by 15 | Viewed by 3964
Abstract
The global monitoring of solar-induced chlorophyll fluorescence (SIF) using satellite-based observations provides a new way of monitoring the status of terrestrial vegetation photosynthesis on a global scale. Several global SIF products that make use of atmospheric satellite data have been successfully developed in [...] Read more.
The global monitoring of solar-induced chlorophyll fluorescence (SIF) using satellite-based observations provides a new way of monitoring the status of terrestrial vegetation photosynthesis on a global scale. Several global SIF products that make use of atmospheric satellite data have been successfully developed in recent decades. The Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1), the first Chinese terrestrial ecosystem carbon inventory satellite, which is due to be launched in 2021, will carry an imaging spectrometer specifically designed for SIF monitoring. Here, we use an extensive set of simulated data derived from the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) and Soil Canopy Observation Photosynthesis and Energy (SCOPE) models to evaluate and optimize the specifications of the SIF Imaging Spectrometer (SIFIS) onboard TECIS for accurate SIF retrievals. The wide spectral range of 670−780 nm was recommended to obtain the SIF at both the red and far-red bands. The results illustrate that the combination of a spectral resolution (SR) of 0.1 nm and a signal-to-noise ratio (SNR) of 127 performs better than an SR of 0.3 nm and SNR of 322 or an SR of 0.5 nm and SNR of 472 nm. The resulting SIF retrievals have a root-mean-squared (RMS) diff* value of 0.15 mW m−2 sr−1 nm−1 at the far-red band and 0.43 mW m−2 sr−1 nm−1 at the red band. This compares with 0.20 and 0.26 mW m−2 sr−1 nm−1 at the far-red band and 0.62 and 1.30 mW m−2 sr−1 nm−1 at the red band for the other two configurations described above. Given an SR of 0.3 nm, the increase in the SNR can also improve the SIF retrieval at both bands. If the SNR is improved to 450, the RMS diff* will be 0.17 mW m−2 sr−1 nm−1 at the far-red band and 0.47 mW m−2 sr−1 nm−1 at the red band. Therefore, the SIFIS onboard TECIS-1 will provide another set of observations dedicated to monitoring SIF at the global scale, which will benefit investigations of terrestrial vegetation photosynthesis from space. Full article
(This article belongs to the Special Issue Hyperspectral Remote Sensing of the Earth)
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16 pages, 2679 KiB  
Technical Note
First Evaluation of PRISMA Level 1 Data for Water Applications
by Claudia Giardino, Mariano Bresciani, Federica Braga, Alice Fabbretto, Nicola Ghirardi, Monica Pepe, Marco Gianinetto, Roberto Colombo, Sergio Cogliati, Semhar Ghebrehiwot, Marnix Laanen, Steef Peters, Thomas Schroeder, Javier A. Concha and Vittorio E. Brando
Sensors 2020, 20(16), 4553; https://doi.org/10.3390/s20164553 - 14 Aug 2020
Cited by 45 | Viewed by 7378
Abstract
This study presents a first assessment of the Top-Of-Atmosphere (TOA) radiances measured in the visible and near-infrared (VNIR) wavelengths from PRISMA (PRecursore IperSpettrale della Missione Applicativa), the new hyperspectral satellite sensor of the Italian Space Agency in orbit since March 2019. In particular, [...] Read more.
This study presents a first assessment of the Top-Of-Atmosphere (TOA) radiances measured in the visible and near-infrared (VNIR) wavelengths from PRISMA (PRecursore IperSpettrale della Missione Applicativa), the new hyperspectral satellite sensor of the Italian Space Agency in orbit since March 2019. In particular, the radiometrically calibrated PRISMA Level 1 TOA radiances were compared to the TOA radiances simulated with a radiative transfer code, starting from in situ measurements of water reflectance. In situ data were obtained from a set of fixed position autonomous radiometers covering a wide range of water types, encompassing coastal and inland waters. A total of nine match-ups between PRISMA and in situ measurements distributed from July 2019 to June 2020 were analysed. Recognising the role of Sentinel-2 for inland and coastal waters applications, the TOA radiances measured from concurrent Sentinel-2 observations were added to the comparison. The results overall demonstrated that PRISMA VNIR sensor is providing TOA radiances with the same magnitude and shape of those in situ simulated (spectral angle difference, SA, between 0.80 and 3.39; root mean square difference, RMSD, between 0.98 and 4.76 [mW m−2 sr−1 nm−1]), with slightly larger differences at shorter wavelengths. The PRISMA TOA radiances were also found very similar to Sentinel-2 data (RMSD < 3.78 [mW m−2 sr−1 nm−1]), and encourage a synergic use of both sensors for aquatic applications. Further analyses with a higher number of match-ups between PRISMA, in situ and Sentinel-2 data are however recommended to fully characterize the on-orbit calibration of PRISMA for its exploitation in aquatic ecosystem mapping. Full article
(This article belongs to the Special Issue Hyperspectral Remote Sensing of the Earth)
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