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Hyperspectral Remote Sensing Data Calibration and Validation

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 13930

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Special Issue Editor

Dr. Charles M. Bachmann

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Guest Editor

Digital Imaging and Remote Sensing Laboratory, Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, 54 Lomb Memorial Drive, Rochester, NY 14623-5604, USA
Interests: digital imaging; remote sensing; hyperspectral imaging; multi-sensor imaging; RADAR; radiative transfer modeling; goniometers; BRDF; calibration and validation; coastal science and applications; coastal sediments; wetlands; manifold and graph algorithms; imaging science

Special Issue Information

Dear Colleagues,

Hyperspectral remote sensing has played an important role in a wide variety of applications including agriculture, forestry, coastal zone monitoring, land-cover mapping and change, geology, and many others. A wide variety of models have been employed in these applications ranging from spectral band indices to physics-based radiative transfer models, and such models are often a component of a larger workflow that includes important data processing steps such as atmospheric correction. Many of these approaches require some form of calibration, whether in the lab, field setting, or both. Model validation in a laboratory setting and, ultimately, field validation assess the efficacy and potential limitations of models, allowing for further improvement.

This Special Issue invites contributed articles that emphasize the calibration and validation of hyperspectral remote sensing data either in a particular application or alternatively in the context of best practices independent of application. Potential topics may include, but are not limited to, the following:

Hyperspectral data calibration methodologies in either lab or field settings for a particular application or in general;
Transfer of hyperspectral data calibration from lab to field settings;
Validation of models using hyperspectral for a specific application, such as those outlined above or in other applications;
Validation of specific types of retrieval models involving band indices, radiative transfer models, etc.;
Validation of specific processing steps (e.g., atmospheric correction) that are essential to the success of hyperspectral product retrieval.

Dr. Charles M. Bachmann
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. Remote Sensing 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 2700 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
calibration
validation
applications
radiative transfer
spectral indices

Published Papers (7 papers)

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20 pages, 38431 KiB

Open AccessArticle

The Effect of Grain Size on Hyperspectral Polarization Data of Particulate Material

by Rachel M. Golding, Christopher S. Lapszynski, Charles M. Bachmann and Chris H. Lee

Remote Sens. 2023, 15(14), 3668; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15143668 - 23 Jul 2023

Viewed by 810

Abstract

Polarization provides useful quantitative information about scattering surfaces. In hyperspectral remote sensing of natural surfaces composed of granular materials, there are relatively few studies of polarization. Most earlier remote sensing studies of polarization have been based on multi-spectral data, and the majority focused on the negative branch of polarization, which typically appears at phase angles less than 20 degrees, using models with limited accuracy. Models of the positive branch have also shown limitations, particularly at longer phase angles. We review these earlier studies by Hapke and Shkuratov and present the results of our laboratory study using hyperspectral polarization imagery of particulate surfaces. Although the linear polarization ratio is typically a nonlinear function of phase angle, our results show that in an approximately linear region of the polarization curve, there is a correlation between the slope of the linear polarization ratio and the average grain size. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Data Calibration and Validation)

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18 pages, 10365 KiB

Open AccessArticle

Radiometric Calibration of GF5-02 Advanced Hyperspectral Imager Based on RadCalNet Baotou Site

by Hongzhao Tang, Chenchao Xiao, Kun Shang, Taixia Wu and Qi Li

Remote Sens. 2023, 15(9), 2233; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15092233 - 23 Apr 2023

Cited by 2 | Viewed by 1195

Abstract

In this study, an on-orbit radiometric calibration campaign of the GF5-02 AHSI was performed at the RadCalNet Baotou site, based on the automated observation of reflectance and atmospheric parameters of a 300 m × 300 m homogeneous desert area. The consistency of the radiometric calibration coefficients was validated both at the Dunhuang calibration site and the Baotou site. The average relative difference between the calibrated top-of-atmospheric (TOA) radiance and the predicted TOA radiance were less than 7%. The R² of these two TOA radiances were all higher than 0.99. These results showed that the accuracy of calibration coefficients could meet the requirements of hyperspectral quantification applications. The uncertainty of GF5-02 AHSI radiometric calibration was 6.18%. This study also demonstrated that automated observation data of the Baotou site were reliable for high-frequency radiometric calibration and radiometric performance monitoring of GF5-02 AHSI. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Data Calibration and Validation)

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19 pages, 6599 KiB

Open AccessArticle

Evaluation of the Spatial Representativeness of In Situ SIF Observations for the Validation of Medium-Resolution Satellite SIF Products

by Micol Rossini, Marco Celesti, Gabriele Bramati, Mirco Migliavacca, Sergio Cogliati, Uwe Rascher and Roberto Colombo

Remote Sens. 2022, 14(20), 5107; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14205107 - 12 Oct 2022

Cited by 3 | Viewed by 1881

Abstract

The upcoming Fluorescence Explorer (FLEX) mission will provide sun-induced fluorescence (SIF) products at unprecedented spatial resolution. Thus, accurate calibration and validation (cal/val) of these products are key to guarantee robust SIF estimates for the assessment and quantification of photosynthetic processes. In this study, we address one specific component of the uncertainty budget related to SIF retrieval: the spatial representativeness of in situ SIF observations compared to medium-resolution SIF products (e.g., 300 m pixel size). Here, we propose an approach to evaluate an optimal sampling strategy to characterise the spatial representativeness of in situ SIF observations based on high-spatial-resolution SIF data. This approach was applied for demonstration purposes to two agricultural areas that have been extensively characterized with a HyPlant airborne imaging spectrometer in recent years. First, we determined the spatial representativeness of an increasing number of sampling points with respect to a reference area (either monocultural crop fields or hypothetical FLEX pixels characterised by different land cover types). Then, we compared different sampling approaches to determine which strategy provided the most representative reference data for a given area. Results show that between 3 and 13.5 sampling points are needed to characterise the average SIF value of both monocultural fields and hypothetical FLEX pixels of the agricultural areas considered in this study. The number of sampling points tends to increase with the standard deviation of SIF of the reference area, as well as with the number of land cover classes in a FLEX pixel, even if the increase is not always statistically significant. This study contributes to guiding cal/val activities for the upcoming FLEX mission, providing useful insights for the selection of the validation site network and particularly for the definition of the best sampling scheme for each site. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Data Calibration and Validation)

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17 pages, 4425 KiB

Open AccessArticle

Band-Averaged Response Sensitivity Study of an Imaging Spectrometer for the CLARREO Pathfinder Mission

by Fatholah Salehi, Kurtis Thome, Brian N. Wenny, Ronald Lockwood and Zhipeng Wang

Remote Sens. 2022, 14(10), 2302; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102302 - 10 May 2022

Cited by 2 | Viewed by 1220

Abstract

Prelaunch absolute, SI-traceable radiometric calibration of satellite-based sensors is key to ensuring the utility of imaging spectrometer-based data products. The development of detector-based calibration techniques leads to the feasibility of meeting the 0.3% uncertainty level needed to provide climate quality data sets. Detector-based calibration is a method in which a well-understood and stable transfer radiometer is calibrated in a standards laboratory to SI-traceable standards, and transported to a facility calibrating a sensor of interest. The transfer radiometer provides the calibration of the source used in the radiometric calibration. A detector-based calibration approach is part of the prelaunch calibration of the CLARREO (Climate Absolute Radiance and Refractivity Observatory) Pathfinder (CPF) sensor with the Goddard Laser for Absolute Measurement of Radiance (GLAMR) system. The SI-traceability of GLAMR is through the electric watt as part of the absolute radiometric calibration of the detectors at the National Institute of Standards and Technology using the Primary Optical Watt Radiometer. The current work uses GLAMR data collected with a visible and near-infrared imaging spectrometer calibration demonstration system to develop a source/sensor modeled calibration data set as part of a sensitivity study to evaluate uncertainties from the spectral sampling and processing methods that accompany the GLAMR calibration process. The spectral “supersets” include realistic instrumental features as well as effects from the GLAMR source. The methods needed to ensure that spurious sensor and GLAMR data are excluded are described. Results are given from the sensitivity study related to GLAMR spectral sampling and signal-to-noise ratio (SNR) effects, sensor integration time, and frame averaging of the imaging spectrometer data. The study shows that the 6 nm bandwidth sensor simulation requires a 1 nm spectral sampling of the GLAMR source with a radiance level that provides an in-band peak SNR > 200 to ensure that climate quality accuracies can be achieved. The results are also used to refine the test plan for the independent calibration for the CLARREO Pathfinder sensor calibration to optimize test time while meeting the required accuracy levels. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Data Calibration and Validation)

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22 pages, 3130 KiB

Open AccessArticle

Absolute Radiometric Calibration of an Imaging Spectroradiometer Using a Laboratory Detector-Based Approach

by Zhipeng Wang, Kurtis Thome, Ronald Lockwood and Brian N. Wenny

Remote Sens. 2022, 14(9), 2245; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092245 - 07 May 2022

Cited by 7 | Viewed by 1910

Abstract

The HyperSpectral Imager for Climate Science (HySICS) is the core instrument of the Climate Absolute Refractivity and Reflectance Observatory (CLARREO) Pathfinder (CPF) mission and is currently scheduled to be launched to the International Space Station (ISS) in 2023. HySICS is an Offner–Chrisp imaging spectrometer designed to meet an unprecedented radiometric uncertainty requirement of 0.3% (k = 1) over its entire spectral range of 350–2300 nm. The approach represents the need for significant improvement over the Radiometric Calibration (RadCal) of existing space-borne spectrometers. One strategy to demonstrate that HySICS achieves this level of accuracy is through an Independent Calibration (IndCal) effort that can provide an alternative referencing RadCal, which follows a traceability chain independent of the operational RadCal of ratioing approach. The IndCal relies on a pre-launch detector-based absolute RadCal of HySICS, using a tunable laser system as source, and the system planned for the HySICS absolute RadCal is the Goddard Laser for Absolute Measurement of Radiance (GLAMR). GLAMR was developed at NASA’s Goddard Space Flight Center and has been used to calibrate multiple operational remote sensing instruments, as well as the SOlar, Lunar Absolute Reflectance Imaging Spectroradiometer (SOLARIS), a calibration demonstration system developed for the CLARREO mission. In this work, the data of SOLARIS GLAMR RadCal conducted in 2019 are processed to derive the Absolute Spectral Response (ASR) functions and other key characterization parameters of SOLARIS detectors. The results are further analyzed with the goals to plan the HySICS GLAMR RadCal, in particular to optimize its configuration, to demonstrate the traceability route to the NIST standard, and to develop the error budget of the calibration approach. The SOLARIS calibration is also compared with other source- and detector-based calibrations to validate the absolute radiometric accuracy achieved. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Data Calibration and Validation)

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23 pages, 4324 KiB

Open AccessArticle

PRISMA L1 and L2 Performances within the PRISCAV Project: The Pignola Test Site in Southern Italy

by Stefano Pignatti, Aldo Amodeo, Maria Francesca Carfora, Raffaele Casa, Lucia Mona, Angelo Palombo, Simone Pascucci, Marco Rosoldi, Federico Santini and Giovanni Laneve

Remote Sens. 2022, 14(9), 1985; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14091985 - 21 Apr 2022

Cited by 11 | Viewed by 2891

Abstract

In March 2019, the PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral satellite was launched by the Italian Space Agency (ASI), and it is currently operational on a global basis. The mission includes the hyperspectral imager PRISMA working in the 400–2500 nm spectral range with 237 bands and a panchromatic (PAN) camera (400–750 nm). This paper presents an evaluation of the PRISMA top-of-atmosphere (TOA) L1 products using different in situ measurements acquired over a fragmented rural area in Southern Italy (Pignola) between October 2019 and July 2021. L1 radiance values were compared with the TOA radiances simulated with a radiative transfer code configured using measurements of the atmospheric profile and the surface spectral characteristics. The L2 reflectance products were also compared with the data obtained by using the ImACor code atmospheric correction tool. A preliminary assessment to identify PRISMA noise characteristics was also conducted. The results showed that: (i) the PRISMA performance, as measured at the Pignola site over different seasons, is characterized by relative mean absolute differences (RMAD) of about 5–7% up to 1800 nm, while a decrease in accuracy was observed in the SWIR; (ii) a coherent noise could be observed in all the analyzed images below the 630th scan line, with a frequency of about 0.3–0.4 cycles/pixel; (iii) the most recent version of the standard reflectance L2 product (i.e., Version 2.05) matched well the reflectance values obtained by using the ImACor atmospheric correction tool. All these preliminary results confirm that PRISMA imagery is suitable for an accurate retrieval of the bio-geochemical variables pertaining to a complex fragmented ecosystem such as that of the Southern Apennines. Further studies are needed to confirm and monitor PRISMA data performance on different land-cover areas and on the Radiometric Calibration Network (RadCalNet) targets. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Data Calibration and Validation)

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Other

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14 pages, 4942 KiB

Open AccessTechnical Note

Image Correction and In Situ Spectral Calibration for Low-Cost, Smartphone Hyperspectral Imaging

by Matthew Davies, Mary B. Stuart, Matthew J. Hobbs, Andrew J. S. McGonigle and Jon R. Willmott

Remote Sens. 2022, 14(5), 1152; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051152 - 25 Feb 2022

Cited by 7 | Viewed by 2761

Abstract

Developments in the portability of low-cost hyperspectral imaging instruments translate to significant benefits to agricultural industries and environmental monitoring applications. These advances can be further explicated by removing the need for complex post-processing and calibration. We propose a method for substantially increasing the utility of portable hyperspectral imaging. Vertical and horizontal spatial distortions introduced into images by ‘operator shake’ are corrected by an in-scene reference card with two spatial references. In situ light-source-independent spectral calibration is performed. This is achieved by a comparison of the ground-truth spectral reflectance of an in-scene red–green–blue target to the uncalibrated output of the hyperspectral data. Finally, bias introduced into the hyperspectral images due to the non-flat spectral output of the illumination is removed. This allows for low-skilled operation of a truly handheld, low-cost hyperspectral imager for agriculture, environmental monitoring, or other visible hyperspectral imaging applications. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Data Calibration and Validation)

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