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Remote Sensing
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Calibration/Validation of Hyperspectral Imagery
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Calibration/Validation of Hyperspectral Imagery

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

Deadline for manuscript submissions: closed (26 June 2020) | Viewed by 29108

Special Issue Editors

Dr. Aaron Pearlman

E-Mail Website
Guest Editor
GeoThinkTank LLC / NASA Goddard Space Flight Center Biospheric Sciences Lab
Interests: pre-launch calibration of space-based optical sensors; satellite sensor validation; UAS payload development; metrology; optoelectronics
Dr. Shihyan Lee

E-Mail Website
Guest Editor
SAIC / NASA Ocean Biological Processing Group
Interests: pre-launch and post-launch space-borne optical instrument calibration and characterization; instrument design and performance specification for science applications

Special Issue Information

Dear Colleagues,

As recognized experts in the field, we would like to invite you to contribute to a Special Issue on the calibration and validation of hyperspectral imagery. Much progress has been made recently in developing advanced hyperspectral sensors that offer rich imagery to aid our understanding of complex environments—thorough calibration is the fundamental cornerstone of producing this high quality data. The issue will cover pre-launch calibration and post-launch validation of hyperspectral imagers on space-based, aircraft-based, and unmanned aircraft systems. We hope this issue will serve as a valuable resource highlighting recent advances in the field.

The issue will cover a broad range of areas of the calibration and validation of space-based, aircraft-based, or unmanned aircraft-based hyperspectral sensors used in remote sensing. These topics include but are not limited to the following:

  • Pre-launch calibration—radiometric, spectral, spatial
  • Post-launch vicarious validation field campaigns
  • Hyperspectral imagery artefact identification and mitigation
  • Cross-comparison of hyperspectral imagers with other satellite sensors

Dr. Aaron Pearlman
Dr. Shihyan Lee
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 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

  • calibration
  • validation
  • hyperspectral
  • pre-launch
  • spectral
  • spatial
  • radiometric
  • artifacts
  • field campaign

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Published Papers (7 papers)

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Research

24 pages, 7170 KiB  
Article
Combination of Cross- and Inter-Band Radiometric Calibrations for a Hyperspectral Sensor Using Model-Based Spectral Band Adjustment
by Hiroki Mizuochi, Satoshi Tsuchida, Kenta Obata, Hirokazu Yamamoto and Satoru Yamamoto
Remote Sens. 2020, 12(12), 2011; https://doi.org/10.3390/rs12122011 - 23 Jun 2020
Cited by 4 | Viewed by 3676
Abstract
Recently, the growing number of hyperspectral satellite sensors have increased the demand for a flexible and robust approach to their calibration. This paper proposes an operational method for the simultaneous correction of inter-sensor and inter-band biases in hyperspectral sensors via the soil line [...] Read more.
Recently, the growing number of hyperspectral satellite sensors have increased the demand for a flexible and robust approach to their calibration. This paper proposes an operational method for the simultaneous correction of inter-sensor and inter-band biases in hyperspectral sensors via the soil line concept for spectral band adjustment. Earth Observing-1 Hyperion was selected as an example, with the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) as a reference. The results over the Railroad Valley Playa calibration site indicated that the discrepancy in the analogous bands between Hyperion and MODIS during 2001–2008 was approximately 4–6% and 7–9% of the root-mean-square error in the top-of-atmosphere (TOA) radiance at the visible and near-infrared region and shortwave infrared region, respectively. For all Hyperion bands, the relative cross-calibration coefficients during this period were calculated (typically ranging from 0.9 to 1.1) to correct the Hyperion TOA radiance to be consistent with the MODIS and the other Hyperion bands. The application of the proposed approach could allow for more flexible cross-calibration of irregular-orbit sensors aboard the International Space Station. Full article
(This article belongs to the Special Issue Calibration/Validation of Hyperspectral Imagery)
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18 pages, 4759 KiB  
Article
Stability Assessment of OCO-2 Radiometric Calibration Using Aqua MODIS as a Reference
by Shanshan Yu, Robert Rosenberg, Carol Bruegge, Lars Chapsky, Dejian Fu, Richard Lee, Thomas Taylor, Heather Cronk, Christopher O’Dell, Amit Angal, Xiaoxiong Xiong, David Crisp and Annmarie Eldering
Remote Sens. 2020, 12(8), 1269; https://doi.org/10.3390/rs12081269 - 17 Apr 2020
Cited by 5 | Viewed by 3100
Abstract
With three imaging grating spectrometers, the Orbiting Carbon Observatory-2 (OCO-2) measures high spectral resolution spectra ( λ / Δ λ ≈ 19,000) of reflected solar radiation within the molecular oxygen (O 2 ) A-band at 0.765 μ m and two carbon dioxide (CO [...] Read more.
With three imaging grating spectrometers, the Orbiting Carbon Observatory-2 (OCO-2) measures high spectral resolution spectra ( λ / Δ λ ≈ 19,000) of reflected solar radiation within the molecular oxygen (O 2 ) A-band at 0.765 μ m and two carbon dioxide (CO 2 ) bands at 1.61 and 2.06 μ m. OCO-2 uses onboard lamps with a reflective diffuser, solar observations through a transmissive diffuser, lunar measurements, and surface targets for radiometric calibration and validation. Separating calibrator aging from instrument degradation poses a challenge to OCO-2. Here we present a methodology for trending the OCO-2 Build 8R radiometric calibration using OCO-2 nadir observations over eight desert sites and nearly simultaneous observations from Moderate Resolution Imaging Spectroradiometer (MODIS) with sensor viewing zenith angles of 15 ± 0.5 ∘ . For the O 2 A-band, this methodology is able to quantify a drift of −0.8 ± 0.1% per year and capture a small error in correcting the aging of the solar calibrator. For the other two OCO-2 bands, no measurable changes were seen, indicating less than 0.1% and less than 0.3% per year drift in the radiometric calibration of Band 2 and Band 3, respectively. Full article
(This article belongs to the Special Issue Calibration/Validation of Hyperspectral Imagery)
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18 pages, 4094 KiB  
Article
Classification of Anomalous Pixels in the Focal Plane Arrays of Orbiting Carbon Observatory-2 and -3 via Machine Learning
by Yuliya Marchetti, Robert Rosenberg and David Crisp
Remote Sens. 2019, 11(24), 2901; https://doi.org/10.3390/rs11242901 - 5 Dec 2019
Cited by 6 | Viewed by 3591
Abstract
A machine learning approach was developed to improve the bad pixel maps that mask damaged or unusable pixels in the imaging spectrometers of National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) and Orbiting Carbon Observatory-3 (OCO-3). The OCO-2 and OCO-3 instruments [...] Read more.
A machine learning approach was developed to improve the bad pixel maps that mask damaged or unusable pixels in the imaging spectrometers of National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) and Orbiting Carbon Observatory-3 (OCO-3). The OCO-2 and OCO-3 instruments use nearly 500,000 pixels to record high resolution spectra in three infrared wavelength ranges. These spectra are analyzed to retrieve estimates of the column-average carbon dioxide (XCO 2) concentration in Earth’s atmosphere. To meet mission requirements, these XCO 2 estimates must have accuracies exceeding 0.25%, and small uncertainties in the bias or gain of even one detector pixel can add significant error to the retrieved XCO 2 estimates. Thus, anomalous pixels are identified and removed from the data stream by applying a bad pixel map prior to further processing. To develop these maps, we first characterize each pixel’s behavior through a collection of interpretable and statistically well-defined metrics. These features and a prior map are then used as inputs in a Random Forest classifier to assign a likelihood that a given pixel is bad. Consequently, the likelihoods are analyzed and thresholds are chosen to produce a new bad pixel map. The machine learning approach adopted here has improved data quality by identifying hundreds of new bad pixels in each detector. Such an approach can be generalized to other instruments that require independent calibration of many individual elements. Full article
(This article belongs to the Special Issue Calibration/Validation of Hyperspectral Imagery)
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15 pages, 8080 KiB  
Article
Bi-Directional Reflectance Factor Determination of the Railroad Valley Playa
by Carol J. Bruegge, Craig Coburn, Arthur Elmes, Mark C. Helmlinger, Fumie Kataoka, Michele Kuester, Akihiko Kuze, Tina Ochoa, Crystal Schaaf, Kei Shiomi and Florian M. Schwandner
Remote Sens. 2019, 11(22), 2601; https://doi.org/10.3390/rs11222601 - 6 Nov 2019
Cited by 17 | Viewed by 4084
Abstract
Vicarious calibration is the determination of an on-orbit sensor’s radiometric response using measurements over test sites such as Railroad Valley (RRV), Nevada. It has the highest accuracy when a remote sensor’s view angle is aligned with that of the surface measurements, namely at [...] Read more.
Vicarious calibration is the determination of an on-orbit sensor’s radiometric response using measurements over test sites such as Railroad Valley (RRV), Nevada. It has the highest accuracy when a remote sensor’s view angle is aligned with that of the surface measurements, namely at a nadir view. For view angles greater than 10°, the dominant error is the uncertainty in the off-nadir correction factor. The factor is largest in the back-scatter principal plane and can reach 20%. The Orbiting-Carbon Observatory has access to a number of datasets to determine this deviation. These include measurements from field instruments such as the Portable Apparatus for Rapid Acquisition of Bidirectional Observation of the Land and Atmosphere (PARABOLA), as well as satellite measurements from Multi-angle Imaging SpectroRadiometer (MISR) and MODerate resolution Imaging Spectroradiometer (MODIS). The correction factor derived from PARABOLA is consistent in time and space to within 2% for view angles as large as 30°. Field spectrometer data show that the correction term is spectrally invariant. For this reason, a time-invariant model of RRV surface reflectance, along with empirically derived coefficients, is sufficient to use in the calibration of off-nadir sensors, provided there has been no recent rainfall. With this off-nadir correction, calibrations can be expected to have uncertainties within 5%. Full article
(This article belongs to the Special Issue Calibration/Validation of Hyperspectral Imagery)
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18 pages, 2234 KiB  
Article
Spectral and Radiometric Calibration of the Next Generation Airborne Visible Infrared Spectrometer (AVIRIS-NG)
by John W. Chapman, David R. Thompson, Mark C. Helmlinger, Brian D. Bue, Robert O. Green, Michael L. Eastwood, Sven Geier, Winston Olson-Duvall and Sarah R. Lundeen
Remote Sens. 2019, 11(18), 2129; https://doi.org/10.3390/rs11182129 - 13 Sep 2019
Cited by 77 | Viewed by 6123
Abstract
We describe advanced spectral and radiometric calibration techniques developed for NASA’s Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). By employing both statistically rigorous analysis and utilizing in situ data to inform calibration procedures and parameter estimation, we can dramatically reduce undesirable artifacts [...] Read more.
We describe advanced spectral and radiometric calibration techniques developed for NASA’s Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). By employing both statistically rigorous analysis and utilizing in situ data to inform calibration procedures and parameter estimation, we can dramatically reduce undesirable artifacts and minimize uncertainties of calibration parameters notoriously difficult to characterize in the laboratory. We describe a novel approach for destriping imaging spectrometer data through minimizing a Markov Random Field model. We then detail statistical methodology for bad pixel correction of the instrument, followed by the laboratory and field protocols involved in the corrections and evaluate their effectiveness on historical data. Finally, we review the geometric processing procedure used in production of the radiometrically calibrated image data. Full article
(This article belongs to the Special Issue Calibration/Validation of Hyperspectral Imagery)
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21 pages, 1476 KiB  
Article
Sensitivity Analysis Method for Spectral Band Adjustment between Hyperspectral Sensors: A Case Study Using the CLARREO Pathfinder and HISUI
by Kenta Obata
Remote Sens. 2019, 11(11), 1367; https://doi.org/10.3390/rs11111367 - 6 Jun 2019
Cited by 2 | Viewed by 3576
Abstract
The International Space Station has become the platform for deploying hyperspectral sensors covering the solar reflective spectral range for earth observation. Intercalibration of hyperspectral sensors plays a crucial role in evaluating/improving radiometric consistency. When intercalibrating between hyperspectral sensors, spectral band adjustment is required [...] Read more.
The International Space Station has become the platform for deploying hyperspectral sensors covering the solar reflective spectral range for earth observation. Intercalibration of hyperspectral sensors plays a crucial role in evaluating/improving radiometric consistency. When intercalibrating between hyperspectral sensors, spectral band adjustment is required to mitigate the effects of differences between the relative spectral responses (RSRs) of the sensors. Errors in spectral parameters used in spectral band adjustment are propagated through to the adjustment results. The present study analytically approximated the uncertainty in the spectral band adjustment for evaluating the relative contributions of uncertainties in parameters associated with the exo-atmosphere, atmosphere, and surface to the total uncertainty. Numerical simulations using the derived equations were conducted to perform a sensitivity analysis for the case of the spectral band adjustment between the Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) and the Hyperspectral Imager Suite (HISUI). The results show that the effects of errors in the solar irradiance were greater than those of other sources of error, indicating that accurate estimates of atmospheric reflectances and tranismittances are not needed for spectral band adjustment between CPF and HISUI in the atmospheric windows. The accuracy of the analytical approximation was also evaluated in the simulations. The framework of the sensitivity analysis is applicable to other pairs of hyperspectral sensors. Full article
(This article belongs to the Special Issue Calibration/Validation of Hyperspectral Imagery)
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21 pages, 9411 KiB  
Article
Empirical Absolute Calibration Model for Multiple Pseudo-Invariant Calibration Sites
by Bipin Raut, Morakot Kaewmanee, Amit Angal, Xiaoxiong Xiong and Dennis Helder
Remote Sens. 2019, 11(9), 1105; https://doi.org/10.3390/rs11091105 - 9 May 2019
Cited by 10 | Viewed by 3785
Abstract
This work extends an empirical absolute calibration model initially developed for the Libya 4 Pseudo-Invariant Calibration Site (PICS) to five additional Saharan Desert PICS (Egypt 1, Libya 1, Niger 1, Niger 2, and Sudan 1), and demonstrates the efficacy of the resulting models [...] Read more.
This work extends an empirical absolute calibration model initially developed for the Libya 4 Pseudo-Invariant Calibration Site (PICS) to five additional Saharan Desert PICS (Egypt 1, Libya 1, Niger 1, Niger 2, and Sudan 1), and demonstrates the efficacy of the resulting models at predicting sensor top-of-atmosphere (TOA) reflectance. It attempts to generate absolute calibration models for these PICS that have an accuracy and precision comparable to or better than the current Libya 4 model, with the intent of providing additional opportunities for sensor calibration. In addition, this work attempts to validate the general applicability of the model to other sites. The method uses Terra Moderate Resolution Imaging Spectroradiometer (MODIS) as the reference radiometer and Earth Observing-1 (EO-1) Hyperion image data to provide a representative hyperspectral reflectance profile of the PICS. Data from a region of interest (ROI) in an “optimal region” of 3% temporal, spatial, and spectral stability within the PICS are used for developing the model. The developed models were used to simulate observations of the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+), Landsat 8 (L8) Operational Land Imager (OLI), Sentinel 2A (S2A) MultiSpectral Instrument (MSI) and Sentinel 2B (S2B) MultiSpectral Instrument (MSI) from their respective launch date through 2018. The models developed for the Egypt 1, Libya 1 and Sudan 1 PICS have an estimated accuracy of approximately 3% and precision of approximately 2% for the sensors used in the study, comparable to the current Libya 4 model. The models developed for the Niger 1 and Niger 2 sites are significantly less accurate with similar precision. Full article
(This article belongs to the Special Issue Calibration/Validation of Hyperspectral Imagery)
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