Special Issue on Ushering a New Era of Hyperspectral Remote Sensing to Advance Remote Sensing Science in the Twenty-first Century

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The American Society for Photogrammetry and Remote Sensing (ASPRS) Photogrammetric Engineering & Remote Sensing (PE&RS)

Special Issue on Ushering a New Era of Hyperspectral Remote Sensing to Advance Remote Sensing Science in the Twenty-first Century

 

Guest Editors: Dr. Prasad S. Thenkabail, Dr. Itiya Aneece, Dr. Pardhasaradhi Teluguntla
U.S. Geological Survey (USGS)
Email: pthenkabail@usgs.gov, ianeece@usgs.gov,  pteluguntla@usgs.gov

Deadline for submission of manuscripts: December 15, 2023  March 31, 2024
Tentative publication date: August 1, 2024

 

Great advances are taking place in remote sensing with the advent of new generation of hyperspectral sensors. These include data from, already in orbit sensors such as: 1. Germany’s Deutsches Zentrum fur Luftund Raumfahrt (DLR’s) Earth Sensing Imaging Spectrometer (DESIS) sensor onboard the International Space Station (ISS), 2. Italian Space Agency’s (ASI’s) PRISMA (Hyperspectral Precursor of the Application Mission), and 3. Germany’s DLR’s Environmental Mapping and Analysis Program (EnMAP). Further, Planet Labs PBC recently announced the launch of two hyperspectral sensors called Tanager in 2023. NASA is planning for the hyperspectral sensor Surface Biology and Geology (SBG) to be launched in the coming years. Further, we already have over 70,000 hyperspectral images of the world acquired from NASA’s Earth Observing-1 (EO-1) Hyperion that are freely available to anyone from the U.S. Geological Survey’s data archives. These suites of sensors acquire data in 200 plus hyperspectral narrowbands (HNBs) in 2.55 to 12 nm bandwidth, either in 400-1000 or 400-2500 nm spectral range with SBG also acquiring data in the thermal range. In addition, Landsat-NEXT is planning a constellation of 3 satellites each carrying 26 bands in the 400-12,000 nm wavelength range. HNBs provide data as “spectral signatures” in stark contrast to “a few data points along the spectrum” provided by multispectral broadbands (MBBs) such as the Landsat satellite series.

The goal of this special issue is to seek scientific papers that perform research utilizing data from these new generation hyperspectral narrowband (HNB) sensors for a wide array of science applications and compare them with the performance of the multispectral broadband (MBB) sensors such as Landsat, Sentinels, MODIS, IRS, SPOT, and a host of others.

Papers on the following topics are of particular interest:

  1. Methods and techniques of understanding, processing, and computing hyperspectral data with specific emphasis on machine learning, deep learning, artificial intelligence (ML/DL/AI), and cloud computing.
  2. Issues of hyperspectral data volumes, data redundancy, and overcoming Hughes’ phenomenon.
  3. Building hyperspectral libraries for purposes of creating reference training, testing, and validation data.
  4. Utilizing time-series multispectral data and hyperspectral data over many years to build data cubes and apply advanced computational methods of ML/DL/AI methods and approaches on the cloud.
  5. Discussions of hyperspectral data analysis techniques like full spectral analysis versus optimal band analysis.
  6. Developing hyperspectral vegetation indices (HVIs) for targeted applications to model and map plant biophysical (e.g., Yield, biomass, leaf area index), biochemical (e.g., Nitrogen, anthocyanins, carotenoids), plant health/stress, and plant structural quantities.
  7. Classification of complex vegetation and crop types/species using HNBs and HVIs and comparing them with the performance of multispectral broadband data.

All submissions will be peer-reviewed in line with PE&RS policy. Because of page limits, not all submissions recommended for acceptance by the review panel may be included in the special issue. Under this circumstance, the guest editors will select the most relevant papers for inclusion in the special issue. Authors must prepare manuscripts according to the PE&RS Instructions to Authors, published in each issue of PE&RS and also available on the ASPRS website at Instructions to Authors.

Important Dates: Manuscripts due: December 15, 2023; Final papers due: May 1, 2024; Publication: August 1, 2024

Please submit your manuscript at https://www.editorialmanager.com/asprs-pers/ select “Hyperspectral Remote Sensing”

Guest Editors

Dr. Prasad S. Thenkabail, PhD
Senior Scientist (ST)
U.S. Geological Survey
Flagstaff, AZ 86001, USA
Email\s: pthenkabail@usgs.gov; thenkabail@gmail.com

Dr. Itiya Aneece
U.S. Geological Survey
Flagstaff, AZ 86001, USA
Email: ianeece@usgs.gov

Dr. Pardhasaradhi Teluguntla
U.S. Geological Survey
Flagstaff, AZ 86001, USA
Email: pteluguntla@usgs.gov