ISPRS Journal of Photogrammetry and Remote Sensing
Call for papers on Special Issue:
New Generation of Hyperspectral Remote Sensing Data in the Study of Global Agriculture and Vegetation
Dr. Prasad S. Thenkabail (United States Geological Survey, USA)
Prof. Alfredo Huete (University of Technology Sydney, Australia)
Prof. Eyal Ben-Dor (Tel Aviv University, Israel)
Submission deadline: October 31, 2021 (Begin submissions anytime from November 1, 2020 through October 31, 2021). All papers will be published together in the July 2022 special issue.
Planned publication date: July 31, 2022 special issue
Great advances in remote sensing are taking place by coming together of: 1. increasingly sophisticated data acquired in H3-mode: hyperspectral (in hundreds of narrow registered bands gathered near-continuously over the electromagnetic spectrum), hyperspatial (<5m spatial resolution), and hyper-temporal (e.g., daily), 2. Machine learningdeep learningartificial intelligence, 3. Cloud computing and code sharing on the cloud, 4. Petabyte-scale big data analytics involving multi-satellite, multi-sensor remote sensing that is harmonized, normalized, and analyzed seamlessly on the cloud, 5. Large web-accessible training and validation data sourced from mobile Apps, very high resolution imagery (VHRI), and secondary sources, 6. Targeted data delivery, in near-real time, through mobile Apps, and 7. Multi domains (laboratory, field, low/ high air altitudes and orbital in both optical and thermal domains.
In this regard several new generations of Earth Observing (EO) advanced spaceborne hyperspectral sensors were launched and others are in preparation. Recent launches include the German Aerospace Center’s (Deutsches Zentrum fur Luft- und Raumfahrt; DLR’s) Earth Sensing Imaging Spectrometer (DESIS) integrated into and onboard the International Space Station’s (ISS) Multi-integrated into User-System for Earth Sensing (MUSES) platform, the Italian Space Agency (Agenzia Spaziale Italiana, ASI’s) PRISMA (PRecursore IperSpettrale della Missione Applicativa), Japanese HISUI (Hyperspectral Imager Suite) onboard ISS, India’s HysIS (Hyperspectral Imaging Satellite), China’s Advanced Hyperspectral Imager (AHSI) aboard China’s GaoFen-5 (GF-5) satellite, and China’s Jilin Hyperspectral Satellite constellation. These will be followed by several hyperspectral missions coming up for launch such as the German DLR’s the Environmental Mapping and Analysis Program (EnMAP) to be launched soon, NASA’s Surface Biology and Geology (SBG) mission (formerly HyspIRI mission), EMIT from NASA, CHIME from ESA FLEX of ESA, SHALOM from ASI-ISA and more others from private players. These sensors will collect data in hundreds of wavebands and, typically, across the entire visible near and short infrared spectral range (400 to 2500 nanometers ) or part of it, and in 30 m or better GDS. Such data captured as “spectral signatures” leading to spectral libraries will be (and is) a quantum leap in data of the Planet Earth relative to older generation multispectral sensors such as the Landsats and Sentinels which have provided great service in the study of the Planet Earth over last few decades. Nevertheless, such data also provides great challenges in terms of data analysis, algorithm development, and application development.
Given the above context, this special issue proposes a comprehensive study of agricultural crops and vegetation based on the data gathered from new generation of hyperspectral data gathered from multiple sensors andor comparison with hyperspectral data from myriad sensors and all spectral and altitude domains. Specific topics of interest include, but not limited to:
Quantitative analysis of agricultural crop biophysical, biochemical, and plant health parameters through hyperspectral narrow-bands (HNBs), hyperspectral vegetation indices (HVIs) and comparison with multispectral broadband derived vegetation indices;
Crop type classifications using HNB data and comparison with multi-spectral broadband (MBB) data showing improvements in accuracies and reduction in uncertainties;
Classification methods and techniques using hyperspectral data for agriculture or vegetation;
Machine learningdeep learningartificial intelligence in hyperspectral data analysis;
Cloud computing and algorithm development to analyse hyperspectral data for myriad applications in agriculture;
Generating spectral libraries as a tool for training and validation of hyperspectral generated cropland products (e.g., crop types, irrigation versus rainfed, biophysical quantities, biochemical quantities, plant health quantities) through machine learning;
Hyperspectral data assessment and management by addressing data redundancy and issues of Hughes’ phenomenon;
Comparative studies of different generation of hyperspectral data, their inter-comparison and synergies;
Florescence measurement of vegetation from air (ASIAIbis) and space (FLEX) missions.
Multi and hyperspectral sensing in the thermal region of vegetation – current and feature missions as ECOSTRESS and LSTM missions respectively.
The new HSR era from drones (UAV’s) to assess vegetation status
Vicarious calibration of HSR sensors
Articles must be original research, not published elsewhere. All papers must be comprehensive and thorough with adequate training and validation data, ability to apply the methods across sites, and with proper accuracy and uncertainty analysis.
All articles will go through rigorous peer-review process as per the journal norms. Review articles around the topics are also encouraged. Papers must follow the instructions for authors at http://www.elsevier.com/journals/isprs-journal-of-photogrammetry-and-remote-sensing/0924-2716/guide-for-authors.
Please submit the full manuscript to http://ees.elsevier.com/photo/default.asp by October 31, 2021.
For any inquiry, please contact Dr. Prasad S. Thenkabail (firstname.lastname@example.org)