Journal of Applied Remote Sensing

Editor-in-Chief: Ni-Bin Chang, University of Central Florida

The Journal of Applied Remote Sensing (JARS) is an online journal that optimizes the communication of concepts, information, and progress within the remote sensing community to improve the societal benefit for monitoring and management of natural disasters, weather forecasting, agricultural and urban land-use planning, environmental quality monitoring, ecological restoration, and numerous other commercial and scientific applications. 

Calls for Papers
How to Submit to a Special Section

To submit a manuscript for consideration in a special section, please prepare the manuscript according to the journal guidelines and use the Online Submission SystemLeaving site. A cover letter indicating that the submission is intended for this special section should be included with the paper. Papers will be peer‐reviewed in accordance with the journal's established policies and procedures. Authors have the choice to publish with open access.

Optics in Atmospheric Propagation and Adaptive Systems
Publication Date
Special section papers are published as soon as the copyedited and typeset proofs are approved by the author.
Submission Deadline
Closed for submissions.
Guest Editors
Karin U. Stein

Fraunhofer Institute of Optronics, System Technologies and Image Exploitation - IOSB
Department Signatorics
Ettlingen, Germany
E-mail: karin.stein@iosb.fraunhofer.de

Szymon Gladysz

Fraunhofer Institute of Optronics, System Technologies and Image Exploitation - IOSB
Department Signatorics
Ettlingen, Germany
E-mail: szymon.gladysz@iosb.fraunhofer.de

Christian Eisele

Fraunhofer Institute of Optronics, System Technologies and Image Exploitation - IOSB
Department Signatorics
Ettlingen, Germany
E-mail: christian.eisele@iosb.fraunhofer.de

Vladimir P. Lukin

VE Zuev Institute of Atmospheric Optics
Tomsk, Russia
E-mail: lukin@iao.ru

Scope

The use of sensors for active and passive remote sensing of the Earth and its atmosphere, for free-space laser communication, and for high-resolution imaging of ground-based and airborne objects are fields of growing interest for both civilian and military applications. Many applications ask for space-to-ground (e.g. remote sensing satellites) or ground-to-space (e.g. astronomical telescopes) imaging systems with ever-improving spectral and/or spatial resolution, working in spectral regions from the UV to radar.

All of these systems have in common that their performance is often not limited by system design, but by effects caused by the long propagation of the signal through the atmosphere, which acts as an absorbing, scattering, and radiating random medium. A thorough understanding of these effects is needed for an accurate analysis of installed system performance and the correct interpretation of measurement results. System performance may be improved by the implementation of modern methods allowing for the reduction of effects associated with signal propagation. These include sophisticated algorithms and compensative hardware, making the system adaptive to changing conditions. A profound knowledge of the relevant atmospheric parameters at different locations is needed for the development of these adaptive systems and has to be obtained from measurements. The Journal of Applied Remote Sensing (JARS) will publish a special section on optics in atmospheric propagation and adaptive systems. The scope includes, but is not limited to:

  • Characterization of the environment: profiles of temperature, humidity, extinction, refractivity, radiance (also non-LTE), optical turbulence, updates of transmission and radiance codes, atmospheric refraction, atmospheric turbulence, VIS and IR backgrounds, and statistics of propagation parameters.
  • Propagation and imaging through turbulent media: meteorological models, the strong turbulence regime, laser beam propagation, laser speckle effects, scattering and multiple scattering effects; aero-optic and jet plume effects, correction methods for atmospheric effects, compensation for anisoplanatism and scintillation; laser beam projection on an extended target, target-in-the-loop propagation, and compensation in atmospheric turbulence.
  • Techniques and devices for measurement and/or mitigation of atmospheric effects on systems: adaptive optics, deconvolution, sensor fusion, post processing etc; multiconjugate adaptive optics, compensated imaging systems, novel optical components such as liquid crystal and MEMS devices, wavefront sensors, high-frame rate, and low-noise infrared detectors.
  • Laser-based sensing and laser communication: laser beam focusing, sensing, and free-space communication, system and atmospheric simulations, hardware configurations, communications theory issues, bandwidth limits, multiplexing issues, adaptive optics use for increased performance, atmospheric modelling, and laser speckle and other noise sources, and loss of coherence for active (laser) systems.

This call for papers is open to everyone, and participants in the Optics in Atmospheric Propagation and Adaptive Systems at Remote Sensing in Warsaw, Poland, are invited to submit results presented at the conference. Our website contains information about our publication policy concerning proceedings submissions to a journal.

Both application and theoretical papers are welcome. To submit to this special section, prepare the paper according to JARS guidelines and submit via the online submission system (https://jars.msubmit.net). A cover letter indicating that the submission is intended for this special section should be included. Papers will be peer reviewed in accordance with the journal's established policies and procedures. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within 6 weeks of manuscript submission. Special sections are opened online once a minimum of four papers have been accepted. Each paper is published as soon as the copyedited and typeset proofs are approved by the author.

Atmospheric propagation
Advances in Agro-Hydrological Remote Sensing for Water Resources Conservation
Publication Date
Special section papers are published as soon as the copyedited and typeset proofs are approved by the author.
Submission Deadline
Closed for submissions.
Guest Editors
Antonino Maltese

University of Palermo
Department of Civil Engineering, Environmental, Aerospace, of Materials
Palermo, Italy
E-mail: antonino.maltese@unipa.it 

Christopher M. U. Neale

University of Nebraska
Robert B. Daugherty Water for Food Global Institute
Lincoln, Nebraska, USA
E-mail: cneale@nebraska.edu

Scope

Remote sensing technology has enhanced our ability to monitor and manage our agriculture, ecosystems, and water resources over time and space.  However, in spite of significant progress in recent years, there are still many areas where the potential of remote sensing has not been fully realized, and these are areas of ongoing research. Of unique importance are those efforts that are focused on gaining a better understanding of what sensors are actually measuring, as well as new applications and inverse modelling techniques to retrieve hydrologic and vegetation parameters.

The Journal of Applied Remote Sensing (JARS) will publish a special section on "Agro-Hydrological Sciences" to bring together research in this area. Both application and theoretical papers related to agro-hydrological sciences are welcome. Review papers are also welcome for state-of-the-art research covering problems, progress, and prospects in key areas of earth observations (EO) from global to basin to plot scales, by assessing the advances and identifying the needs in physical modelling for improving our knowledge of water resource, food security, and ecosystems processes. Please contact the guest editors for approval prior to writing a review paper.  

Papers related (but not limited) to the following agro-hydrological sciences topics are solicited:   

  • agro-hydrological modelling
  • sensors for monitoring in hydrology and water resources
  • data assimilation in agriculture and hydrology
  • satellite-based rainfall estimation and modelling (e.g., meteorological RADAR)
  • soil water content, precipitation, snow, and ice hydrology
  • water resource management
  • drought monitoring, analysis, and prediction
  • radar applications in agro-hydrology (e.g., soil moisture and flooding)
  • lidar applications in agro-hydrology
  • water quality
  • evapotranspiration, energy balance (EB) modelling at different scale and validation (eddy covariance, scintillometry etc.)
  • smarter solutions for farmers based on IT, cloud computing, mobile technology, GPS
  • precision farming applications
  • crop yield modelling
  • food production, energy, and water nexus
  • open data for agriculture and food production
  • water securing for food
  • disease detection in agriculture
  • UAV and airborne platforms.  

This call for papers is open to everyone, and participants in the Remote Sensing for Agriculture, Ecosystems, and Hydrology conference at Remote Sensing in Warsaw, Poland, are invited to submit results presented at the conference. Our website contains information about our publication policy concerning proceedings submissions to a journal.

Both application and theoretical papers are welcome. To submit to this special section, prepare the paper according to JARS guidelines and submit via the online submission system (https://jars.msubmit.net). A cover letter indicating that the submission is intended for this special section should be included. Papers will be peer reviewed in accordance with the journal's established policies and procedures. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within 6 weeks of manuscript submission. Special sections are opened online once a minimum of four papers have been accepted. Each paper is published as soon as the copyedited and typeset proofs are approved by the author.

Water Resources Conservation
Advances in Remote Sensing for Air Quality Management
Publication Date
Special section papers are published as soon as the copyedited and typeset proofs are approved by the author.
Submission Deadline
Manuscripts due 31 May 2018
Guest Editors
Barry Gross

City University of New York
160 Convent Avenue
New York, New York, USA
E-mail: gross@ccny.cuny.edu

Klaus Schäfer

Atmospheric Physics Consultant
Garmisch-Partenkirchen, Germany
E-mail: schaefer@atmosphericphysics.de

Philippe Keckhut

Institute Pierre Simon Laplace (IPSL)
Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS)
Guyancourt, France
E-mail: Philippe.Keckhut@latmos.ipsl.fr

Scope

With the continued increase of urbanization around the globe, the emission and transport of a wide range of gases and aerosol particulates continues to pose significant potential health risks. To better understand and mitigate the air-quality risks using informed scientifically based protocols, we need to further continue to improve our observational and analysis capabilities to identify and quantify these atmospheric components and their sources. In order to provide a comprehensive survey of existing capabilities and potential improvements using remote sensing, this special section of the Journal of Applied Remote Sensing seeks to collect and organize papers addressing the latest state of the art in air quality monitoring and assessment from a diverse set of instrumental platforms and technologies including satellite sensors as well as ground-based and air-borne active and passive remote sensors. Studies using multiple instruments and platforms which improve interpretation of pollution mechanisms are of special interest.

Suggested topics may include but are not limited to:

  • Observational and algorithmic approaches to quantifying pollutant gases including nitrogen dioxide (NO2), sulfur dioxide (SO2), ammonia (NH3), carbon monoxide (CO), methane (CH4), carbon dioxide (CO2), ozone (O3) etc.
  • Retrieval of aerosol optical properties including aerosol optical depth (AOD), angstrom exponent (AE), albedo and particle modes, and the use of these products to better quantify PM2.5 and/or PM10 levels
  • Use of integrated remote sensing tools to better constrain and improve current atmospheric pollution modeling methodologies
  • Assimilation of air quality remote sensing retrieval products and profiles into existing operational meteorological chemical transport models
  • Innovative use of GIS and and/or machine learning tools like source apportionment to better integrate remote sensing data for a better understanding of pollution sources, transport mechanisms, and potential health effects
  • Improved tracking and quantification of both natural and anthropogenic pollutant plumes
  • Reports and assessments of next-generation air pollution sensors such as TEMPO (Tropospheric Emissions: Monitoring of Pollution), TROPOMI (TROPOspheric Monitoring Instrument) and IASI-NG (Infrared Atmospheric Sounding Interferometer Next Generation)
  • Characterization of temporal and spatial air pollution trends and coupling at local, regional, and global scales
  • Novel sensor designs and applications
  • Regional studies in stressed environments.

To submit a manuscript for consideration, please prepare the manuscript according to the journal guidelines and submit the paper via the online submission system (https://jars.msubmit.net). A cover letter indicating that the submission is intended for this special section should be included with the paper. Papers will be peer reviewed in accordance with the journal's established policies and procedures. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within 6 weeks of manuscript submission. Special sections are opened online once a minimum of four papers have been accepted. Each paper is published as soon as the copyedited and typeset proofs are approved by the author.

Air Quality
Advances in Deep Learning for Hyperspectral Image Analysis and Classification (ADLHI)
Publication Date
Special section papers are published as soon as the copyedited and typeset proofs are approved by the author.
Submission Deadline
Manuscripts due 30 September 2018
Guest Editors
Masoumeh Zareapoor

Lead Guest Editor
Shanghai Jiao Tong University
Shanghai, China
and
Shanghai Jiao Tong University
Institute of Pattern Recognition and Image Processing
Shanghai, China
E-mail: mzarea@sjtu.edu.cn

Jinchang Ren

University of Strathclyde
Department of Electronic and Electrical Engineering
Centre for Signal and Image Processing
Strathclyde Hyperspectral Imaging Centre
Glasgow, Scotland, UK
E-mail: jinchang.ren@strath.ac.uk

Huiyu Zhou

University of Leicester
Department of Informatics
Leicester, England, UK
E-mail: hz143@leicester.ac.uk

Wankou Yang

Southeast University
School of Automation
Nanjing, China
E-mail: wkyang@seu.edu.cn

Scope

In recent years, the analysis of hyperspectral images acquired by remote sensors has gained substantial attention and is becoming an increasingly active research discipline. HSI classification plays a key role in many applications; such as urban development, scene interpretation, monitoring of the earth's surface, etc. However, there are several challenges in hyperspectral data classification, including ultrahigh dimensionality of data, a limited number of labeled instances, and large spatial variability of spectral signature. These challenges degrade the ability to differentiate the pairwise distance between points, thus making it difficult to discriminate the most relevant features, resulting in the classification performance giving wrong or inaccurate results.

Deep learning approaches have shown promise to extract complex and discriminative features and competently learn their representations in a wide variety of computer vision tasks, including image classification, speech recognition, etc. Deep learning holds great promise to fulfill the challenging needs of remote sensing image processing. Interest of the remote sensing field toward deep learning models is growing fast, and many applications have been proposed to address the remote sensing problems. The goal of this special section is to develop and gain new ideas and technologies to facilitate the utility of hyperspectral imaging, and also explore its potential in various applications.

Applications of hyperspectral image (HSI) range from traditional remote sensing, such as mining and precision agriculture, into industry-based applications. Food and pharmaceutical quality inspection, medical applications, and even monitoring of the earth's surface are the examples of advanced HSI applications. As industrial demand increases, the need for more effective and appropriate data analysis techniques that can deal with such massive hyperspectral imagery becomes more pressing.

This special section aims to presents state-of-the-art algorithms and applications for HSI-based deep learning. Original papers that review and report on recent progress in this area or address potential solutions to the opening questions are also welcome. In this special section we will cover the following topics:

  • Novel deep learning architectures and algorithms designed for HSI analysis/classification
  • Feature learning from HSI using deep learning (contains feature extraction/selection dimensionality reduction)
  • Target extraction/detection from HSI using deep learning
  • Multisensor fusion with deep learning
  • Deep learning for large-scale remote sensing images
  • Deep learning model for high-resolution and image quality assessment
  • Compressive sensing, sparse representation, tensor decomposition
  • Deep learning for remote sensing image retrieval
  • Survey on emerging HSI processing and evaluation technologies.

To submit a manuscript for consideration, please prepare the manuscript according to the journal guidelines and submit the paper via the online submission system (https://jars.msubmit.net). A cover letter indicating that the submission is intended for this special section should be included with the paper. Papers will be peer reviewed in accordance with the journal's established policies and procedures. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within 6 weeks of manuscript submission. Special sections are opened online once a minimum of four papers have been accepted. Each paper is published as soon as the copyedited and typeset proofs are approved by the author.

hyperspectral
Previously Published Special Sections

Recent Advances in Earth Observation Technologies for Agrometeorology and Agroclimatology (April-June 2018)
Guest Editors: Shi-bo Fang, George P. Petropoulos, and Davide Cammarano

Improved Intercalibration of Earth Observation Data (January-March 2018)
Guest Editors: Craig Coburn and Aaron Gerace

Feature and Deep Learning in Remote Sensing Applications (October-December 2017)
Guest Editors: John E. Ball, Derek T. Anderson, Chee Seng Chan

Recent Advances in Geophysical Sensing of the Ocean: Remote and In Situ Methods (July-September 2017)
Guest Editors: Weilin Hou and Robert Arnone

Remote Sensing for Investigating the Coupled Biogeophysical and Biogeochemical Process of Harmful Algal Blooms (January-March 2017)
Guest Editors: Alan Weidemann and Ni-Bin Chang

Sparsity-Driven High Dimensional Remote Sensing Image Processing and Analysis (October-December 2016)
Guest Editors: Xin Huang, Paolo Gamba, and Bormin Huang

Advances in Remote Sensing for Renewable Energy Development: Challenges and Perspectives (2015)
Guest Editors: Yuyu Zhou, Lalit Kumar, and Warren Mabee

Onboard Compression and Processing for Space Data Systems (2015)
Guest Editors: Enrico Magli and Raffaele Vitulli

Management and Analytics of Remotely Sensed Big Data (2015)
Guest Editors: Liangpei Zhang, Qian (Jenny) Du, and Mihai Datcu

Remote Sensing and Sensor Networks for Promoting Agro-Geoinformatics (2014 and 2015)
Guest Editors: Liping Di and Zhengwei Yang

High-Performance Computing in Applied Remote Sensing: Part 3 (2014)
Guest Editors: Bormin Huang, Jiaji Wu, and Yang-Lang Chang

Airborne Hyperspectral Remote Sensing of Urban Environments (2014)
Guest Editors: Qian (Jenny) Du and Paolo Gamba

Progress in Snow Remote Sensing (2014)
Guest Editors: Hongjie Xie, Chunlin Huang, and Tiangang Liang

Advances in Infrared Remote Sensing and Instrumentation (2014)
Guest Editors: Marija Strojnik and Gonzalo Paez

Earth Observation for Global Environmental Change (2014)
Guest Editor: Huadong Guo

Advances in Onboard Payload Data Compression (2013)
Guest Editors: Enrico Magli and Raffaele Vitulli

Advances in Remote Sensing Applications for Locust Habitat Monitoring and Management (2013)
Guest Editors: Ramesh Sivanpillai and Alexandre V. Latchininsky

High-Performance Computing in Applied Remote Sensing: Part 2 (2012)
Guest Editors: Bormin Huang and Antonio Plaza

Advances in Remote Sensing for Monitoring Global Environmental Changes (2012)
Guest Editors: Yuyu Zhou, Qihao Weng, Ni-Bin Chang

High-Performance Computing in Applied Remote Sensing: Part 1 (2011)
Guest Editors: Bormin Huang and Antonio Plaza

Satellite Data Compression (2010)
Guest Editor: Bormin Huang

Remote Sensing for Coupled Natural Systems and Built Environments (2010)
Guest Editor: Ni-Bin Chang

Remote Sensing Applications to Wildland Fire Research in the Eastern United States: Selected Papers from the 2007 EastFIRE Conference - Part 2 (2009)
Guest Editors: John J. Qu and Stephen D. Ambrose

Remote Sensing of the Wenchuan Earthquake (2009)
Guest Editor: Huadong Guo

Remote Sensing Applications to Wildland Fire Research in the Eastern United States: Selected Papers from the 2007 EastFIRE Conference (2008)
Guest Editors: John J. Qu and Stephen D. Ambrose

Aquatic Remote Sensing Applications in Environmental Monitoring and Management (2007)
Guest Editors: Vittorio E. Brando and Stuart Phinn

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