Aims & Scope

The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.

Special Issue on Advances in Remote Sensing Applications for Local Climate Zone Characterization in Urban Environments
截稿日期: 2024-12-31

This Special Issue aims to showcase the latest developments in the application of remote sensing techniques for Local
Climate Zone (LCZ) characterization in urban environments. LCZ classification provides a comprehensive framework
for understanding the local climate and microclimatic variations within urban environments. This Special Issue seeks
to explore the diverse applications of remote sensing in capturing, monitoring, and analyzing the dynamic features of
LCZs to improve our understanding of urban climate dynamics and enhance sustainable urban development.
We invite researchers to submit original research articles, reviews, and technical notes that address the application of
remote sensing in LCZ characterization. Manuscripts should present innovative methodologies, explore theoretical
frameworks, and showcase practical applications that contribute to the advancement of understanding local climate
dynamics in urban environments, especially in the following topics:

 Validation and accuracy assessment of LCZ classification using remote sensing data.
 Novel remote sensing methods for LCZ classification and mapping.
 High temporal resolution remote sensing monitoring, in-situ monitoring, and mobile monitoring for LCZ.
 Integration of multi-sensor remote sensing data for detailed LCZ characterization.
 Remote sensing contributions to urban energy balance modeling within LCZ contexts.
 Remote sensing applications for assessing green spaces and water bodies in different LCZs.
 Coupling multi-sensor remote sensing data to analyze the exposure risk of LCZ.
 Applications of machine learning and artificial intelligence in remote sensing-based LCZ studies.

Schedule
May 1, 2024, Submission window opening
December 31, 2024, Submission window closing

Format
All submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society guidelines.
Submitted articles should not have been published or be under review elsewhere. Submit your manuscript on
http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the “Advances in Remote
Sensing Applications for Local Climate Zone Characterization in Urban Environments” special issue
manuscript type. Prospective authors should consult the site https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for guidelines and information on submission.
All submissions must be formatted using the IEEE standard format (double column, single spaced). Please visit
http://www.ieee.org/publications_standards/publications/authors/author_templates.html to download a template for
transactions. Please note that as of Jan.1, 2024, IEEE J-STARS, being a fully open-access journal since 2020,
charges a flat publication fee $1,496 per paper.

Guest Editors
Jun Yang, Northeastern University, China (yangjun8@mail.neu.edu.cn)
Xiangming Xiao, University of Oklahoma, USA (xiangming.xiao@ou.edu)
Zhi Qiao, Tianjin University, China (qiaozhi@tju.edu.cn)
Xiao Liu, South China University of Technology, China (xiaoliu@scut.edu.cn)

Special Issue on Street View Imagery and GeoAI
截稿日期: 2024-12-31

The advent of GeoAI and the proliferation of street view imagery have revolutionized our understanding of urban micro-
environments. Unlike remote sensing data, street view imagery (Not only sourced from commercial companies, but also
potentially obtained through crowdsourcing source such as social media and Volunteered Geographic Information)
offers a close-up perspective, providing detailed, ground-level insights into cityscapes. This imagery enables researchers
to conduct finely-grained, time-series analyses of environmental features. This Special Issue seeks to explore these
unique advantages of street view imagery over traditional remote sensing approaches in urban analysis.

This Special Issue aims to showcase innovative research that utilizes street view imagery for intelligent perception of
urban micro-environments. We invite contributions that apply GeoAI methods to address various urban issues. The
broad topics include (but are not limited to):

 Urban functional zone identification, jointly observed from the aerial perspective of remote sensing imagery and the close-up perspective of street view imagery
 Urban resilience, jointly observing urban communities using multiple sensors and data sources to measure differences in resilience in the face of disasters;
 Health geography applications, urban ecological, and green space analysis;
 Urban safety perception, fine exploration of urban crime spatiotemporal patterns;
 Smart transportation, evaluating traffic patterns based on environmental features to improve urban mobility.
 Cultural heritage protection, using temporal imagery to record and identify changes in the built environment.
 Real estate analysis, assessing building age, style, energy consumption, and valuation, etc.

This special issue focuses on the joint observation of urban close-up imagery and remote sensing imagery, with at least
one type of image observation data present. Studies demonstrating innovative methods and practical impacts on urban
planning, policy-making, and environmental monitoring are especially welcome.

We welcome original research articles, reviews, case studies, and technical reports that detail the methodology, data
analysis, and outcomes, highlighting the practical application and potential impact on urban micro-environment sensing.
Submissions should clearly articulate the significance of street view imagery in urban studies and showcase innovative
analytical techniques.

Schedule
Jun 01, 2024, Submission system opening
Dec 31, 2024, Submission system closing

Format
All submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society guidelines.
Submitted articles should not have been published or be under review elsewhere. Submit your manuscript on
http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the “Street View Imagery and
GeoAI” special issue manuscript type. Prospective authors should consult the site https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for guidelines and information on paper submission. All submissions must be formatted using the IEEE standard format (double column, single spaced). Please visit http://www.ieee.org/publications_standards/publications/authors/author_templates.html to download a template for transactions. Please note that since Jan. 1, 2024, IEEE J-STARS, as a fully open-access journal, is charging a flat publication fee $1,496 per paper.

Guest Editors
Yan Zhang, The Chinese University of Hong Kong, Hong Kong SAR (yanzhang@cuhk.edu.hk)
Mei-Po Kwan, The Chinese University of Hong Kong, Hong Kong SAR (mpkuan@cuhk.edu.hk)
Nengcheng Chen, China University of Geosciences (Wuhan), China (chennengcheng@cug.edu.cn)
Marco Helbich, Utrecht University, Netherlands (m.helbich@uu.nl)
PeiXiao Wang, Institute of Geographic Sciences and Natural Resources Research, CAS, China (wpx@lreis.ac.cn)

Special Issue on Sensing Wetlands from Space: Current Status and Future Solutions
截稿日期: 2024-12-31

Wetlands are pivotal in conserving water sources, regulating hydrology, sustaining biodiversity, ensuring regional
ecological security, and contributing to global carbon storage and climate regulation. Often described as the Earth's
'kidneys,' 'bird havens,' 'biological supermarkets,' 'genetic reservoirs,' 'natural sponges,' and 'urban oxygen bars,' they
embody the planet's most valuable ecosystem. Despite occupying less than 8% of global land area, wetlands deliver
around 40% of world's ecosystem services (supply, regulation, culture) and hold over 30% of terrestrial carbon.
However, their importance is often underrecognized. Coupled with socioeconomic development and climate change
impacts, global wetlands are shrinking and degrading.

Wetlands are dynamic, complex ecosystems, varying across landscapes, regions, and climates. Accurate, timely
monitoring is essential for understanding their dynamics, assessing environmental changes, and devising effective
conservation strategies. Since the 1970s, remote sensing (RS) technologies have become a vital tool for observing,
detecting, analyzing, and evaluating wetlands at diverse spatial and temporal scales. However, challenges persist in RS
applications for wetland research due to variability in classifications, complex structures, and shifting boundaries.
This Special Issue invites submissions of review and research papers that explore RS data, products, techniques,
methods, and models tailored for sensing various types, locations, and scales of wetlands.

The broad topics include (but are not limited to):
 Big RS data applications for wetlands;
 Advanced machine learning for wetland classification and mapping;
 Utilizing Hyperspectral, SAR, LiDAR, and UAV technologies in wetland studies;
 Spatiotemporal dynamics of wetlands;
 RS in measuring carbon emissions (e.g., CO2, CH4) and storage in wetlands;
 Hydrological studies of wetlands via RS;
 Investigating wetland vegetation structure and phenology through RS.

Schedule
Jun. 1, 2024, Submission system opening
Dec. 31, 2024, Submission system closing

Format
All submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society guidelines.
Submitted articles should not have been published or be under review elsewhere. Submit your manuscript on
http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the “Sensing Wetlands from
Space: Current Status and Future Solutions” special issue manuscript type. Prospective authors should consult the
site https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for guidelines and information on paper
submission. All submissions must be formatted using the IEEE standard format (double column, single spaced). Please
visit http://www.ieee.org/publications_standards/publications/authors/author_templates.html to download a template
for transactions. Please note that since Jan. 1, 2024, IEEE J-STARS, as a fully open-access journal, is charging a flat
publication fee $1,496 per paper.

Guest Editors
Alim Samat, Xinjiang Institute of Ecology and Geography, CAS, China, (alim_smt@ms.xjb.ac.cn)
Hongtao Duan, Nanjing Institute of Geography and Limnology, CAS, China (htduan@niglas.ac.cn)
Weiguo Jiang, Beijing Normal University, China (jiangweiguo@bnu.edu.cn)
Dehua Mao, Northeast Institute of Geography and Agroecology, CAS, China (maodehua@iga.ac.cn)
Tim Van de Voorde, Ghent University, Belgium (tim.vandevoorde@ugent.be)
Antonio Plaza, University of Extremadura, Spain (aplaza@unex.es)

Special Issue on Efficient Fusion of Multi-Source Remote Sensing Data
截稿日期: 2024-12-31

Fusion of multi-source remote sensing data, including multi-temporal, multi-angle, multi-spectral, and active and
passive sensing data, is an effective approach to improve the accuracy and robustness of remote sensing. In the last
decades, the remote sensing industries of countries around the world have been developed rapidly, with a significantly
increasing number of remote sensing sensors and multi-source remote sensing data explosion. The traditional
implementation of fusion of multi-source remote sensing data includes data acquisition, downlink transmission, and
ground-based data fusion processing, which results in low efficiency of information perception and decision-making,
especially for urgent Earth observation tasks.

The recent development of space information network and the trend of deployment of edge computing devices at remote
sensing sensors provide the opportunity to achieve the efficient multi-source remote sensing data fusion and real-time
information perception and decision-making. However, most of existing high-performance multi-source remote sensing
data fusion methods depend on powerful computation devices because of massive number of model parameters and
complicated model structures therein. This limits the implementations of existing high-performance fusion methods on
edge computing devices, like satellites or unmanned aerial vehicles. In addition, the classical lightweight fusion methods
often suffer from obvious performance degradation. As a result, it is necessary to study the topic of efficient fusion of
multi-source remote sensing data.

This special issue aims to collect outstanding contributions of recent state-of-the-art methods and hardware-
implementations of efficient fusion of multi-source remote sensing data and will provide a platform to promote
interdisciplinary research across remote sensing, information fusion, real-time processing and artificial intelligence. The
broad topics include (but are not limited to):

 Efficient multi-source fusion imaging
 Efficient remote sensing image registration
 Knowledge-guided and efficient multi-source remote sensing data fusion
 Efficient object detection and recognition based on multi-source remote sensing data
 Efficient change detection based on multi-source remote sensing data
 Efficient and online training strategies for multi-source remote sensing data fusion models
 Neural architecture search methods for efficient fusion
 Efficient fusion of multi-source remote sensing data utilizing foundation models
 Efficient hardware implementations of efficient fusion methods

Schedule
March 1st, 2024, Submission system opening
December 30st, 2024, Submission system closing

Format
All submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society guidelines.
Submitted articles should not have been published or be under review elsewhere. Submit your manuscript on
http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the “Efficient Fusion of
Multi-Source Remote Sensing Data” special issue manuscript type. Prospective authors should consult the site
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for guidelines and information on paper
submission. All submissions must be formatted using the IEEE standard format (double column, single spaced). Please
visit http://www.ieee.org/publications_standards/publications/authors/author_templates.html to download a template for
transactions. Please note that as of Jan. 1, 2020, IEEE J-STARS has become a fully open-access journal charging a flat
publication fee $1,496 per paper.

Guest Editors
Xudong Kang, Hunan University, China (xudong_kang@163.com)
Liang Chen, Beijing Institute of Technology, China (chenl@bit.edu.cn)
Yu Liu, Naval Aviation University, China (liuyu77360132@126.com)
Xueqian Wang, Tsinghua University, China (wangxueqian@mail.tsinghua.edu.cn)
Puhong Duan, Hunan University, China (puhong_duan@hnu.edu.cn)
Ruben Fernandez-Beltran, University of Murcia, Spain (raybenfb@gmail.com)
Antonio Plaza, University of Extremadura, Spain (aplaza@unex.es)

Special Issue on Recent Advances in China’s Gaofen Satellite Applications: Challenges and Opportunities
截稿日期: 2024-12-31

The first Gaofen satellite, GF-1 was launched on April 26, 2013. Since then, a series of Gaofen satellites, from GF-1 to
GF-7 have been launched. The latest GF-7 was successfully launched on November 3, 2019, and is currently in operation.
The spectral bands cover ultraviolet, visible, near-infrared, shortwave-infrared, thermal infrared, and microwave. The
spatial resolution ranges from sub-meter, meter, ten-meter, and hundred-meter to kilometer levels. Currently, Gaofen
series products include 1) standard products (level 0 to level 2), 2) common products (level 3 to level 5), and 3) specific
products (levels 6 and 7). These products can provide a continuous supply of high-quality remote sensing data with a high
spatial, temporal, and spectral resolution for various scientific research and industry applications (including national land
surveying, resource exploration, environmental monitoring, agricultural yield estimation, etc.). This special issue aims to
provide a synthesized overview of the latest advances and challenges related to Gaofen series data/products, which can
serve as the directional support for applications of Gaofen series data in multiple fields.

The broad topics include (but are not limited to):
 Receiving/generation/retrieval of Gaofen series data/products: raw data, GaoFen standard, common and specific
products
 Image processing and analysis of Gaofen series products: fusion/integration, registration, classification, target
detection, feature extraction, change detection, 3D reconstruction
 Calibration of Gaofen products: vicarious, cross-calibration, automatic radiometric calibration
 Validation of Gaofen products: field measurement, accuracy evaluation datasets, quantitative assessment
 Applications of Gaofen satellites: natural resource management (e.g. forest, water, wetland, soil), agricultural
monitoring, sustainable urban development

Schedule
January 1, 2024 Submission system opening
December 31, 2024 Submission system closing

Format
All submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society guidelines. Submitted articles should not have been published or be under review elsewhere. Submit your manuscript on http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the “Recent Advances in China’s
Gaofen Satellite Applications: Challenges and Opportunities” special issue manuscript type. Prospective authors
should consult the site https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for guidelines and information
on paper submission. All submissions must be formatted using the IEEE standard format (double column, single spaced).
Please visit http://www.ieee.org/publications_standards/publications/authors/author_templates.html to download a template for transactions. Please note that as of Jan. 1, 2020, IEEE J-STARS has become a fully open-access journal charging a flat publication fee $1,496 per paper.

Guest Editors
Yongchao Zhao, Professor, Aerospace Information Research Institute, CAS, China (zhaoyc@aircas.ac.cn)
Luyan Ji, Assistant Professor, Aerospace Information Research Institute, CAS, China (jily@mail.ustc.edu.cn)
Hongsheng Zhang, Assistant Professor, The University of Hong Kong, Hong Kong (zhanghs@hku.hk)
Wenyi Zhang, Associate Professor, Aerospace Information Research Institute, CAS, China (wyzhang@aircas.ac.cn)
Suhong Liu, Professor, Beijing Normal University, China (liush@bnu.edu.cn)
Guli Jiapaer, Xinjiang Institute of Ecology and Geography, CAS, China (glmr@ms.xjb.ac.cn)

Special Issue on Fusion and Inference of Multi-modal Ocean Observation and Remote Sensing Data
截稿日期: 2024-12-31

With the continuous development of ocean observation and monitoring technologies and the ongoing
improvement of the global ocean observation network, the variety and scale of ocean observation and remote
sensing data have seen rapid growth. Marine science big data, primarily sourced from observations and
monitoring, especially remote sensing, serves as an effective tool for understanding key processes in ocean
physics, chemistry, biology, and more. With the support of artificial intelligence technologies, leveraging
marine science big data for scientific perception, understanding, and prediction has become an important
means to overcome traditional limitations. Furthermore, physical information neural networks are employed
to predict and forecast the spatio-temporal evolution of ocean meteorology and marine environments.
While integrating marine observation data and artificial intelligence methods has already brought about
new advancements in traditional ocean science, the diversity of sources and acquisition methods of ocean big
data has led to challenges in data fusion, cross-modal inference, composite computation, and collaborative
applications. These challenges arise from the multi-modal characteristics of ocean observation data,
including differences in spatio-temporal scales, uneven spatial distributions, and variations in sample
distributions.

This special issue focuses on ocean observation and remote sensing data to address the challenges posed
by the multi-modal characteristics of ocean observation and remote sensing big data in data fusion, inference,
computation, and application. The aim is to advance the core issues of artificial intelligence and big data
technologies in marine science applications, promote the development of tools and methodologies for ocean
science cognition, and enhance the theoretical research and technical system of artificial intelligence in
oceanography.

This special issue primarily revolves around the multi-modal issues of marine observation big data, and
the topics include but are not limited to:
 Identification, content understanding, classification, and segmentation of multi-modal remote sensing
images in the ocean domain.
 Fusion techniques for multi-modal ocean observation and remote sensing data.
 Completion of spatio-temporal data fields in multi-modal ocean observation and remote sensing datasets.
 Cross-modal retrieval of textures in ocean remote sensing images.
 Physics-guided multi-modal big data prediction and forecasting in the ocean domain.
 Cognition of ocean processes and phenomena based on multi-modal observation and remote sensing data.
 Predicting and forecasting typical ocean disasters based on multi-modal observation and remote sensing data.
 Knowledge-driven collaborative inference of multi-modal ocean data.

Schedule
Submission Date Start: 01 Jan 2024
Submission Date End: 31 Dec 2024

Format(to be modified)
All submissions will be peer-reviewed according to the IEEE Geoscience and Remote Sensing Society
guidelines. Submitted articles should not have been published or be under review elsewhere. Submit
your manuscript on using the Manuscript Central interface, and select the “Fusion and Inference of Multi-
modal Ocean Observation and Remote Sensing data” special issue manuscript type. Prospective authors
should consult the site: https:// ieeexplore. ieee. org/stamp/stamp. jsp?tp=& arnumber= 88 5 5 0 3 9 for guidelines
and information on paper submission. All submissions must be formatted using the IEEE standard format
(double-column, single-spaced).
Please visit http://www.ieee.org/publications_standards/publications/authors/author_templates.html to
download a template for transactions. Please note that as of Jan. 1, 2020, IEEE J-STARS has become a
fully open-access journal charging a flat publication fee of $1, 496 per paper.

Guest Editors:
Xiaofeng Li, Institute of Oceanology, Chinese Academy of Sciences, China (Xiaofeng.Li@ieee.org)
Jie Nie, Ocean University of China, China (niejie@ouc.edu.cn)
Martin Gade, University of Hamburg, Germany (martin.gade@uni-hamburg.de)
Ferdinando Nunziata, University of Naples, Italy, (ferdinando.nunziata@uniparthenope.it)

Special Issue on Gravity Satellite Systems, Data Processing, and Applications
截稿日期: 2024-12-31

The field of geodesy has made significant progress in recent decades with the development of advanced satellite
gravimetry technologies. As a result, satellite gravimetry missions such as GRACE, GRACE-FO, and GOCE have
provided invaluable insights into the Earth’s gravity field. Numerous theories have been developed and successfully
applied to use these valuable datasets for enhancing our understanding and knowledge about the Earth’s interior
structures and processes. We believe that the observations from GRACE, GRACE-FO, and GOCE missions can be
further utilized for various geophysical purposes. However, there remain numerous unresolved issues and emerging
challenges that necessitate further investigation.

This special issue will aim to address these challenges while providing a platform for researchers and practitioners to
share their latest findings, methodologies, and technologies related to gravity satellite systems, data processing, and
innovative applications. The broad topics include (but are not limited to):
• Satellite gravimetry data processing and global gravity field solutions
• Integration of gravity data with other geophysical datasets
• Simulations of next-generation satellite gravimetry missions
• Satellite gravity applications to hydrology, glaciology, solid earth deformation, oceanography, and atmosphere
Other related topics

Schedule
01 Dec 2023 Submission system opening
31 Dec 2024 Submission system closing

Format
All submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society guidelines.
Submitted articles should not have been published or be under review elsewhere. Submit your manuscript on
http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the “Gravity Satellite
Systems, Data Processing, and Applications” special issue manuscript type. Prospective authors should consult the
site https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for guidelines and information on paper
submission. All submissions must be formatted using the IEEE standard format (double column, single spaced). Please
visit http://www.ieee.org/publications_standards/publications/authors/author_templates.html to download a template for
transactions. Please note that as of Jan. 1, 2020, IEEE J-STARS has become a fully open-access journal charging a flat
publication fee $1,250 perpaper.

Guest Editors
Qiujie Chen Tongji University, China (qiujiechen@tongji.edu.cn)
Taoli Yang University of Electronic Science and Technology of China, China (yangtl@uestc.edu.cn)
Tianhe Xu Shandong Univeristy, China (thxu@sdu.edu.cn)
C.K. Shum The Ohio State University, USA (ck.shum@outlook.com)

Special Issue on Recent Advances in Remote Sensing Image Super-Resolution for Earth Observation
截稿日期: 2024-12-31

High-resolution remote sensing images are crucial for a wide range of earth observation applications, including urban
planning, land cover classification, agricultural monitoring, disaster management, and environmental analysis.
However, due to limitations in imaging sensors and transmission capabilities, remote sensing images often suffer from
low spatial resolution in practical. To overcome the challenge of acquiring high-resolution imagery with sufficient
coverage and frequency, remote sensing image super-resolution (SR) techniques have emerged as a promising solution,
by enhancing the spatial resolution of the existing remote sensing images to meet the increasing demand for detailed
and accurate information. In recent years, significant progress has been made in the field of remote sensing image
super-resolution, fueled by advancements in deep learning, image processing, and computer vision. These techniques
offer promising solutions to generate high-resolution images from low-resolution inputs, providing valuable insights
for earth observation applications.

By exploring the recent advances in remote sensing image super-resolution and their applications in earth observation,
this special issue aims to present state-of-the-art techniques, address challenges, and explore novel approaches for
enhancing the spatial resolution of remote sensing images, ultimately improving the accuracy and quality of remote
sensing data analysis for earth observation applications.

The broad topics include (but are not limited to):
 Deep learning-based super-resolution methods for remote sensing images
 Multi-modal remote sensing image super-resolution techniques
 Fusion of remote sensing data with auxiliary information for super-resolution
 Domain adaptation and transfer learning for remote sensing image super-resolution
 Reconstruction and restoration of multi/hyperspectral remote sensing images
 Integration of super-resolution with object detection and recognition in remote sensing
 Super-resolution for remote sensing video sequences
 Quality assessment and evaluation metrics for super-resolved remote sensing images
 Applications of remote sensing image super-resolution in environmental monitoring, land cover classification,
agriculture, urban planning, and disaster management

Schedule
01 Apr 2024 Submission system opening
31 Dec 2024 Submission system closing

Format
All submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society guidelines.
Submitted articles should not have been published or be under review elsewhere. Submit your manuscript on
http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the “Recent Advances in
Remote Sensing Image Super-Resolution for Earth Observation” special issue manuscript type. Prospective
authors should consult the site https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for guidelines
and information on submission. All submissions must be formatted using the IEEE standard format (double column,
single spaced). Please visit http://www.ieee.org/publications_standards/publications/authors/author_templates.html to
download a template for transactions. Please note that as of Jan. 1, 2024, IEEE J-STARS, being a fully open-access
journal since 2020, charges a flat publication fee $1,496 per paper.

Guest Editors
Yang Li Shihezi University, China (liyang328@shzu.edu.cn)
Qian Shi Sun Yat-sen University, China (shixi5@mail.sysu.edu.cn)
Muhammad Khurram Khan King Saud University, Saudi Arabia (mkhurram@ksu.edu.sa)
Mohammad Kamrul Hasan Universiti Kebangsaan Malaysia, Malaysia (mkhasan@ukm.edu.my)

Special Issue on Foundation and Large Vision Models for Remote Sensing
截稿日期: 2025-01-31

In recent years, foundation models have emerged as a powerful framework that can be adapted for a variety of
downstream vision tasks. In the arena of remote sensing, prior work has been focused on task-specific models that
are optimized for specific tasks at hand (e.g. precision agriculture, target recognition, object detection etc. from
specific sensors). There is significant and emergent interest in developing and deploying task-agnostic generalized
models that can be tailored for various downstream tasks. Likewise, there is a strong interest in deploying vision
language models for remote sensing. This special issue will provide an avenue for researchers working at the
intersection of foundation models, large vision models and earth observation applications to contribute their latest
research. Topics include (but are not limited to):

● Foundation Models, Large Vision Language Models and Large Multi-Modal Models in Remote Sensing
● Discriminative and Generative Models
● Training of Large Vision Models (e.g. masked image modeling, new datasets, and benchmarks)
● Deploying Large Vision Models for downstream tasks (e.g. segmentation, classification, regression, object detection, counting, change detection etc.)
● Adaptation strategies, prompt tuning and visual instruction tuning
● Few-shot and Continual learning
● Open-set recognition and classification
● Applications to Remote Sensing and Earth Observations
● Applications to multi-sensor and multi-temporal datasets

Schedule
07-01-2024 Submission system opening
01-31-2025 Submission system closing

Format
All submissions will be peer-reviewed according to the IEEE Geoscience and Remote Sensing Society
guidelines. Submitted articles should not have been published or be under review elsewhere. Submit your
manuscript on http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the
“Foundation and Large Vision Models for Remote Sensing” special issue manuscript type. Prospective authors
should consult the site https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for guidelines and
information on paper submission.
All submissions must be formatted using the IEEE standard format (double column, single spaced). Please visit
http://www.ieee.org/publications_standards/publications/authors/author_templates.html to download a template
for transactions. Please note that since Jan. 1, 2024, IEEE J-STARS, as a fully open-access journal, is charging a
flat publication fee of $1,496 per paper.

Guest Editors
Saurabh Prasad University of Houston, USA (sprasad2@uh.edu)
Biplab Banerjee Indian Institute of Technology, Bombay (bbanerjee@iitb.ac.in)
Salman Khan MBZ University of Artificial Intelligence (salman.khan@mbzuai.ac.ae)
Levente Klein IBM (kleinl@us.ibm.com)

Special Issue on Challenges and Recent Progress in Remote Sensing of Nighttime Light
截稿日期: 2025-01-31

Satellite-recorded city light at night is able to reflect human acitvities, socioecnomic dynamics and light pollution.
Remote sensing of nighttime light was orginated in 1970s, and it has rapidly grown since time series DMSP/OLS
products were published by NOAA in 2010. In the last decade, the on-orbit satellites recording nighttime light are
becoming highly diversed, with new satellites including Suomi-NPP, NOAA-20, NOAA-21, FY-3E, Luojia-1,
SDGSAT-1, Yangwang-1 as well as commercial satellites such as EROS-B and Jilin-1. These satellites provide a
variety night-time light images at different spatial resolutions with some of them owning multi-spectral bands.
Thereby, the application of nighttime light images have expanded from mapping urbanization to much broader
domains such as estimating regional economy, monitoring fishery, evaluating disasters and mapping light
pollution. With more kinds of data with rich information, challenges in exploring these information from nighttime
light remote sensing are more obivious. For example, mechanism behind uncertainty of daily nighttime light data
need more clarficiation although the angular effect of nighttime light has been discovered. It is also interesting to
see deep learning has been adopted to explore knowledge from the data at different spatial resolutions, while
limited training samples from economic statistics may make the learning process less reliable. In sum, we can infer
that the remote sensing of nighttime light is still developed in early stages, and theories and techniques are
urgently needed for improving data quality and different application fields. As a result, this special issue aims at
sharing research ideas with solid analysis to promote the advances of nighttime light remote sensing.

The broad topics include (but not limited to)
1) Characteristics of new emerging images of nighttime light such as SDGSAT-1 and data acquired by drones, balloons as well as in-situ camera.
2) Preprocessing nighttime light data such as radiometric calibration, denoising and super resolution reconstruction.
3) New techniques, such as deep learning and econometric models, in data mining of nighttime light data.
4) Application of nighttime light data in tracking Sustainable Development Goals (SDGs) such as erdicatication of poverty, electrification and economic growth.
5) Application of nightime light data in monitoring light pollution at night.
6) Exploring multispectral/hyperspectral images of nighttime light.
7) New methodology of nightime light data in human activity multidimensional representation in the process of urbanization.

Schedule
Jun 1, 2024, Submission system opening
Jan 31, 2025, Submission system closing

Format
All submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society guidelines.
Submitted articles should not have been published or be under review elsewhere. Submit your
manuscript on http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the
“Challenges and Recent Progress in Remote Sensing of Nighttime Light” special issue manuscript type.
Prospective authors should consult the site https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=xxxxxx
for guidelines and information on paper submission. All submissions must be formatted using the IEEE
standard format (double column, single spaced). Please visit
http://www.ieee.org/publications_standards/publications/authors/author_templates.htmlto download a template for
transactions. Please note that since Jan. 1, 2024, IEEE J-STARS, as a fully open-access journal, is charging a flat
publication fee $1,496 per paper.

Guest Editors
Kaifang Shi, Anhui Normal University, China (shikf@ahnu.edu.cn)
Gang Xu, Wuhan University, China (xugang@whu.edu.cn)
Zuoqi Chen, Fuzhou University, China (zqchen@fzu.edu.cn)
Yuanzheng Cui, Hohai University (ryancyz@hhu.edu.cn)

Special Issue on Applications of Remote Sensing Techniques in Forest Mensuration
截稿日期: 2025-02-28

Forest mensuration is the key to gathering data and information on forest resources for forest planning and adaptive
management. Fully developed forest mensuration schemes and technologies help us to formulate appropriate forest
rules and regulations for sustainable forest management and support forest product needs. Taking advantage of state-
of-the-art remote sensing technologies, forest information including tree-level parameters, stand-level attributes and
structures, and ecosystem services can be measured or retrieved through UAV, airborne, and spaceborne platforms
with massive remote sensing data (including high-resolution optical images, SAR, LiDAR, social media data).
Reliable data collection and analysis enable forest societies to conduct integrity procedures involving forest
measurement, reporting, and validation (the MRV processes) with global consistency.

This Special Issue intends to highlight the significance of applying big remote sensing data and processing techniques
to gather accurate forest information on MRV processes in plantation forests, secondary forests, and pristine forests.
Techniques for retrieving tree parameters, stand attributes, and the structure of forest ecosystems for tropical,
temperate, and boreal ecoregions are encouraged. Recent theoretical and application results related to “remote sensing
for Forest Mensuration” from the perspectives of theories, algorithms, architectures, and applications, such as the
application of remote sensing data (including RGB, multispectral, and hyperspectral images, LiDAR, SAR, etc.) from
multiple platforms (including UAV, airborne, and spaceborne, social media) at variant forest scales are welcome.

The broad topics include (but are not limited to):
● Multi-platforms (UAV/Airborne/Spaceborne/social median) sensing technology for forest mensuration;
● Data (color, spectral, SAR, LiDAR) processing (calibration, feature extraction, data fusion, classification, mapping, etc.);
● Tree parametrization;
● Stand attributes’ estimation;
● Species and forest type mapping;
● Stand dynamics;
● Forest degradation diagnosing;
● Plantation precision management;
● Secondary forest management;
● Ecosystem productivity;
● Adaptive management of forest ecosystems.

Schedule
June 1, 2024: Submission system opening
February 28, 2025: Submission system closing

Format
All submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society guidelines.
Submitted articles should not have been published or be under review elsewhere. Submit your manuscript on
http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the “Applications of Remote
Sensing Techniques in Forest Mensuration” special issue manuscript type. Prospective authors should consult the
site https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for guidelines and information on paper
submission. All submissions must be formatted using the IEEE standard format (double column, single spaced). Please
visit http://www.ieee.org/publications_standards/publications/authors/author_templates.html to download a template
for transactions. Please note that since Jan. 1, 2024, IEEE J-STARS, as a fully open-access journal, is charging a flat
publication fee $1,496 per paper.

Guest Editors
Porf. Chinsu Lin, National Chiayi University, Chiayi, Taiwan (chinsu@mail.ncyu.edu.tw)
Prof. Wenzhi Liao, Ghent University, Belgium (wenzhi.Liao@ugent.be)
Dr. Akemi Itaya, Mie University, Japan (itaya@bio.mie-u.ac.jp)
Dr. Hee Han, Seoul National University, Korea (hee.han@snu.ac.kr)

Special Issue on Deep Generative Models for Multi-Sensor Image Fusion and Reconstruction for Earth observation and monitoring
截稿日期: 2025-02-28

Multi-sensor systems in hyperspectral/multispectral and SAR imaging are crucial for expanding spectral coverage, improving spectral and spatial resolutions, providing flexibility for various applications, ensuring redundancy and reliability, and enabling real-time imaging capabilities. The fusion and reconstruction of multi-sensor images acquired from different sensors can capture a wider and shorter range of spectral information, enhance the accuracy of spectral analysis, adapt to specific requirements, mitigate sensor limitations, and enable dynamic data acquisition. These benefits enhance the potential for in-depth analysis and interpretation of multi-sensor data, making their configurations indispensable for numerous environmental applications, and hyperspectral/multispectral and SAR imagery become suitable to be successfully used in various fields of remote sensing.

However, the current approaches face challenges in accurately integrating the diverse spectral information and spatial details captured by different sensors. Additionally, the limited availability of ground truth data for training and evaluation further hinders the development of robust fusion and reconstruction techniques. Deep learning models play an important role in addressing these challenges and acts as a bridge to provide data intensive information. In particular, deep generative models such as variational autoencoders and Generative Adversarial Networks (GANs), have shown promise in capturing complex data distributions and generating high-quality images. By leveraging these models, it is possible to develop novel approaches for fusing and reconstructing multi-sensor images that better preserve spectral fidelity, spatial details, and statistical characteristics.

The broad topics include (but are not limited to):
 Remote sensing based applications of Deep Generative Models in Environmental, Land, Sea, Ocean Monitoring, Agriculture, etc.
 Deep generative approaches for enhanced hyper/multi-spectral image fusion and reconstruction.
 Deep Learning-Based Real-time Analysis of hyper/multi-spectral Time Series Data for environmental monitoring.
 Data Augmentation and enhancement to increase the benefits of Deep Learning in the creation of benchmark datasets.
 Domain Adaptation Techniques for hyper/multi-spectral Image Fusion
 Enhancement of spectral and spatial details through deep generative models.
 Generating long-term trends on environmental changes like Automated Dehazing, ocean and sea water monitoring, fire detection, etc.
 Generative Adversarial Networks for 3D Scene Reconstruction
 Graph-Based Approaches for Multispectral Image Fusion and Reconstruction
 Multi-exposure image fusion method based on GAN
 Multi-modal data fusion techniques using deep generative models for integrating remotely sensed data from different sensors.
 Transfer Learning Approaches for Hyperspectral/Multispectral Image Reconstruction
 User defined Conditional for Targeted Image Synthesis
 Deep generative models for multi/hyperspectral Image Synthesis
 Variational Autoencoders (VAE) for Hyperspectral Image segmentation and classification.

Schedule
Jul 1, 2024 - Submission system opening
Feb 28, 2025 - Submission system closing

Format
All submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society guidelines. Submitted articles should not have been published or be under review elsewhere. Submit your manuscript on http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the “Deep Generative Models for Multi-Sensor Image Fusion and Reconstruction for Earth observation and monitoring” special issue manuscript type. Prospective authors should consult the site https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for guidelines and information on paper submission. All submissions must be formatted using the IEEE standard format (double column, single spaced). Please visit http://www.ieee.org/publications_standards/publications/authors/author_templates.html to download a template for transactions. Please note that since Jan. 1, 2024, IEEE J-STARS, as a fully open-access journal, is charging a flat publication fee $1,496 per paper.

Guest Editors
Dr. C. Krishna Mohan Indian Institute of Technology Hyderabad, India (ckm@cse.iith.ac.in)
Prof. Silvia Liberata Ullo University of Sannio, Italy (silvullo@unisannio.it)
Dr. Linga Reddy Cenkeramaddi University of Agder, Norway (linga.cenkeramaddi@uia.no)
Dr. Miguel Garcia-Torres Pablo de Olavide University, Spain (mgarciat@upo.es)
Dr. Rajeshreddy Datla ISRO, India. (rajesh@adrin.res.in)

Special Issue on Sensing and Remote Sensing in the Poles
截稿日期: 2025-02-28

The poles play a crucial and major role in the climate cycle of the earth. Recently, polar regions are undergoing
catastrophic changes due to the amplification effect of global warming. For example, melting glaciers, rising sea
levels, disruption in ocean currents etc. In addition, the polar regions also play a major role in maritime trade which
also needs precise monitoring of the conditions in the seas in the polar regions.
Hence, measurements in the polar regions are gaining growing importance. In addition to the use of satellite sensing,
the use of in-situ sensors is also becoming ubiquitous both in the Arctic as well as in the Antarctic. This special issue
will aim to consolidate work from across the globe aimed at sensing the poles (both water and land bodies). We
encourage work around the development of new sensing techniques, new sensors, new models as well as new
algorithms to this effect.

The broad topics include (but are not limited to):
● Use of satellites in polar observation
● Use of ice buoys in polar observation
● Radar for polar observation
● Safety in the poles
● New methods of sensing (like penetrometers and fiber-optics based remote monitoring)

Schedule
Jun 1, 2024, Submission system opening
Feb 28, 2025,Submission system closing

Papers would be reviewed as soon as they are submitted and would be published in an ongoing manner. Hence, the
authors do not have to wait till the end of the submission window.

Format
All submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society
guidelines. Submitted articles should not have been published or be under review elsewhere. Submit your
manuscript on http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the
“Sensing and Remote Sensing in the Poles” special issue manuscript type. Prospective authors should consult
the site https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for guidelines and information on
paper submission. All submissions must be formatted using the IEEE standard format (double column, single
spaced). Please visit http://www.ieee.org/publications_standards/publications/authors/author_templates.html to
download a template for transactions. Please note that since Jan. 1, 2024, IEEE J-STARS, as a fully open-access
journal, is charging a flat publication fee $1,496 per paper.

Guest Editors
Amit Kumar Mishra, Aberystwyth University, UK (amit.mishra@aber.ac.uk)
Tao Che, Chinese Academy of Sciences, China (chetao@lzb.ac.cn)
Adrian Bruce McCallum, University of the Sunshine Coast, Australia (amccallu@usc.edu.au)
Marc De Vos, University of Connecticut, USA (marc.devos@uconn.edu)

Special Issue on Remote Sensing for Monitoring Fluvial Geomorphic Changes and Disaster Risk Reduction Planning
截稿日期: 2025-03-31

Remote sensing is a powerful technique for enhancing natural hazard management, particularly in monitoring fluvial
geomorphic changes and aiding disaster risk reduction. It involves detecting and monitoring an area's physical
properties using reflected and emitted radiation, typically via satellite or aerial imagery. A key application is
analyzing and mapping river landforms and floodplain features, such as using aerial photography to map floodplain
features and assess river morphology changes over time. Remote sensing provides high-resolution temporal data to
quantify river channel changes, sediment deposition, and erosion patterns, crucial for understanding fluvial dynamics
and mitigating hazards. Remote sensing data, particularly from satellites is invaluable for disaster management,
offering essential information for pre-disaster risk assessment, immediate post-disaster response, and long-term
recovery planning. For example, post-flood remote sensing assesses inundation extent, monitors floodwater
progression, and evaluates infrastructure and landscape damage, aiding relief efforts and resource allocation.
Integrating remote sensing data with Geographic Information Systems (GIS) enhances its utility by organizing,
analyzing, and visualizing spatial data, identifying priority intervention areas, and informing disaster risk reduction
and urban planning strategies.

Beyond disaster management, remote sensing is used in agriculture to monitor crop health, in urban planning to
assess land use changes, and in environmental conservation to track biodiversity and ecosystem changes. This special
issue aims to highlight innovative applications, advance methodologies, foster collaboration, inform policy, address
challenges, and promote education and capacity building. It emphasizes remote sensing's role in disaster risk
reduction, sustainable development goals, climate change adaptation, natural resource management, and resilient
infrastructure planning, encouraging integration with other technologies and interdisciplinary collaboration.

The broad topics include (but are not limited to):
 GIS and Remote Sensing for Disaster Risk Reduction and Evaluation of Natural Hazards.
 Using Google Earth Engine to Identify Changes in River Channels for Fluvial Geomorphology.
 Remote Sensing Methods for Analyzing Channel Dynamics and Geomorphic Effects of Floods.
 Vegetation Coverage and Planform Morphology for River Management Applications.
 Geographic Information Systems and Remote Sensing for Managing Natural Disasters.
 Tracking the Evolution of River Channels Using GIS and Remote Sensing.
 An Integrated Approach to Studying River Flooding and Urban Expansion to Reduce Hydrogeomorphic Risk.
 Evaluating the Geomorphic Response of River Systems to Holocene Climate Changes Using Remote Sensing.

Schedule
01 Oct 2024, Submission system opening
31 March 2025, Submission system closing

Format
All submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society guidelines.
Submitted articles should not have been published or be under review elsewhere. Submit your manuscript on
http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the “Remote Sensing for
Monitoring Fluvial Geomorphic Changes and Disaster Risk Reduction Planning” special issue manuscript type.
Prospective authors should consult the site https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for
guidelines and information on paper submission. All submissions must be formatted using the IEEE standard format
(double column, single spaced). Please visit
http://www.ieee.org/publications_standards/publications/authors/author_templates.html to download a template for
transactions. Please note that since Jan. 1, 2024, IEEE J-STARS, as a fully open-access journal, is charging a flat
publication fee $1,496 per paper.

Guest Editors
Dr. Shakeel Mahmood, Government College University, Lahore, Pakistan. (shakeelmahmoodkhan@hotmail.com)
Dr. Sofiane Bensefia, University Mohamed El Bachir El Ibrahimi, Algeria. (sofiane.bensefia@univ-bba.dz)
Dr. Helen Muhammad Abdul Hussein AL-Badiri, University of Kufa, Najaf, Iraq. (helenm.abdulhussein@uokfa.edu.iq)
Dr. Muhammad Irfan Ahamad, Henan University, Kaifeng 475004, China. (mirfan230@hotmail.com)

Special Issue on Advanced SAR/InSAR technologies for surface deformation change
截稿日期: 2025-03-31

Land subsidence and structural deformation under natural and anthropogenic activities are threatening infrastructural
health and public safety. Synthetic aperture radar (SAR) and Interferometric SAR (InSAR) have been widely used for
deformation monitoring in various fields. The second-generation SAR satellite missions, such as Sentinel-1,
TerraSAR-X, PAZ, COSMO-SkyMed first and second generation, SAOCOM, LuTan-1 as well as commercial
constellations including Umbra, Capella Space and ICEYE provide invaluable insights into geophysical dynamics at
different scales. While innovative InSAR processing algorithms and systems are continuously being developed, the
research of SAR/InSAR technology can further have a positive impact on risk assessment, early warning and
monitoring. However, there remain numerous unresolved issues and emerging challenges that necessitate further
investigation.

This special issue will aim to address these challenges while providing a platform for researchers and practitioners to
share their latest findings, methodologies, and technologies related to SAR/InSAR systems, data processing, and
innovative applications. The broad topics include (but are not limited to):

 Advanced SAR/InSAR algorithms for surface deformation measurement.
 SAR-based detection and monitoring algorithms.
 AI for InSAR data processing and interpretation.
 Innovative systems at different bands, platform and resolutions.
 Integration of SAR/InSAR data with other datasets.
 Applications in the monitoring of subsidence, structural deformation, landslides, and etc.
 Other related topics.

Schedule
01 Jun, 2024, Submission system opening
31 March, 2025, Submission system closing

Format
All submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society guidelines.
Submitted articles should not have been published or be under review elsewhere. Submit your manuscript on
http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the “Advanced SAR/InSAR
technologies for surface deformation change” special issue manuscript type. Prospective authors should consult the
site https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for guidelines and information on paper
submission. All submissions must be formatted using the IEEE standard format (double column, single spaced). Please
visit http://www.ieee.org/publications_standards/publications/authors/author_templates.html to download a template
for transactions. Please note that as of Jan. 1, 2020, IEEE J-STARS has become a fully open-access journal charging a
flat publication fee $1,250 per paper.

Guest Editors
Peifeng Ma The Chinese University of Hong Kong, Hong Kong, China (mapeifeng@cuhk.edu.hk)
Hanwen Yu University of Electronic Science and Technology of China, China (yuhanwenxd@gmail.com)
Oriol Monserrat Centre Tecnològic de Telecomunicacions de Catalunya, Spain (omonserrat@cttc.cat)
Pietro Milillo University of Houston, USA (pmilillo@central.uh.edu)
Zherong Wu Cornell University, USA (zw734@cornell.edu)

Special Issue on UAV Remote Sensing Monitoring and Applications
截稿日期: 2025-04-30

Unmanned Aerial Vehicle (UAV) provides a cost-effective solution for high-frequency, high-resolution, and on-
demand data collection. This affordability allows for more frequent monitoring, which is crucial for tracking dynamic
changes in the environment or emergency situations. The exceptional capabilities of UAV remote sensing in Earth
observation enable its applications across a broad spectrum of monitoring and analytical tasks, encompassing
environmental monitoring, ecological assessment, urban planning, smart agriculture, and disaster management. And
the integration of UAV remote sensing with cutting-edge technologies such as information geography, artificial
intelligence, and big data analytics has significantly enhanced the efficiency and accuracy of data processing and
interpretation. This synergy enables more sophisticated and nuanced analysis of remote sensing data, leading to more
informed decision-making.

This Special Issue intends to highlight the methods and solutions of applying UAV remote sensing data and processing
techniques to gather accurate geometry, physical properties, and evolutionary processes of Earth’s surface targets.
Intelligent imagery processing, application and monitoring technology for UAV remote sensing are encouraged.
Quantitative inversion theories, algorithms, architectures, and applications using UAV remote sensing data, including
RGB, multispectral, and hyperspectral images, LiDAR are welcome.

The broad topics include (but are not limited to):
 UAV remote sensing dataset;
 UAV remote sensing data synthesis, mosaic;
 Cross modal UAV data registration, assimilate;
 UAV remote sensing imagery enhancement (image fusion, feature extraction, noise reduction, shadow removal, defect repair, etc.);
 Object detection and semantic analysis based on UAV remote sensing data;
 3D target reconstruction based on UAV remote sensing technology;
 Analysis of surface morphology changes;
 Theories and methods of quantitative inversion using UAV remote sensing;
 Advancements in quantitative remote sensing inversion based on UAV data;
 Monitoring natural resources and disasters with UAV remote sensing;
 Applications of UAV remote sensing in precision agriculture, urban environments, lakes and oceans.

Schedule
Aug 1, 2024, Submission system opening
Apr 30, 2025, Submission system closing

Format
All submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society guidelines.
Submitted articles should not have been published or be under review elsewhere. Submit your manuscript on
http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the “UAV Remote Sensing
Monitoring and Applications” special issue manuscript type. Prospective authors should consult the site
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for guidelines and information on paper
submission. All submissions must be formatted using the IEEE standard format (double column, single spaced). Please
visit http://www.ieee.org/publications_standards/publications/authors/author_templates.html to download a template
for transactions. Please note that since Jan. 1, 2024, IEEE J-STARS, as a fully open-access journal, is charging a flat
publication fee $1,496 per paper.

Guest Editors
Dr. Qingwang Wang Kunming University of Science and Technology, Kunming, China (wangqingwang@kust.edu.cn)
Dr. Zhen Zhang Kunming University of Science and Technology, Kunming, China (zhangzhen@kust.edu.cn)
Dr. Wenguan Wang Zhejiang University, Hangzhou, China (wenguanwang@zju.edu.cn)
Dr. Nan Su Harbin Engineering University, Harbin, China (sunan08@hrbeu.edu.cn)
Dr. Junshi Xia Geoinformatics Team, RIKEN Center, Tokyo, Japan (junshi.xia@riken.jp)

Special Issue on Intelligent Sensing and Navigation Technologies for 6G
截稿日期: 2025-06-30

Sensing stands as a foundational capability to address the diverse requirements of forthcoming 6G application scenarios,
enabling the detection and recognition of environmental data. Modern Remote Sensing (RS) technologies offer broad
observation ranges, swift speeds, and short period, finding widespread utility in agriculture, environmental monitoring,
disaster prevention, mapping, urban construction, and management. Their deployment significantly enhances human
productivity and quality of life. As communication and sensing technologies advance, the concept of Integrated Sensing
and Communication (ISAC) has garnered attention, promising improvements in system spectrum efficiency, hardware
utilization, and information processing efficiency while seamlessly blending sensing and communication functionalities.
Navigation Sensing (NS) technology also plays a crucial role in this landscape. Moreover, to meet the demands of diverse
future application scenarios, there's a growing concept of integrating communication, navigation, and remote sensing. In
recent years, Artificial Intelligence (AI) has undergone continuous evolution, reaching the realm of perceptual intelligence.
Leveraging AI in sensing scenarios holds the promise of substantially enhancing sensing capabilities and recognition
accuracy. This special issue seeks contributions from researchers, practitioners, and scholars in relevant fields to showcase
their research findings, delving into the current research landscape of 6G-assisted intelligent sensing technologies.

The broad topics include (but are not limited to):
 RS/NS information recognition based on lightweight deep learning models
 AI assisted/enhanced NS/RS technology
 Basic theoretical performance limitations of ISAC in 6G
 Performance analysis/optimization of Space-Air-Ground-Sea Integrated Networks supported by ISAC
 The ISAC and RS/NS with state-of-the-art wireless technologies (e.g., RIS，ambient backscatter, massive MIMO, mmWave/THz, privacy/security, NOMA, covert communication, etc.)
 Positioning, timing, and navigation of ISAC
 Centralized/distributed machine learning of ISAC and RS/NS
 ISAC and RS/NS system based on spectrum sharing
 Transfer learning and domain adaptation techniques for improving RS/NS sensing performance
 AI platforms, frameworks, and systems used to support sensing
 Development of a test bench for ISAC and RS/NS coexistence experiments

Schedule
October 1, 2024, Submission system opening
June 30, 2025, Submission system closing

Format
All submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society guidelines.
Submitted articles should not have been published or be under review elsewhere. Submit your manuscript on
http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the “Intelligent Sensing and
Recognition Technologies for Remote Sensing” special issue manuscript type. Prospective authors should consult
the site https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for guidelines and information on paper
submission. All submissions must be formatted using the IEEE standard format (double column, single spaced). Please
visit http://www.ieee.org/publications_standards/publications/authors/author_templates.html to download a template
for transactions. Please note that since Jan. 1, 2024, IEEE J-STARS, as a fully open-access journal, is charging a flat
publication fee $1,496 per paper.

Guest Editors
Xingwang Li Henan Polytechnic University, China (lixingwang@hpu.edu.cn)
Arumugam Nallanathan Queen Mary University of London, UK. (a.nallanathan@qmul.ac.uk)
Shuanggen Jin Henan Polytechnic University, China (sgjin@hpu.edu.cn)
Derrick Wing Kwan Ng University of New South Wales, Australia (w.k.ng@unsw.edu.au)
Zhiyong Feng Beijing University of Post & Telecommunication, China (fengzy@bupt.edu.cn)
Chau Yuen Nanyang Technological University, Singapore (chau.yuen@ntu.edu.sg)

Special Issue on Enhancing Remote Sensing of Coastal Areas through Multi-Sensor Data Fusion
截稿日期: 2025-06-30

The rapid advancement of remote sensing technology has led to the emergence of very high-resolution (VHR) imaging
sensors and other technologies deployed on both visible and spaceborne vehicles. Additionally, one of the most
popular methods, remote sensing data fusion, combines data from sensors installed on satellites, airliners, and
popularity structures with varying geographic and frequency objectives to create fused data that becomes more
specific than data collected from all the sensors separately. Coastal areas are remarkably relevant to humankind,
serving as vital hubs for progress in society and the economy. Multiple sensors must be used since a single sensor or
survey cannot accurately capture a component's whole set of attributes. Numerous applications may be found for the
commercial internet of things. For this reason, handling multi-sensor fusion data is crucial. A particular combination,
or the integration of data, can create more. The Multi-Sensor Data Fusion aspects all emphasize the necessity of
creating novel data analysis techniques that can manage remote sensing data, supporting the use of integrated and
sustainable systems. Throughout the field of remote sensing analysis, characteristic partitioning is among the most
applicable methods for data pretreatment. In order to increase the effectiveness of smart image analyzing techniques
and make it easier for specialists to comprehend and apply the collected remote sensing intelligence, its primary
objective is to constantly convert visual features into isolated ones. The analysis makes use of the Special Issue on
Remote Sensing of the Coastal Area to emphasize recent developments in the field's understanding of the coastal area's
remote sensing and to establish several development goals for the area. Multi-sensor data fusion techniques were
developed from several fields, such as neural networks, analytical forecasting, recognizing trends, and others. An
introduction to data fusion applications, process diagrams, and the identification of relevant methodologies are all
covered in this instructional section.

Papers are invited that consider, but are not limited to, the following themes:

The broad topics include (but are not limited to):
 An evaluation of effective uses for spacecraft remote sensing in coastal areas
 Analysis of spectrum and landcover projection with multi-sensor data fusion methods
 Geomorphological and ecological vulnerability indicator modelling using multi-sensor data fusion
 Coastal area recognition using a combination of information and multi-sensor data
 Understanding eddy-induced the rise in the southern coastal area using remote sensing
 Multi-sensor data fusion for resource transfer and hydrology prediction of parameters
 Utilizing multi-sensor fusion methods for coastal mangrove ecosystem remote sensing
 Integration of multi-sensor features for high-spatial location extraction in transitory developments
 Land-surface temporal recovery using multi-sensor fusion at high geographical improvements
 Coastal area tracking using a multi-sensor setting: a method for developing

Schedule
01 Oct 2024 Submission system opening
30 Jun 2025 Submission system closing

Format
All submissions will be peer reviewed according to the IEEE Geoscience and Remote Sensing Society guidelines.
Submitted articles should not have been published or be under review elsewhere. Submit your manuscript on
http://mc.manuscriptcentral.com/jstars, using the Manuscript Central interface and select the “Enhancing Remote
Sensing of Coastal Areas through Multi-Sensor Data Fusion” special issue manuscript type. Prospective authors
should consult the site https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9082768 for guidelines and
information on paper submission. All submissions must be formatted using the IEEE standard format (double column,
single spaced). Please visit http://www.ieee.org/publications_standards/publications/authors/author_templates.html to
download a template for transactions. Please note that since Jan. 1, 2024, IEEE J-STARS, as a fully open-access
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Guest Editors
Alireza Sharifi Shahid Rajaee Teacher Training University, Iran (a_sharifi@sru.ac.ir)
Hadi Mahdipour University of Oviedo, Spain (mahdipourhadi@uniovi.es)
Khilola Amankulova University of Szeged, Szeged, Hungary (amankulova.khilola@stud.u-szeged.hu)