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Remote Sensing
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Advancement of Multi-Source Remote Sensing Data Fusion in Environmental Monitoring
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Advancement of Multi-Source Remote Sensing Data Fusion in Environmental Monitoring

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 2771

Special Issue Editors

Dr. Junjue Wang

E-Mail Website
Guest Editor
Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, Japan
Interests: multi-modal remote sensing processing; earth vision; land-cover mapping; sustainable urban planning; disaster assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Jiaqi Yang

E-Mail Website
Guest Editor
Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
Interests: multi-modal remote sensing image fusion; hyperspectral remote sensing; remote sensing image interpretation; data-driven earth system science
Dr. Yinhe Liu

E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: high-resolution remote sensing; land use and land cover mapping; change detection; multi-modal data fusion
Dr. Zhuo Zheng

E-Mail Website
Guest Editor
Department of Computer Science, Stanford University, Stanford, CA 94305, USA
Interests: earth vision remote sensing; computational sustainability; change detection
Special Issues, Collections and Topics in MDPI journals
Dr. Yonghao Xu

E-Mail Website
Guest Editor
Department of Electrical Engineering, Linköping University, SE-581 83 Linköping, Sweden
Interests: remote sensing data interpretation; AI security for earth observation; AI for environmental monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multi-source remote sensing images from satellite and airborne platforms are being acquired daily, including optical, multi-spectral, hyperspectral, LiDAR, SAR, etc. Due to the different imaging mechanisms, each source of imagery provides unique observation signals, enabling the accurate monitoring of the dynamic and complex earth environment from diverse perspectives. The integration of multi-source Earth observation data can improve the temporal-spatial-spectral resolution, accuracy, and data coverage of observations, allowing for fine-grained analyses of vegetation dynamics, forest health, species diversity, and biomass estimation.

As an ancient topic, multi-source remote sensing data fusion is now facing new challenges with the exponential growth of data volume and complexity. By addressing large-scale heterogeneous data, the differences in imaging mechanisms, resolutions, and environments become prominent. How to develop innovative techniques to address these challenges and serve for better Earth environment monitoring is an open question for remote sensing research.

This Special Issue aims at advancing innovative techniques or datasets for multi-source remote sensing data fusion, covering diverse applications that could help scientists and decision-makers to understand complex Earth system processes and to better respond to global environmental and climate change. We encourage the integration of recent deep learning techniques (large pre-trained multi-modal models, domain adaptation strategies, etc.) and large-scale applications (land-cover mapping, ecosystem monitoring, urban analysis, disaster assessment, etc.). The scope includes, but is not limited to, the following:

  • Multi-source remote sensing data fusion;
  • Multi-source transferable or domain adaptation models;
  • Multi-modal models for remote sensing tasks;
  • Land-cover mapping;
  • Ecosystem monitoring;
  • Urban analysis;
  • Crop monitoring and yield forecasting;
  • Glacier and sea ice monitoring;
  • Atmospheric monitoring;
  • Biodiversity and ecological conservation;
  • Greenhouse gas emission monitoring;
  • Vegetation dynamics and the carbon cycle.

Dr. Junjue Wang
Dr. Jiaqi Yang
Dr. Yinhe Liu
Dr. Zhuo Zheng
Dr. Yonghao Xu
Guest Editors

Mr. Weihao Xuan
Guest Editor Assistant
Affiliation: Graduate School of Frontier Sciences, The University of Tokyo & RIKEN Center for Advanced Intelligence Project, Chiba 277-8561, Japan
Email:
Webpage: https://weihaoxuan.com/
Interests: foundation models for remote sensing data; multi-modal learning; vision-language models; point cloud analysis

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multi-source remote sensing data fusion
  • multi-source transferable or domain adaptation models
  • multi-modal models for remote sensing tasks
  • ecosystem monitoring
  • urban analysis
  • crop monitoring and yield forecasting
  • glacier and sea ice monitoring
  • atmospheric monitoring
  • greenhouse gas emission monitoring
  • vegetation dynamics and carbon cycle

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

19 pages, 66023 KiB  
Article
Boosting Urban Openspace Mapping with the Enhancement Feature Fusion of Object Geometry Prior Information from Vision Foundation Model
by Zijian Xu, Jiajun Chen, Hongyang Niu, Runyu Fan, Dingkun Lu and Ruyi Feng
Remote Sens. 2025, 17(7), 1230; https://doi.org/10.3390/rs17071230 - 30 Mar 2025
Viewed by 292
Abstract
Urban open spaces (UO) play a crucial role in urban environments, particularly in areas where social and economic activities are rapidly increasing. However, the challenges of high inter-class similarity, complex environmental surroundings, and scale variations often result in suboptimal performance in UO mapping. [...] Read more.
Urban open spaces (UO) play a crucial role in urban environments, particularly in areas where social and economic activities are rapidly increasing. However, the challenges of high inter-class similarity, complex environmental surroundings, and scale variations often result in suboptimal performance in UO mapping. To address these issues, this paper proposes UOSAM, a novel approach that leverages the Segment Anything Model (SAM) for efficient UO mapping using high-resolution remote sensing images. Our method employs a pyramid transformer to extract feature pyramids at multiple scales, capturing multi-scale semantic context and addressing the issue of scale variation. Additionally, SAM is used to achieve the more precise geometry segmentation of ubiquitous objects within the images, effectively tackling the challenges posed by their high inter-class similarity and environmental complexity. Furthermore, we introduce a feature fusion module (FFM) that integrates multi-level features from the remote sensing images. Extensive experiments conducted on the Urban Openspace China Ten Cities (UOCTC) dataset from ten major cities in China, using manually annotated samples, demonstrate the superiority of the proposed UOSAM. Full article
(This article belongs to the Special Issue Advancement of Multi-Source Remote Sensing Data Fusion in Environmental Monitoring)
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21 pages, 5531 KiB  
Article
STFNet: A Spatiotemporal Fusion Network for Forest Change Detection Using Multi-Source Satellite Images
by Yingjiao Tan, Kaimin Sun, Jinjiang Wei, Song Gao, Wei Cui, Yu Duan, Junyi Liu and Wanghui Zhou
Remote Sens. 2024, 16(24), 4736; https://doi.org/10.3390/rs16244736 - 19 Dec 2024
Viewed by 934
Abstract
Forest resources have important ecological and environmental values, and monitoring forest changes using remote sensing images is essential for resource management and ecological protection. However, current forest change detection methods fail to simultaneously integrate fine spatial information with temporal dynamic data, making them [...] Read more.
Forest resources have important ecological and environmental values, and monitoring forest changes using remote sensing images is essential for resource management and ecological protection. However, current forest change detection methods fail to simultaneously integrate fine spatial information with temporal dynamic data, making them susceptible to pseudo changes induced by seasonal factors. In this paper, we propose a forest change detection method called STFNet that integrates multi-source spatiotemporal information. By combining fine spatial details of high-resolution images with dynamic information from time-series images, STFNet enhances the accuracy of forest change detection, alleviating the problem of information fusion difficulties caused by inconsistent granularity in spatiotemporal spectral features from different sources. In STFNet, we propose a cross-attention-based temporal differential feature fusion module (CATFF) to capture spatiotemporal dependencies within time-series images and a multiresolution contextual differential feature fusion module (MCDF) to achieve efficient spatial contexture fusion across multiresolution images. To validate our method, we conduct experiments using Gaofen and Sentinel-2 satellite images. Experimental results demonstrate that STFNet achieves excellent performance with an F1-score of 87.65%, outperforming state-of-the-art methods by at least 2.02%. Our ablation study further confirms the effectiveness of our method in leveraging time-series information to detect forest changes and suppress seasonal interference. Full article
(This article belongs to the Special Issue Advancement of Multi-Source Remote Sensing Data Fusion in Environmental Monitoring)
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