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

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 12940

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Special Issue Editors

Dr. Junjue Wang

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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
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Dr. Jiaqi Yang

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Guest Editor

Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
Interests: AI for earth observation; hyperspectral image classification; forestry
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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; agent; deep reinforcement learning; land use and land cover
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Dr. Zhuo Zheng

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Guest Editor

Department of Computer Science, Stanford University, Stanford, CA 94305, USA
Interests: earth vision remote sensing; computational sustainability; change detection
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Dr. Yonghao Xu

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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
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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: weihaoxuan@g.ecc.u-tokyo.ac.jp
Webpage: https://weihaoxuan.com/
Interests: foundation models for remote sensing data; multi-modal learning; vision-language models; point cloud analysis

Manuscript Submission Information

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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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Published Papers (9 papers)

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Research

22 pages, 6732 KB

Open AccessArticle

A High-Precision Monitoring Method for Surface Subsidence in Western Chinese Mining Areas by Fusing InSAR and LiDAR

by Dayong Xu, Tao Wei, Lei Wang, Jingyu Li, Shenshen Chi and Xiaohan Liu

Remote Sens. 2026, 18(10), 1521; https://doi.org/10.3390/rs18101521 - 12 May 2026

Viewed by 279

Abstract

Surface subsidence monitoring in western Chinese mining areas is challenged by complex topography, dense vegetation, and large deformation gradients. Traditional single remote sensing methods suffer from inherent drawbacks: InSAR performs well in small-gradient areas but is prone to incoherence in large-gradient zones, whereas LiDAR achieves high accuracy in large-gradient monitoring but lacks precision for small-gradient deformations. This study proposes a high-precision monitoring method by fusing InSAR and LiDAR data. First, a non-growing surface feature extraction approach using LiDAR echo characteristics is adopted to reduce vegetation noise and improve monitoring stability. Second, fusion boundaries are determined using InSAR corrected deformation gradient and image coherence, dividing the subsidence basin into small-, medium-, and large-gradient zones. Third, an inverse mean squared error weighted fusion strategy is applied in the medium-gradient zone to realize reliable data integration. Experiments conducted at the Sihe Coal Mine show that the full-gradient RMSE of InSAR is 415 mm, and that of LiDAR is 56 mm. The proposed fusion method reduces the full-gradient RMSE to 39 mm, which is 91% lower than that of InSAR and 30% lower than that of LiDAR. The method achieves centimeter-level accuracy across the entire subsidence basin while maintaining high precision in both large-gradient centers and small-gradient edges. It provides a stable and practical technical solution for full-gradient subsidence monitoring in western Chinese mining areas with complex terrain and dense vegetation. Full article

(This article belongs to the Special Issue Advancement of Multi-Source Remote Sensing Data Fusion in Environmental Monitoring)

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32 pages, 8456 KB

Open AccessArticle

Spatiotemporal Dynamics and Driving Patterns of Forest Fires in Yunnan Province, China: An Empirical Study Based on Event-Level Reconstruction from Multi-Source Remote Sensing (2012–2024)

by Hang Deng, Junfan Zhao, Lan Wang and Fan Zhao

Remote Sens. 2026, 18(9), 1359; https://doi.org/10.3390/rs18091359 - 28 Apr 2026

Viewed by 395

Abstract

Pixel-based Active Fire Spot (AFS) statistics alone are insufficient for characterizing forest fire activity in fragmented mountainous agroforestry regions because cross-sensor differences, geometric distortion, and discontinuous satellite overpasses can fragment physically continuous fires into multiple detections. To address this problem, we developed a reconstruction framework that combines optical–thermal cross-validation with multi-level spatio-temporal clustering to identify physically independent fires in Yunnan Province, China. Starting from 497,834 raw AFSs detected during 2012–2024, the framework removed unusable detections, aggregated the retained AFSs, and identified 41,215 validated Forest Fire Events (FFEs). The reconstructed database revealed clear temporal, spatial, and topographic heterogeneity. Fire activity was strongly concentrated in the late dry season, with 32.8% of all FFEs occurring during the main spring fire window. Daytime FFEs accounted for 82.8% of all FFEs, but nocturnal activity increased substantially in some years, reaching 20.7% in 2023. Persistence showed a long-tailed structure under both observation frameworks, although the operational thresholds differed between 2012–2017 (105 min) and 2018–2024 (75 min). Regionally, Southeast and Southwest Yunnan concentrated most reconstructed FFEs, whereas Northwest and Central Yunnan showed much higher CFRP per event. Topographically, fire energy was concentrated mainly on gentle-to-moderate slopes, and nighttime fires were centered 215.03 m higher than daytime fires. The typology analysis further showed that event frequency and physical fire impact were not distributed proportionally across fire types. Random Forest validation indicated high reproducibility of the rule-based typology system (Macro-F1 = 0.9935; Weighted-F1 = 0.9964), whereas the first two principal components explained 42.65% of the total variance. These results show that event-level reconstruction provides a stronger basis than AFS counts alone for understanding fire heterogeneity and supporting zone-specific fire management in Yunnan. Full article

(This article belongs to the Special Issue Advancement of Multi-Source Remote Sensing Data Fusion in Environmental Monitoring)

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30 pages, 11760 KB

Open AccessArticle

A Multi-Dimensional Indicator Framework for Peri-Urban Area Delineation: Insights from Equal- and AHP-Weighted Models in Java, Indonesia

by Ziyue Wang, Adhitya Marendra Kiloes, Md. Ali Akber, Bagus Setiabudi Wiwoho and Ammar Abdul Aziz

Remote Sens. 2026, 18(7), 1062; https://doi.org/10.3390/rs18071062 - 2 Apr 2026

Viewed by 591

Abstract

Peri-urban areas (PUAs), as transitional zones between urban and rural regions, play a critical role in supporting food systems and agricultural livelihoods, yet they are increasingly pressured by rapid urban expansion. Reliable spatial delineation of PUAs remains challenging, as administrative boundaries often fail to capture their functional and spatial heterogeneity. This study proposes a multi-dimensional, spatially explicit framework to delineate peri-urban areas using Indonesia as a case study. Eighteen indicators representing six analytical dimensions—land use/land cover, economic, demographic, infrastructural, spatial accessibility, and landscape structure—were derived from remote sensing and GIS-based data sources and integrated into a composite scoring system using equal-weighted and AHP-weighted approaches. The framework was applied to four major cities on Java Island (Jakarta, Surabaya, Bandung, and Yogyakarta) to generate continuous peri-urban probability surfaces, which were validated using expert surveys across 25 districts in the Jakarta and Bandung metropolitan areas. The results show that the framework effectively captures the spatial heterogeneity and gradients of peri-urban areas, with the equal-weighted approach exhibiting statistically significant agreement with expert assessments (Pearson’s r = 0.517, p = 0.008; Spearman’s ρ = 0.522, p = 0.008; Kendall’s τ = 0.387, p = 0.008), consistently outperforming the AHP-weighted model across all validation metrics. The proposed approach provides a transferable spatial mapping framework for monitoring peri-urban dynamics in rapidly urbanizing regions using remote sensing and GIS. Full article

(This article belongs to the Special Issue Advancement of Multi-Source Remote Sensing Data Fusion in Environmental Monitoring)

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23 pages, 12314 KB

Open AccessArticle

Spatial Assessment of Water Balance and Soil Erosion Under Land-Use Change in Chieng Hac, Northern Vietnam

by Adhera Sukmawijaya, Md. Ali Akber, Ziyue Wang, Fathin Ayuni Azizan, Michael Bell and Ammar Abdul Aziz

Remote Sens. 2026, 18(7), 998; https://doi.org/10.3390/rs18070998 - 26 Mar 2026

Viewed by 436

Abstract

Chieng Hac in northern Vietnam is expanding maize cultivation, intensifying water competition and soil erosion. This study mapped regional water balance and erosion using remote sensing and GISs by coupling the Thornthwaite–Mather (TM) water balance model with the Revised Universal Soil Loss Equation (RUSLE) at 12.5 m resolution. Land cover was classified into maize, tree crops, paddy, forest, and other types using Random Forest. The TM model used 2021 precipitation and temperature measurements to estimate evapotranspiration, surplus, and deficit, while the RUSLE quantified soil loss. Two scenarios were evaluated: a baseline reflecting existing land use and an adjusted case applying strip cropping on 10–20° maize slopes and converting maize to tree crops on slopes > 20°. Tree crop conversion increased evapotranspiration and prolonged seasonal deficits relative to maize, increasing water deficit from 1013.6 to 1022.2 mm/year. In contrast, the interventions reduced mean soil loss from 15.52 to 11.51 t/ha/year, with the largest decline in the 5–25 t/ha/year class. Residual hotspots persisted on steep slopes and near drainage lines. The integrated framework highlights trade-offs between erosion control and seasonal water availability, supporting slope-based land-use planning in upland agricultural systems. These findings offer guidance for slope-based land-use planning by indicating that intervention priorities should vary depending on slope conditions and local water availability. Full article

(This article belongs to the Special Issue Advancement of Multi-Source Remote Sensing Data Fusion in Environmental Monitoring)

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37 pages, 28486 KB

Open AccessArticle

Investigating Very-High-Resolution Land Cover Mapping in the Pearl River Delta with Remote Sensing Foundation Models and Multi-Source Data Bayesian Fusion

by Junshen Luo, Yikai Zhao, Mingyang Xuan, Jizhou Zheng, Yan Zhou and Xiaoping Liu

Remote Sens. 2026, 18(6), 897; https://doi.org/10.3390/rs18060897 - 15 Mar 2026

Viewed by 511

Abstract

Very-high-resolution (VHR) land cover mapping in highly heterogeneous regions faces critical challenges including strong annotation dependence, significant image heterogeneity, and insufficient spectral information. To address these challenges, this study proposes a novel framework integrating remote sensing foundation models with multi-source data Bayesian fusion for VHR land cover mapping in the Pearl River Delta (PRD), which is one of the most complex and heterogeneous landscapes in China. To implement this framework, we first construct three datasets including PRD262K containing 262,436 unlabeled VHR images for pretraining, PRDLC-PRO with 33,342 annotated samples for semantic segmentation, and a 15,000-point sample set for medium-resolution (MR) classification. A Segmentation-Driven Masked AutoEncoder (SDMAE) is developed to learn robust feature representations from large-scale unlabeled VHR imagery, which is subsequently integrated with a Scene-Based Feature Network (SBFNet) to capture multi-scale semantic features for accurate land cover segmentation. Finally, a decision-level Bayesian fusion method is proposed to effectively integrate the fine spatial details of VHR imagery with the spectral stability of MR data. Experiments demonstrate that the proposed framework outperforms existing methods across multiple datasets, achieving an overall accuracy of 87.98% and mIoU of 66.61% on PRDLC-PRO. The subsequent decision-level Bayesian fusion further enhances spatial consistency and robustness, providing an effective solution for large-scale VHR land cover mapping in highly heterogeneous regions with limited annotations. Full article

(This article belongs to the Special Issue Advancement of Multi-Source Remote Sensing Data Fusion in Environmental Monitoring)

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19 pages, 122185 KB

Open AccessArticle

Multi-Granularity Domain-Adaptive Teacher for Unsupervised Remote Sensing Object Detection

by Fang Fang, Jianing Kang, Shengwen Li, Panpan Tian, Yang Liu, Chaoliang Luo and Shunping Zhou

Remote Sens. 2025, 17(10), 1743; https://doi.org/10.3390/rs17101743 - 16 May 2025

Cited by 3 | Viewed by 1415

Abstract

Object detection in remote sensing images (RSIs) is pivotal for various tasks such as natural disaster warning, environmental monitoring, teacher–student urban planning. Object detection methods based on domain adaptation have emerged, which effectively decrease the dependence on annotated samples, making significant advances in unsupervised scenarios. However, these methods fall short in their ability to learn remote sensing object features of the target domain, thus limiting the detection capabilities in many complex scenarios. To fill this gap, this paper integrates a multi-granularity feature alignment strategy and the teacher–student framework to enhance the capability of detecting remote sensing objects, and proposes a multi-granularity domain-adaptive teacher (MGDAT) framework to better bridge the feature gap across target and source domain data. MGDAT incorporates the teacher–student framework at three granularities, including pixel-, image- and instance-level feature alignment. Extensive experiments show that MGDAT surpasses SOTA baselines in detection accuracy, and exhibits great generalizability. This proposed method can serve as a methodology reference for various unsupervised interpretation tasks of RSIs. Full article

(This article belongs to the Special Issue Advancement of Multi-Source Remote Sensing Data Fusion in Environmental Monitoring)

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17 pages, 12223 KB

Open AccessArticle

Evaluating Arctic Thin Ice Thickness Retrieved from Latest Version of Multisource Satellite Products

by Huan Li, Jiarui Lian, Yu Zhang, Hailong Guo, Changsheng Chen, Weizeng Shao, Yi Zhou, Deshuai Wang and Song Hu

Remote Sens. 2025, 17(10), 1680; https://doi.org/10.3390/rs17101680 - 10 May 2025

Cited by 1 | Viewed by 1899

Abstract

Currently, the performance of sea ice thickness (SIT) data retrieved from multisource satellite products in the Arctic seasonal ice zones remains unclear. This study presented the spatiotemporal intercomparison and evaluation of satellite data, including the latest versions of Soil Moisture and Ocean Salinity (SMOS), CryoSat-2, combined CryoSat-2 and SMOS (CS2SMOS), and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), specifically focusing on area with mean SIT below 0.5 m. Five evaluation datasets were used. During 2010–2023, SMOS had the smallest mean SIT, with CryoSat-2 showing the largest mean SIT. During 2018–2023, with the inclusion of ICESat-2, SMOS still showed the smallest mean SIT. CryoSat-2 exhibited the largest mean SIT, followed by ICESat-2, CS2SMOS ranked third. Evaluation results indicated that four satellite products generally underestimated SIT. In two periods, SMOS consistently exhibited the weakest performance, which showed a large gap from what was expected in previous studies. In contrast, CS2SMOS demonstrated the highest alignment with five evaluation datasets during 2010–2023, indicating the best overall performance. During 2018–2023, ICESat-2 exhibited the best overall performance with two evaluation datasets. This study refreshes previous knowledge about SMOS in the seasonal ice zones and contributes to further improvements in SIT retrieval. Full article

(This article belongs to the Special Issue Advancement of Multi-Source Remote Sensing Data Fusion in Environmental Monitoring)

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19 pages, 66023 KB

Open AccessArticle

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 1268

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. 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 KB

Open AccessArticle

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

Cited by 10 | Viewed by 2541

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 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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