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Multisource Remote Sensing Data Fusion and Applications in Vegetation Monitoring (Second Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 28 May 2026 | Viewed by 811

Special Issue Editors


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Guest Editor
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Interests: ecological remote sensing; impact of land-use and land cover changes on environment

E-Mail Website
Guest Editor
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Interests: remote sensing images processing; remote sensing applications; AI applications in remote sensing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100049, China
Interests: remote sensing images processing; remote sensing applications; ecological remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The monitoring and characterization of vegetation ecosystems represents a cornerstone of global environmental research, underpinning efforts to address climate change, biodiversity loss, and sustainable land management. Traditional approaches relying on field-based inventories or single-sensor remote sensing have long provided critical insights, but their limitations, such as restricted spatial coverage, temporal infrequency, or spectral constraints, have fueled the need for more holistic methodologies. Multisource remote sensing data fusion has emerged as a transformative approach for advancing vegetation monitoring, enabling synergistic insights that surpass the capabilities of individual sensors.

This Special Issue explores cutting-edge methodologies for integrating heterogeneous datasets, including optical, radar, LiDAR, hyperspectral, and thermal infrared imagery, from satellite, aerial, and ground-based platforms. By combining complementary spatial, spectral, and temporal resolutions, contributors are expected to address critical challenges in vegetation ecosystems, such as biodiversity conservation, carbon cycle dynamics, land-use change, and disturbance resilience.

This Special Issue advances this frontier by curating cutting-edge research that bridges technical innovation and ecological application. By fostering cross-disciplinary dialog, it aims to catalyze solutions for one of Earth’s most pressing challenges: sustaining vegetation ecosystems in the Anthropocene. The issue supports the journal's core objective of advancing remote sensing innovations for environmental monitoring and sustainability.

We welcome submissions of original research, reviews, and case studies. Articles may address, but are not limited to, the following topics:

  • Vegetation parameter estimation (e.g., biomass, leaf area index, species composition)
  • Ecosystem service monitoring (pollination, soil erosion control, water regulation)
  • Disturbance detection and recovery (wildfires, droughts, invasive species)
  • Carbon flux modeling and sequestration potential mapping
  • Precision agriculture and crop health diagnostics
  • Urban vegetation mapping and heat island mitigation
  • Biodiversity hotspot identification and connectivity analysis
  • Climate change impacts on phenology and range shifts
  • Fusion algorithms for cross-sensor calibration and uncertainty reduction
  • Machine learning-driven upscaling from local plots to global datasets
  • Policy-relevant applications (e.g., REDD+ monitoring, habitat restoration planning)

Dr. Dong Liu
Dr. Jiakui Tang
Dr. Qing Zhang
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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

  • multisource data fusion
  • vegetation monitoring
  • hyperspectral imaging
  • carbon sequestration
  • land-use change
  • biodiversity
  • spatiotemporal analysis
  • ecological informatics
  • big data analytics

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Related Special Issue

Published Papers (1 paper)

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Research

21 pages, 9861 KB  
Article
Accounting for 10 m Resolution Mapping for Above-Ground Biomass of Urban Trees in C40 Cities Across Eurasia Continent
by Ge Yan, Zhifang Shi, Gaomin Lian, Kailong Cui, Nan Li, Ying Luo, Shuyuan Zhou, Mengmeng Cao and Yaoping Cui
Remote Sens. 2025, 17(23), 3898; https://doi.org/10.3390/rs17233898 - 30 Nov 2025
Viewed by 299
Abstract
High-resolution above-ground biomass (AGB) data play a critical role in advancing low-carbon development strategies across cities. However, research on urban trees’ AGB largely relies on high-accuracy field measurements, which limits the feasibility of conducting cross-regional studies. In contrast, existing remote-sensing-based AGB products provide [...] Read more.
High-resolution above-ground biomass (AGB) data play a critical role in advancing low-carbon development strategies across cities. However, research on urban trees’ AGB largely relies on high-accuracy field measurements, which limits the feasibility of conducting cross-regional studies. In contrast, existing remote-sensing-based AGB products provide extensive coverage while lacking the spatial resolution required for precise city-scale analysis. To address the dilemma of achieving both high spatial resolution and broad coverage, this study integrated 149 feature variables derived from multi-source datasets and implemented quality-control procedures to select high-quality samples from two globally representative AGB products (GEDI AGB and CCI AGB). This strategy substantially improved the performance of the random forest model and generated 10 m resolution urban trees’ AGB maps for 51 C40 cities across Eurasia continent. The results indicate that: (1) after applying quality control to the target variables, the mean R2 of ten-fold cross validation improved from 0.37 to 0.75, and the MAE decreased substantially from 47.02 Mg/ha to 17.48 Mg/ha; (2) by enhancing the spatial resolution of AGB maps to 10 m, the resulting products exhibit superior spatial detail, better capture local variations, and maintain greater spatial continuity compared with the CCI AGB and GEDI AGB datasets; (3) the mean AGB density across the Eurasian continent was 39.44 Mg/ha, with total urban tree s’ AGB reaching 83.83 × 106 t. Comparison with previous single-city C40 studies shows that our estimated AGB density and total AGB closely align with previously reported values. The above data implies that cities carry an undeniable amount of carbon storage, both in terms of carbon density and total amount. This study provides a robust foundation for accurately assessing the potential of urban carbon sinks and optimizing the path to achieving carbon neutrality. Full article
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