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Advances in Multi-Sensor Remote Sensing for Vegetation Monitoring
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Advances in Multi-Sensor Remote Sensing for Vegetation Monitoring

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: 29 December 2025 | Viewed by 1717

Special Issue Editors

Prof. Dr. Shan Lu

E-Mail Website
Guest Editor
School of Geographical Sciences, Northeast Normal University, 5268 Renmin Street, Changchun 130024, China
Interests: vegetation remote sensing; biophysical parameter retrieval; multi-angle reflectance; polarized remote sensing; hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chunying Ren

E-Mail Website
Guest Editor
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Interests: post-fire disturbance; vegetation change; forest aboveground biomass estimation; forest carbon sequestration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vegetation monitoring is pivotal for addressing global challenges such as climate change, food security, and biodiversity conservation. Multi-sensor approaches, integrating optical, radar, lidar, and hyperspectral data, enable variable insights into vegetation health, structure, and stress responses. Advances in data fusion, machine learning, and cloud computing have revolutionized applications like precision agriculture and carbon sequestration mapping. This Special Issue seeks to highlight cutting-edge research addressing sensor interoperability, scaling algorithms for local and global applications, and validating multi-sensor products across biomes, with a focus on novel methodologies, cross-disciplinary collaborations, and real-world applications. 

This Special Issue aims to show cutting-edge methodologies and applications in multi-sensor vegetation monitoring. It aligns with Remote Sensing’s scope by advancing sensor integration, algorithm development, and data fusion. Topics cover novel fusion techniques, cross-sensor calibration, and scalable solutions for global ecosystems. By bridging gaps between technical innovation and real-world challenges (e.g., drought prediction, deforestation tracking), this Issue supports our aim to publish high-impact research across Earth sciences.

We encourage the submissions concerning advanced techniques/approaches that are relative to the multi-sensor data fusion (e.g., SAR–optical synergy, multispectral–hyperspectral integration, LIDAR–hyperspectral combination, and deep learning frameworks), vegetation stress monitoring using SIF, thermal, or optical remote sensing data, 3D structure mapping via LIDAR and SAR, and cross-disciplinary applications in agroecology, forestry, and climate change in various ecosystems. Articles integrating multi-temporal or multi-scale datasets are also welcomed.

Prof. Dr. Shan Lu
Prof. Dr. Chunying Ren
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 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

  • SAR–optical synergy
  • thermal–optical synergy
  • LIDAR–optical synergy
  • SIF–multispectral synergy
  • vegetation biomass
  • vegetation phenology
  • vegetation mapping
  • vegetation parameter inversion

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

Published Papers (2 papers)

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24 pages, 5793 KB  
Article
Comparative Assessment of Planar Density and Stereoscopic Density for Estimating Grassland Aboveground Fresh Biomass Across Growing Season
by Cong Xu, Jinchen Wu, Yuqing Liang, Pengyu Zhu, Siyang Wang, Fangming Wu, Wei Liu, Xin Mei, Zhaoju Zheng, Yuan Zeng, Yujin Zhao, Bingfang Wu and Dan Zhao
Remote Sens. 2025, 17(17), 3038; https://doi.org/10.3390/rs17173038 - 1 Sep 2025
Viewed by 753
Abstract
Grassland aboveground biomass (AGB) serves as a critical indicator of ecosystem productivity and carbon cycling, playing a pivotal role in ecosystem functioning. The advances in hyperspectral and terrestrial Light Detection and Ranging (LiDAR) data have provided new opportunities for grassland AGB monitoring, but [...] Read more.
Grassland aboveground biomass (AGB) serves as a critical indicator of ecosystem productivity and carbon cycling, playing a pivotal role in ecosystem functioning. The advances in hyperspectral and terrestrial Light Detection and Ranging (LiDAR) data have provided new opportunities for grassland AGB monitoring, but current research remains predominantly focused on data-driven machine learning models. The black-box nature of such approaches resulted in a lack of clear interpretation regarding the coupling relationships between these two data types in grassland AGB estimation. For grassland aboveground fresh biomass, the theoretical estimation can be decomposed into either the product of planar density (PD) and plot area or the product of stereoscopic density (SD) and grassland community volume. Based on this theory, our study developed a semi-mechanistic remote sensing model for grassland AGB estimation by integrating hyperspectral-derived biomass density with extracted structural parameters from terrestrial LiDAR. Initially, we built hyperspectral estimation models for both PD and SD of grassland fresh AGB using PLSR. Subsequently, by integrating the inversion results with grassland quadrat area and community volume measurements, respectively, we achieved quadrat-scale remote sensing estimation of grassland AGB. Finally, we conducted comparative accuracy assessments of both methods across different phenological stages to evaluate their performance differences. Our results demonstrated that SD, which incorporated structural features, could be more precisely estimated (R2 = 0.90, nRMSE = 7.92%, Bias% = 0.01%) based on hyperspectral data compared to PD (R2 = 0.79, nRMSE = 10.19%, Bias% = −7.25%), with significant differences observed in their respective responsive spectral bands. PD showed greater sensitivity to shortwave infrared regions, while SD exhibited stronger associations with visible, red-edge, and near-infrared bands. Although both methods achieved comparable overall AGB estimation accuracy (PD-based: R2 = 0.79, nRMSE = 10.19%, Bias% = −7.25%; SD-based: R2 = 0.82, nRMSE = 10.58%, Bias% = 1.86%), the SD-based approach effectively mitigated the underestimation of high biomass values caused by spectral saturation effects and also demonstrated superior and more stable performance across different growth periods (R2 > 0.6). This work provided concrete physical meaning to the integration of hyperspectral and LiDAR data for grassland AGB monitoring and further suggested the potential of multi-source remote sensing data fusion in estimating grassland AGB. The findings offered theoretical foundations for developing large-scale grassland AGB monitoring models using airborne and spaceborne remote sensing platforms. Full article
(This article belongs to the Special Issue Advances in Multi-Sensor Remote Sensing for Vegetation Monitoring)
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23 pages, 2715 KB  
Systematic Review
Application of Remote Sensing and Geographic Information Systems for Monitoring and Managing Chili Crops: A Systematic Review
by Ziyue Wang, Md Ali Akber and Ammar Abdul Aziz
Remote Sens. 2025, 17(16), 2827; https://doi.org/10.3390/rs17162827 - 14 Aug 2025
Viewed by 684
Abstract
Chili (Capsicum sp.) is a high-value crop cultivated by farmers, but its production is vulnerable to weather extremes (such as irregular rainfall, high temperatures, and storms), pest and disease outbreaks, and spatially fragmented cultivation, resulting in unstable yields and income. Remote sensing [...] Read more.
Chili (Capsicum sp.) is a high-value crop cultivated by farmers, but its production is vulnerable to weather extremes (such as irregular rainfall, high temperatures, and storms), pest and disease outbreaks, and spatially fragmented cultivation, resulting in unstable yields and income. Remote sensing (RS) and geographic information systems (GIS) offer promising tools for the timely, spatially explicit monitoring of chili crops. Despite growing interest in agricultural applications of these technologies, no systematic review has yet synthesized how RS and GIS have been used in chili production. This systematic review addresses this gap by evaluating existing literature on methodological approaches and thematic trends in the use of RS and GIS in chili crop monitoring and management. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines a comprehensive literature search was conducted using predefined keywords across Scopus, Web of Science, and Google Scholar. Sixty-five peer-reviewed articles published through January 2025 were identified and grouped into different thematic areas: crop mapping, biotic stress, abiotic stress, land suitability, crop health, soil and fertilizer management, and others. The findings indicate RS predominantly serves as the primary analytical method (82% of studies), while GIS primarily supports spatial integration and visualization. Key research gaps identified include limitations in spatial resolution, insufficient integration of intelligent predictive models, and limited scalability for smallholder farming contexts. The review highlights the need for future research incorporating high-resolution RS data, advanced modelling techniques, and spatial decision-support frameworks. These insights aim to guide researchers, agronomists, and policymakers toward enhanced precision monitoring and digital innovation in chili crop production. Full article
(This article belongs to the Special Issue Advances in Multi-Sensor Remote Sensing for Vegetation Monitoring)
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