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High-Resolution Multisource Remote Sensing of Vegetation: Biomass, Structure, and Carbon...
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High-Resolution Multisource Remote Sensing of Vegetation: Biomass, Structure, and Carbon Dynamics

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1548

Special Issue Editors

Dr. Shangrong Lin

E-Mail Website
Guest Editor
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Interests: vegetation production; vegetation remote sensing; plant payment content; climate change
Prof. Dr. Gaofei Yin

E-Mail Website
Guest Editor
School of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 610031, China
Interests: vegetation biophysical parameters; retrieval methods; phenology; vegetation monitoring
Dr. Guodong Zhang

E-Mail Website
Guest Editor
School of Geosciences and Environment Engineering, Southwest Jiaotong University, Chengdu 610031, China
Interests: vegetation biophysical parameters; time series analysis; deep learning model; vegetation dynamics
Special Issues, Collections and Topics in MDPI journals
Dr. Xinyao Xie

E-Mail Website
Guest Editor
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China
Interests: vegetation productivity; mountainous areas; surface topography; process models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent years have seen the launch of an increasing number of satellites equipped with visible, near-infrared, Lidar, and microwave sensors, leading to the growing availability of high-spatial-resolution (less than 30 m) remote sensing data for the detection of vegetation dynamics. Scholars have developed and produced numerous types of high-spatial-resolution vegetation data, such as vegetation structural data (e.g., leaf area index, clumping index, vegetation cover, etc.), vegetation functional data (e.g., vegetation pigment content, photosynthetic capacity), and data on carbon dynamics (e.g., vegetation productivity, biomass), based on the latest high-spatial-resolution remote sensing data from Landsat, Sentinel-2, Planet Scope, Gaofen, and the Global Ecosystem Dynamics Investigation (GEDI) instrument series. These high-spatial-resolution data are highly beneficial to research fields addressing issues such as global change, food security, and forest ecosystem services, opening new doors for Earth science research.

This Special Issue aims to publish studies covering the high-spatial-resolution data acquired by multisource remote sensing platforms in vegetation and carbon dynamic sciences. Topics may cover, but are not limited to, the development, comparison, and validation of high-spatial-resolution remote sensing vegetation algorithms and products; novel high-spatial-resolution remote sensing vegetation data related to biomass, vegetation structure, and plant production; and vegetation or carbon dynamic analysis based on high-spatial-resolution remote sensing data.

  • Development of high-spatial-resolution remote sensing (HiRS) vegetation algorithms and products.
  • Comparison or validation of HiRS vegetation products.
  • Novel HiRS vegetation data related to biomass, vegetation structure and plant production, etc.
  • Carbon dynamic analysis based on HiRS vegetation products.
  • Monitoring vegetation dynamics with HiRS vegetation data.

Dr. Shangrong Lin
Prof. Dr. Gaofei Yin
Dr. Guodong Zhang
Dr. Xinyao Xie
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

  • high-spatial-resolution vegetation remote sensing product
  • carbon dynamic
  • biomass
  • vegetation structure parameters
  • vegetation monitoring
  • lidar based vegetation products

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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

18 pages, 2295 KB  
Article
Assessment of Vegetation Index Saturation Based on Vertically Stratified Aboveground Biomass in Temperate Meadow Steppe
by Yuli Shi, Yidi Wang, Yiqing Hao, Cong Xu, Fangwen Yang, Zhijie Bai, Dan Zhao, Xiaohua Zhu and Wei Liu
Remote Sens. 2026, 18(4), 554; https://doi.org/10.3390/rs18040554 - 10 Feb 2026
Viewed by 455
Abstract
Grassland aboveground biomass (AGB) is a key indicator of grassland ecosystem structure and function, and its accurate monitoring is of great importance for assessing grassland ecological conditions and supporting sustainable grassland management. Traditional biomass estimation methods based on vegetation indices (VIs) often suffer [...] Read more.
Grassland aboveground biomass (AGB) is a key indicator of grassland ecosystem structure and function, and its accurate monitoring is of great importance for assessing grassland ecological conditions and supporting sustainable grassland management. Traditional biomass estimation methods based on vegetation indices (VIs) often suffer from saturation due to canopy shading. However, comparative studies on VI saturation and the saturation height of AGB detectable by different indices remain limited. In this study, we evaluated 12 commonly used VIs based on field-measured AGB and hyperspectral data in the Hulunbuir meadow steppe. Relationships between vertically accumulated biomass and VIs were analyzed to identify optimal AGB fitting models and to determine the saturation height of each index. Results showed that vertical distribution of AGB followed a unimodal pattern, with biomass peaking at approximately 36 cm in this region. This study employed four models (namely the Linear model, the Logarithmic model, the Power Function model and the Gompertz model) to fit the relationship between the vegetation index and AGB. Among them, Gompertz models consistently outperformed other models, indicating saturation across all indices. Based on saturation height, the 12 VIs were classified into two groups: ARVI, GNDVI, NDRE, OSAVI, and SAVI saturated at 40 cm, whereas DVI, EVI, MSAVI, NDPI, NDVI, RVI, and VARI maintained sensitivity up to 50 cm, demonstrating a stronger anti-saturation capacity. NDVI and NDPI exhibited the highest fitting accuracy and resistance to saturation. These findings validate the saturation limitations of VIs and provide guidance for selecting appropriate indices to improve the accuracy of grassland biomass retrieval. Full article
(This article belongs to the Special Issue High-Resolution Multisource Remote Sensing of Vegetation: Biomass, Structure, and Carbon Dynamics)
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24 pages, 5060 KB  
Article
Enhancing Machine Learning-Based GPP Upscaling Error Correction: An Equidistant Sampling Method with Optimized Step Size and Intervals
by Zegen Wang, Jiaqi Zuo, Zhiwei Yong and Xinyao Xie
Remote Sens. 2026, 18(1), 23; https://doi.org/10.3390/rs18010023 - 22 Dec 2025
Viewed by 582
Abstract
Current machine learning-based gross primary productivity (GPP) upscaling error correction approaches exhibit two critical limitations: (1) failure to account for nonuniform density distributions of sub-pixel heterogeneity factors during upscaling and (2) dependence on subjective classification thresholds for characterizing factor variations. These shortcomings reduce [...] Read more.
Current machine learning-based gross primary productivity (GPP) upscaling error correction approaches exhibit two critical limitations: (1) failure to account for nonuniform density distributions of sub-pixel heterogeneity factors during upscaling and (2) dependence on subjective classification thresholds for characterizing factor variations. These shortcomings reduce accuracy and limit transferability. To address these issues, we propose an equidistant sampling method with optimized step size and intervals that precisely quantifies nonuniform density distributions and enhances correction precision. We validate our approach by applying it to correct 480 m resolution GPP simulations generated from an eco-hydrological model, with performance evaluation against 30 m resolution benchmarks using determination coefficient (R2) and root mean square error (RMSE). The proposed method demonstrates a significant improvement over previous elevation-based correction research (baseline R2 = 0.48, RMSE = 285 gCm−2yr−1), achieving a 0.27 increase in R2 and 91.22 gCm−2yr−1 reduction in RMSE. For comparative analysis, we implement k-means clustering as an alternative geostatistical method, which shows lesser improvements (ΔR2 = 0.21, ΔRMSE = −63.54 gCm−2yr−1). Crucially, when using identical statistical interval counts, our optimized-step equidistant sampling method consistently surpasses k-means clustering in performance metrics. The optimal-step equidistant sampling method, paired with appropriate interval selection, offers an efficient solution that maintains high correction accuracy while minimizing computational costs. Controlled variable experiments further revealed that the most significant factors affecting GPP upscaling error correction are land cover, altitude, slope, and TNI, trailed by LAI, whereas slope orientation, SVF, and TWI hold equal relevance. Full article
(This article belongs to the Special Issue High-Resolution Multisource Remote Sensing of Vegetation: Biomass, Structure, and Carbon Dynamics)
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