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Quantitative Remote Sensing of Vegetation and Its Applications

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 297

Special Issue Editors

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Guest Editor
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: remote sensing of vegetation; land cover/land use; remote sensing of ecological environment; agriculture remote sensing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Wilkes Center for Climate Science and Policy, School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
Interests: remote sensing of vegetation; modeling; forest ecophysiology; carbon science and climate change; climate change mitigation

Special Issue Information

Dear Colleagues,

Vegetation is the basic component of the terrestrial ecosystem and it plays an important role in energy exchange as well as biogeochemical and hydrological cycling processes on Earth’s surface. Quantitative remote sensing of vegetation can provide spatially and temporally continuous monitoring of Earth’s system parameter data and deliver invaluable insights into diverse fields such as agriculture, forestry, and environment. The past decades have witnessed great progress in satellite remote sensing data processing and the retrieval of Earth’s system parameter, as well as their applications. The advances in monitoring methodologies/technologies, such as empirical statistical models, radiative transfer models, artificial intelligence, and cloud computing technology, have improved the quality and accuracy of remote sensing products. Furthermore, remote sensing products play an increasingly critical role in resolving global environmental issues and climate change mitigation.

This aim of this Special Issue is to advance novel techniques/approaches for retrieving and estimating vegetation structure and function parameters at various spatial (e.g., leaf, canopy, stand, landscape, and regional levels) and temporal scales using remote sensing data across various ecosystems and vegetation types, as well as their applications such as in delineating the responses of vegetation structure and the function of climate change and disturbance in key ecological issues.

Potential topics for this Special Issue may include, but are not limited to, the following:

  • Satellite-based vegetation monitoring, estimation, and modeling: techniques (artificial intelligence, multi-sensor data fusion, etc.), evaluation, and future missions;
  • Applications of new sensors/algorithms to biochemical/biophysical parameters, such as FVC, LAI, vegetation productivity, biomass, pigments;
  • Novel data fusion of spectral, LiDAR, or Radar data obtained from different platforms;
  • New product development or evaluation of uncertainty in current products;
  • Vegetation degradation and structure variation monitoring using remote sensing;
  • Evaluations of ecosystem vulnerability and resilience to climate change;
  • Remote sensing applications in global environmental issues;
  • Remote sensing applications in efforts to mitigate climate change, such as nature-based climate solutions.

Prof. Dr. Kun Jia
Dr. Linqing Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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.


  • remote sensing
  • biochemical/biophysical parameters
  • vegetation dynamics
  • multi-sensor data fusion
  • algorithm development
  • artificial intelligence
  • accuracy validation
  • inter-comparison and evaluation
  • products and applications

Published Papers (1 paper)

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17 pages, 10875 KiB  
An Improved Gross Primary Production Model Considering Atmospheric CO2 Fertilization: The Qinghai–Tibet Plateau as a Case Study
by Jie Li, Kun Jia, Linlin Zhao, Guofeng Tao, Wenwu Zhao, Yanxu Liu, Yunjun Yao and Xiaotong Zhang
Remote Sens. 2024, 16(11), 1856; - 23 May 2024
Viewed by 132
Involving the effect of atmospheric CO2 fertilization is effective for improving the accuracy of estimating gross primary production (GPP) using light use efficiency (LUE) models. However, the widely used LUE model, the remote sensing-driven Carnegie–Ames–Stanford Approach (CASA) model, scarcely considers the effects [...] Read more.
Involving the effect of atmospheric CO2 fertilization is effective for improving the accuracy of estimating gross primary production (GPP) using light use efficiency (LUE) models. However, the widely used LUE model, the remote sensing-driven Carnegie–Ames–Stanford Approach (CASA) model, scarcely considers the effects of atmospheric CO2 fertilization, which causes GPP estimation uncertainties. Therefore, this study proposed an improved method for estimating GPP by integrating the atmospheric CO2 concentration into the CASA model and generated a long time series GPP dataset with high precision for the Qinghai–Tibet Plateau. The CASA model was improved by considering the impact of atmospheric CO2 on vegetation productivity and discerning variations in CO2 gradients within the canopy and leaves. A 500 m monthly GPP dataset for the Qinghai–Tibet Plateau from 2003 to 2020 was generated. The results showed that the improved GPP estimation model achieved better performances on estimating GPP (R2 = 0.68, RMSE = 406 g C/m2/year) than the original model (R2 = 0.67, RMSE = 499.32 g C/m2/year) and MODIS GPP products (R2 = 0.49, RMSE = 522.56 g C/m2/year). The GPP on the Qinghai–Tibet Plateau increased significantly with the increase in atmospheric CO2 concentration and the gradual accumulation of dry matter. The improved method can also be used for other regions and the generated GPP dataset is valuable for further understanding the ecosystem carbon cycles on the Qinghai–Tibet Plateau. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing of Vegetation and Its Applications)
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