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Advances in Estimating Aboveground Biomass Based on Multi-source Remote Sensing...
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Advances in Estimating Aboveground Biomass Based on Multi-source Remote Sensing Data

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

Deadline for manuscript submissions: 25 May 2026 | Viewed by 8477

Special Issue Editors

Dr. Shaobo Sun

E-Mail Website
Guest Editor
Institute of Surface-Earth System Science, Tianjin University, Tianjin, China
Interests: remote sensing in ecology; carbon fluxes; GEE; ecosystem carbon stocks
Prof. Dr. Xufeng Wang

E-Mail Website
Guest Editor
Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: vegetation responses to climate change; vegetation remote sensing; drought detection
Special Issues, Collections and Topics in MDPI journals
Dr. Kai Liu

E-Mail Website
Guest Editor
School of Geography and Planning, Center of Integrated Geographic Information Analysis, Sun Yat-sen University, Guangzhou 510275, China
Interests: UAV; LiDAR; machine learning; vegetation index; leaf area index; mangrove forests; WorldView-2 imagery; red-edge band; variable importance

Special Issue Information

Dear Colleagues,

Above-ground biomass（AGB) is a critical proxy for productivity and the most dynamic terrestrial carbon pool. The accurate determination of AGB is critical in monitoring plants’ growth and assessing terrestrial carbon budgets. Remote sensing provides the most practical and effective approach for estimating AGB from a local to a global scale. In particular, the emerging open and high spatiotemporal resolution microwave and optical sensors (such as the Sentinel sensors) and the development of LiDAR and unmanned aerial vehicles (UAVs) allows an unprecedented opportunity for the accurate estimation of AGB. Considerable amounts of multi-source remote sensing data and popular cloud computing platforms such as Google Earth Engine (GEE) make it easy to rapidly monitor AGB in different ecosystems at low costs.  AGB estimates based on multi-source remote sensing data, new algorithms and well-calibrated models have a wide range of applications, such as assessing terrestrial carbon budgets, monitoring crop growth, and examining the impacts of climate change and human activities on vegetation.

The Special Issue aims to call for papers on recent advances to estimate the AGB of different ecosystems (such as forests, grasslands, croplands, and wetlands) using multi-source remote sensing data at different scales

Topics may include but are not limited to model and algorithm developments, the assessment of different methods, responses of the AGB to climate change and human activities, and applications of remotely sensed AGB data.

Dr. Shaobo Sun
Prof. Dr. Xufeng Wang
Dr. Kai Liu
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

  • above ground biomass
  • LiDAR
  • machine learning
  • multi-source remote sensing
  • vegetation carbon stocks
  • unmanned aerial vehicle (UAV)

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Published Papers (3 papers)

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26 pages, 19498 KB  
Article
Estimation of Forest Aboveground Biomass in China Based on GEDI and Sentinel-2 Data: Quantitative Analysis of Optical Remote Sensing Saturation Effect and Terrain Compensation Mechanisms
by Jiarun Wang, Chengzhi Xiang and Ailin Liang
Remote Sens. 2025, 17(20), 3437; https://doi.org/10.3390/rs17203437 - 15 Oct 2025
Viewed by 1264
Abstract
Forests store substantial amounts of aboveground biomass (AGB) and play a critical role in the global carbon cycle. Optical remote sensing offers long-term, large-scale monitoring capabilities; however, spectral saturation in high-biomass regions limits the accuracy of AGB estimation. Although radar and LiDAR data [...] Read more.
Forests store substantial amounts of aboveground biomass (AGB) and play a critical role in the global carbon cycle. Optical remote sensing offers long-term, large-scale monitoring capabilities; however, spectral saturation in high-biomass regions limits the accuracy of AGB estimation. Although radar and LiDAR data can mitigate the saturation problem, optical imagery remains irreplaceable for continuous, multi-decadal monitoring from regional to global scales. Nevertheless, quantitative analyses of nationwide optical saturation thresholds and compensation mechanisms are still lacking. In this study, we integrated high-accuracy AGB estimates from the Global Ecosystem Dynamics Investigation (GEDI) L4A product, Sentinel-2 optical imagery, and topographic variables to develop a 200 m resolution Light Gradient Boosting Machine (LightGBM) machine learning model for forests in China. Stratified error analysis, locally weighted scatterplot smoothing (LOWESS) curves, and SHapley Additive exPlanations (SHAP) were employed to quantify optical saturation thresholds and the compensatory effects of topographic features. Results showed that estimation accuracy declined markedly when AGB exceeded approximately 300 Mg·ha−1. Red and red-edge bands saturated at around 80 Mg·ha−1, while certain spectral indices delayed the threshold to 100–150 Mg·ha−1. Topographic features maintained stable contributions below 300 Mg·ha−1, providing critical compensation for AGB prediction in high-biomass areas. This study delivers a high-resolution national AGB dataset and a transferable analytical framework for saturation mechanisms, offering methodological insights for large-scale, long-term optical AGB monitoring. Full article
(This article belongs to the Special Issue Advances in Estimating Aboveground Biomass Based on Multi-source Remote Sensing Data)
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19 pages, 5297 KB  
Article
Estimating the Aboveground Biomass of Various Forest Types with High Heterogeneity at the Provincial Scale Based on Multi-Source Data
by Tianbao Huang, Guanglong Ou, Yong Wu, Xiaoli Zhang, Zihao Liu, Hui Xu, Xiongwei Xu, Zhenghui Wang and Can Xu
Remote Sens. 2023, 15(14), 3550; https://doi.org/10.3390/rs15143550 - 14 Jul 2023
Cited by 29 | Viewed by 4219
Abstract
It is important to improve the accuracy of models estimating aboveground biomass (AGB) in large areas with complex geography and high forest heterogeneity. In this study, k-nearest neighbors (k-NN), gradient boosting machine (GBM), random forest (RF), quantile random forest (QRF), regularized random forest [...] Read more.
It is important to improve the accuracy of models estimating aboveground biomass (AGB) in large areas with complex geography and high forest heterogeneity. In this study, k-nearest neighbors (k-NN), gradient boosting machine (GBM), random forest (RF), quantile random forest (QRF), regularized random forest (RRF), and Bayesian regularization neural network (BRNN) machine learning algorithms were constructed to estimate the AGB of four forest types based on environmental factors and the variables selected by the Boruta algorithm in Yunnan Province and using integrated Landsat 8 OLI and Sentinel 2A images. The results showed that (1) DEM was the most important variable for estimating the AGB of coniferous forests, evergreen broadleaved forests, deciduous broadleaved forests, and mixed forests; while the vegetation index was the most important variable for estimating deciduous broadleaved forests, the climatic factors had a higher variable importance for estimating coniferous and mixed forests, and texture features and vegetation index had a higher variable importance for estimating evergreen broadleaved forests. (2) In terms of specific model performance for the four forest types, RRF was the best model both in estimating the AGB of coniferous forests and mixed forests; the R2 and RMSE for coniferous forests were 0.63 and 43.23 Mg ha−1, respectively, and the R2 and RMSE for mixed forests were 0.56 and 47.79 Mg ha−1, respectively. BRNN performed the best in estimating the AGB of evergreen broadleaved forests; the R2 was 0.53 and the RMSE was 68.16 Mg ha−1. QRF was the best in estimating the AGB of deciduous broadleaved forests, with R2 of 0.43 and RMSE of 45.09 Mg ha−1. (3) RRF was the best model for the four forest types according to the mean values, with R2 and RMSE of 0.503 and 52.335 Mg ha−1, respectively. In conclusion, different variables and suitable models should be considered when estimating the AGB of different forest types. This study could provide a reference for the estimation of forest AGB based on remote sensing in complex terrain areas with a high degree of forest heterogeneity. Full article
(This article belongs to the Special Issue Advances in Estimating Aboveground Biomass Based on Multi-source Remote Sensing Data)
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15 pages, 3404 KB  
Technical Note
Estimating Biomass Carbon Stocks of Inner Mongolia Grasslands Using Multi-Source Data
by Yong Liu, Shaobo Sun, Xiaolei Yang, Xufeng Wang, Kai Liu and Haibo Dong
Remote Sens. 2025, 17(1), 29; https://doi.org/10.3390/rs17010029 - 26 Dec 2024
Cited by 3 | Viewed by 1841
Abstract
Accurate estimates of biomass C stocks of grasslands are crucial for grassland management and climate change mitigation efforts. Here, we estimated the mean C stocks of grasslands in the Inner Mongolia Autonomous Region (IMAR), China, in 2020 at a 10 m spatial resolution [...] Read more.
Accurate estimates of biomass C stocks of grasslands are crucial for grassland management and climate change mitigation efforts. Here, we estimated the mean C stocks of grasslands in the Inner Mongolia Autonomous Region (IMAR), China, in 2020 at a 10 m spatial resolution by combining multi-source data, including remote sensing, climate, topography, soil properties, and field surveys. We used the random forest model to estimate the aboveground biomass (AGB) of grasslands, achieving an R2 value of 0.83. We established a relationship between belowground biomass (BGB) and AGB using a power function based on field data, which allows us to estimate the BGB of grasslands from our AGB estimate. We estimated the mean AGB across IMAR to be 100.7 g m−2, with a total value of 1.4 × 108 t. The BGB of grasslands is much higher than AGB, with mean and total values of 526.0 g m−2 and 7.4 × 108 t, respectively. Consequently, our C stock estimates show that IMAR grasslands store significantly more C in their BGB (332.6 Tg C) compared to AGB (63.7 Tg C). Random forest model analyses suggested that remotely sensed vegetation indices and soil moisture are the most important predictors for estimating the AGB of grasslands in the IMAR. We highlight the important role of BGB for the C store in the Inner Mongolia grasslands. Full article
(This article belongs to the Special Issue Advances in Estimating Aboveground Biomass Based on Multi-source Remote Sensing Data)
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