Chin J Plant Ecol ›› 2023, Vol. 47 ›› Issue (10): 1356-1374.DOI: 10.17521/cjpe.2023.0008

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A review of forest aboveground biomass estimation based on remote sensing data

HAO Qing1, HUANG Chang1,2,*()   

  1. 1College of Urban and Environmental Science, Northwest University, Xi’an 710127, China
    2Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Shannxi Key Laboratory for Carbon Neutral Technology, Northwest University, Xi’an 710127, China
  • Received:2023-01-11 Accepted:2023-05-30 Online:2023-10-20 Published:2023-11-23
  • Contact: * (


Forests are crucial terrestrial ecosystems with wide distribution and substantial biomass, playing a vital role in the global carbon cycle. The estimation of aboveground biomass (AGB) in forests serves as a significant indicator of ecosystem productivity and is pivotal for studying material cycles and global climate change. Traditional methods for AGB estimation rely on individual tree-scale or forest stand-scale tree physical structural information measurements, which are often time-consuming and labor-intensive to obtain. Remote sensing technology offers a solution for comprehensively and multi-temporally obtaining forest structural information in large scale, making it indispensable for forest AGB estimation. Therefore, it is important to review and summarize recent advancements in remote sensing techniques for estimating forest AGB to promote their application and guide the development of related industries. This paper presents a comprehensive overview of the principles and methods used for estimating forest AGB using optical data, synthetic aperture radar (SAR) data, and light detection and ranging (LiDAR) data. It also analyzes the current status of synergistic estimation of forest AGB using multiple remote sensing data sources. The study highlights three key findings: (1) The use of novel remote sensing data, such as high-resolution satellite imagery and Global Ecosystem Dynamics Investigation LiDAR data, is expanding the boundaries of spatial and temporal resolutions, providing enhanced data sources for forest AGB research. (2) Synergistic approaches that combine multiple remote sensing data sources show promise in improving the accuracy of forest AGB estimation, but further optimization of related models is needed. (3) Machine learning, artificial intelligence, and deep learning techniques have been widely applied in forest AGB estimation, but continuous research on remote sensing mechanisms remains essential for innovation. Improvements in models and methodologies should revolve around a better understanding of these mechanisms.

Key words: forest aboveground biomass, optical remote sensing, synthetic aperture radar, light detection and ranging, multi-source remote sensing data collaboration