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Forest and Grassland Resources Research >

2025 , Vol. 0 >Issue 4: 101 - 111

DOI: https://doi.org/10.13466/j.cnki.lczyyj.2025.04.011

Technical Application

Forest above ground biomass inversion using machine learning and sentinel data

  • Gao LIU ,
  • Zeqi XIE ,
  • Jianhao ZHOU ,
  • Lipeng LIAO
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  • 1. Zhengzhou Sias University,Zhengzhou 450000,China
    2. College of Information and Management Science,Henan Agricultural University,Zhengzhou 450002,China
    3. School of Information and Engineering,Zhengzhou University,Zhengzhou 450001,China

Received date: 2024-09-11

  Revised date: 2025-07-30

  Online published: 2026-02-13

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Abstract

To investigate the potential of synergistic active-passive remote sensing for estimating forest aboveground biomass (AGB),with the central urban area of Guangzhou as the study area,31 multi-source remote sensing features (including 6 SAR features and 25 optical features) were extracted through Sentinel-1 SAR data and Sentinel-2 multispectral imagery.Combined with field-measured AGB,six machine-learning (ML) regression models (Random Forest,Support Vector Machine,Extreme Gradient Boosting,k-Nearest Neighbors regression,AdaBoost,and Linear Regression) were used to develop forest AGB inversion models.The results showed that:1) The Visible Atmospherically Resistant Index Green (VIGreen) performed prominently in forest AGB inversion,ranking fifth in feature importance in the Random Forest (RF) model;different polarization combinations also contributed significantly to AGB inversion;2) Across different dataset combinations,the RF model achieved the highest accuracy among the six regression models;3) Models using only optical data outperformed those using only SAR data;4) Fusion of SAR and optical data produced substantially higher AGB inversion accuracy than using SAR or optical data alone:compared with SAR-only features,the coefficient of determination (R2) increased by 0.48 and the root mean square error (RMSE) decreased by 3.73;compared with optical-only features,R2 increased by 0.14 and RMSE decreased by 2.08.Therefore,ML approaches that integrate optical and SAR data can effectively improve the accuracy of forest AGB inversion.

Key words： forest biomass inversion; sentinel data; machine learning; random forest

Cite this article

Gao LIU , Zeqi XIE , Jianhao ZHOU , Lipeng LIAO . Forest above ground biomass inversion using machine learning and sentinel data[J]. Forest and Grassland Resources Research, 2025 , 0(4) : 101 -111 . DOI: 10.13466/j.cnki.lczyyj.2025.04.011

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