REVIEW article
Front. Remote Sens.
Sec. Atmospheric Remote Sensing
Volume 6 - 2025 | doi: 10.3389/frsen.2025.1668676
Exploring the Utility of Remote Sensing Technology in Vegetation Below Ground Biomass (BGB) Estimation: A Critical Review of Methods and Challenges
Provisionally accepted
- 1University of KwaZulu-Natal, South Africa, Durban (PMB campus), KwaZulu-Natal, South Africa
- 2University of Nottingham Malaysia, Semenyih, Malaysia
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Understanding vegetation Below Ground Biomass (BGB) dynamics is essential to ensure long-term ecological functions such as carbon sequestration and optimizing critical tuber crops productivity. Whereas the utility of remote sensing in assessing vegetation Above Ground Biomass (AGB) is well documented in literature, studies using this technology to estimate BGB have become elusive due to technical challenges of direct underground sensing. Therefore, this study aims to critically review the methods and challenges in adopting remote sensing technology for estimating vegetation BGB, while proposing a consolidated approach for improving the accuracy of subsurface biomass assessment. The review indicates that although remote sensors do not directly measure underground, variations in BGB can be inferred through deriving canopy vegetation indices, where machine learning algorithms and empirical relationships play a crucial role in extrapolating these indices to predict subsurface biomass. While optical multispectral and hyperspectral sensors provide critical canopy biophysical information, offering invaluable insights about BGB status, these cameras are constrained by atmospheric interference and inability to penetrate dense vegetation. Active remote sensing cameras such as LiDAR do not provide biophysical information, however, they stand out for their ability to penetrate atmospheric conditions, dense vegetation, and provide topographic information, that can improve BGB estimation. Amongst the challenges highlighted, the review raises concerns about the reliability of using the remote sensing of vegetation AGB status and canopy spectral reflectance for estimating BGB, considering the influence of seasonality in crown cover fluctuations. Nevertheless, advances in Unmanned Aerial Vehicle (UAV) platforms coupled with smart optical and active sensors remain promising for accurately assessing vegetation BGB while overcoming various limitations such as low spatial resolution, long revisit cycles, and atmospheric influence. This review has consolidated methods for estimating vegetation and crop BGB, allowing researchers to evaluate their choice of technique based on the tradeoffs between sensors spectral characteristics, spatial coverages, and practicality.
Keywords: remote sensing, Below ground biomass, vegetation, tuber crops, methods, Challenges
Received: 18 Jul 2025; Accepted: 30 Sep 2025.
Copyright: © 2025 Dlamini, Odindi, Matongera and Mutanga. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Celuxolo Dlamini, 219003508@stu.ukzn.ac.za
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