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ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Sustainable and Intelligent Phytoprotection

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1623458

Self-supervised Disturbing Feature Reconstruction Network for Mangrove Biomass Estimation with Limited Data

Provisionally accepted
Jun  HaoJun Hao1,2,3Xiaowei  XuXiaowei Xu4Haiyan  XuHaiyan Xu2,3,5Gang  XuGang Xu2,3,5,6*
  • 1School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, China
  • 2Zhejiang College of Security Technology, Wenzhou, China
  • 3Wenzhou Future City Research Institute, Wenzhou, China
  • 4Wenzhou Forestry Technology Extension and Wildlife Protection Management Station, Wenzhou, China
  • 5Wenzhou Key Laboratory of Natural Disaster Remote Sensing Monitoring and Early Warning, Wenzhou, China
  • 6Wenzhou Collaborative Innovation Center for Space-borne, Airborne and Ground Monitoring Situational Awareness Technology, Wenzhou, China

The final, formatted version of the article will be published soon.

Accurate estimation of mangrove biomass is significant for ensuring the mangrove ecosystem productivity and global carbon cycling. Although well-known deep neural networks (DNNs) have been successfully applied in mangrove biomass estimation using remote sensing data, the key problem of data scarcity is not addressed very well for existing methods. Thus, a novel DNN called self-supervised disturbing feature reconstruction network (SSDFRN) is constructed in this article for mangrove biomass estimation with limited data. Firstly, a disturbed feature reconstruction-based self-supervised learning (DFRSSL) method based on random feature shuffle and disturbed feature reconstruction is proposed for solving the data scarcity problem. In addition, a multi-view convolution neural network (MVCNN) is constructed by stacking several multi-view cascaded convolution modules (MVCCMs), which effectively enhances feature learning performance and improves mangrove biomass estimation accuracy. The mangrove biomass dataset obtained from Ximen Island (28° 21′ N, 121° 10′ E) is used in this study to verify the outperformance of SSDFRN. The experimental results illustrate that SSDFRN is effective in deep feature learning and mangrove biomass estimation with limited data.

Keywords: Mangrove biomass estimation, Self-supervised learning, disturbed feature reconstruction, Multi-view convolution neural network, deep learning

Received: 22 May 2025; Accepted: 31 Jul 2025.

Copyright: © 2025 Hao, Xu, Xu and Xu. 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: Gang Xu, Zhejiang College of Security Technology, Wenzhou, China

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