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<p>Accurate modeling of forest aboveground biomass (AGB) in cloud-prone tropical mountains remains constrained by irregular optical remote-sensing observations and limited field-reference data. This study proposes a 10&#xa0;m AGB modeling framework that combines Sentinel-2 time-series data with regression training based on synthetic samples. Sentinel-2 observations from 2019 to 2021 were folded into target-year annual sequences and encoded as weekly spectral tensors to preserve irregular cloud-free observation patterns. Field-derived AGB samples from northern Vietnam were spatially aggregated to 10&#xa0;m resolution and used to generate approximately 128,000 synthetic training samples through weighted spectral–biomass mixing. A one-dimensional convolutional neural network (1D-CNN) regression model was then trained using the synthetic samples and evaluated against independent field-derived test samples. The synthetic-data model (Model-S) achieved MAE = 1.19&#xa0;kg m⁻², RMSE = 1.61&#xa0;kg m⁻², and R² = 0.87 across all validation locations, improving MAE by 32%, RMSE by 31%, and R² by 18% relative to the model trained only with real samples (Model-R). The resulting 10&#xa0;m AGB map revealed moderate regional biomass levels and localized high-biomass patches in the western mountainous areas. This framework provides a scalable approach for biomass mapping in regions where field plots are sparse and Sentinel-2 optical time series are irregular.</p>

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Modeling forest aboveground biomass in cloud-prone tropical mountains using temporally encoded Sentinel-2 time series and synthetic training data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Thi Nhat Minh Doan,
  • Vu Dong Pham,
  • Van Manh Vu,
  • Dinh Hung Nguyen,
  • Felipe Bravo

摘要

Accurate modeling of forest aboveground biomass (AGB) in cloud-prone tropical mountains remains constrained by irregular optical remote-sensing observations and limited field-reference data. This study proposes a 10 m AGB modeling framework that combines Sentinel-2 time-series data with regression training based on synthetic samples. Sentinel-2 observations from 2019 to 2021 were folded into target-year annual sequences and encoded as weekly spectral tensors to preserve irregular cloud-free observation patterns. Field-derived AGB samples from northern Vietnam were spatially aggregated to 10 m resolution and used to generate approximately 128,000 synthetic training samples through weighted spectral–biomass mixing. A one-dimensional convolutional neural network (1D-CNN) regression model was then trained using the synthetic samples and evaluated against independent field-derived test samples. The synthetic-data model (Model-S) achieved MAE = 1.19 kg m⁻², RMSE = 1.61 kg m⁻², and R² = 0.87 across all validation locations, improving MAE by 32%, RMSE by 31%, and R² by 18% relative to the model trained only with real samples (Model-R). The resulting 10 m AGB map revealed moderate regional biomass levels and localized high-biomass patches in the western mountainous areas. This framework provides a scalable approach for biomass mapping in regions where field plots are sparse and Sentinel-2 optical time series are irregular.