Research on canopy height inversion based on Sentinel-1/2 spatio-temporal fusion model

doi:10.13474/j.cnki.11-2246.2026.0312

Abstract

Abstract: To address the limitations in forest canopy height estimation,including low efficiency and high costs of field monitoring,insufficient resolution of single-source remote sensing data,and lack of underlying surface parameters.This study proposes a multi-source spatio-temporal fusion deep learning model named SST-CLT,based on a ConvLSTM-Transformer hybrid neural network.The model integrates Sentinel-1 time-series SAR data,Sentinel-2 multi-spectral data,and airborne LiDAR-derived canopy height reference data to enhance inversion accuracy through collaborative spatio-temporal-spectral feature modeling.The results demonstrate excellent performance: the training set achieved R²=0.93,RMSE=3.10 m,and MAE=2.39 m,while the validation set yielded R²=0.84,RMSE=4.54 m,and MAE=3.00 m.The overall sample set reached R²=0.89,RMSE=3.81 m,MAE=2.65 m,with generated canopy height maps accurately characterizing spatial heterogeneity.The SST-CLT model exhibits both high precision and strong generalization capability,providing reliable technical support for forest resource dynamic monitoring and ecosystem studies.

Key words: forest canopy height, forest biomass, airborne LiDAR, Autokeras, SST-CLT model

CLC Number:

P237

ZHANG Wenwen, ZHOU Wei, WANG Jie. Research on canopy height inversion based on Sentinel-1/2 spatio-temporal fusion model[J]. Bulletin of Surveying and Mapping, 2026, 0(3): 68-74.

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