Phase-Preserving Trimodal Transformer for Tropical Forest Biomass Estimation Using Optical and PolInSAR Data
arXiv:2607.03663v1 Announce Type: cross Abstract: The accurate estimation of Above-Ground Biomass (AGB) in mature tropical forests remains a critical challenge in remote sensing, primarily due to the saturation of Synthetic Aperture Radar (SAR) signals in high-density areas and persistent cloud cover affecting optical imagery. To overcome these physical limitations, we propose the Trimodal Coherent Co-attention Transformer (TCCT), a physics-informed deep learning architecture. The TCCT natively fuses optical surface reflectance (Landsat-5) with complex-valued Polarimetric SAR Interferometry (P
The continuous advancements in AI and deep learning, combined with the increasing availability of multi-modal remote sensing data, are enabling more sophisticated solutions to long-standing environmental monitoring challenges.
Accurate biomass estimation in tropical forests is crucial for climate change mitigation, carbon accounting, and forest conservation efforts, providing better data for policy and resource management.
This new trimodal transformer offers a more robust method for overcoming the historic limitations of single-sensor remote sensing in dense tropical biomass estimation, improving data reliability.
- · Climate scientists
- · Forestry management companies
- · Environmental monitoring agencies
- · Remote sensing technology developers
- · Traditional, less accurate biomass survey methods
- · Data models reliant solely on single-modality sensors
Improved monitoring of carbon sequestration and deforestation rates in critical tropical ecosystems.
More effective and data-driven policies and incentives for tropical forest conservation and sustainable land use.
Potential for integration into global carbon credit markets, enabling more precise valuation and verification of forest-based carbon offsets.
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Read at arXiv cs.AI