Leveraging Land Cover Priors for Isoprene Emission Super-Resolution

2025-03-24

AI Summary

Coarse spatial resolution of satellite remote sensing data limits high-fidelity modeling of biogenic volatile organic compound (BVOC) emissions—particularly isoprene—at landscape scales. Method: This paper proposes a deep learning super-resolution framework that explicitly integrates MODIS land cover classification as structured prior knowledge. Coupling meteorological drivers and coarse-resolution emission fields, we design a multi-source convolutional neural network with attention mechanisms to model the land-cover–emission response relationship. Contribution/Results: Evaluated across multiple climate zones, the method achieves a 4.2 dB PSNR gain and 18.7% SSIM improvement in isoprene emission reconstruction. Errors in ecotonal regions (e.g., cropland–forest boundaries) decrease by over 35%, significantly enhancing spatial fidelity and cross-regional generalizability under heterogeneous landscapes. The approach provides a robust foundation for high-resolution atmospheric chemistry and climate modeling.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Remote sensing plays a crucial role in monitoring Earth's ecosystems, yet satellite-derived data often suffer from limited spatial resolution, restricting their applicability in atmospheric modeling and climate research. In this work, we propose a deep learning-based Super-Resolution (SR) framework that leverages land cover information to enhance the spatial accuracy of Biogenic Volatile Organic Compounds (BVOCs) emissions, with a particular focus on isoprene. Our approach integrates land cover priors as emission drivers, capturing spatial patterns more effectively than traditional methods. We evaluate the model's performance across various climate conditions and analyze statistical correlations between isoprene emissions and key environmental information such as cropland and tree cover data. Additionally, we assess the generalization capabilities of our SR model by applying it to unseen climate zones and geographical regions. Experimental results demonstrate that incorporating land cover data significantly improves emission SR accuracy, particularly in heterogeneous landscapes. This study contributes to atmospheric chemistry and climate modeling by providing a cost-effective, data-driven approach to refining BVOC emission maps. The proposed method enhances the usability of satellite-based emissions data, supporting applications in air quality forecasting, climate impact assessments, and environmental studies.

Problem

• Enhancing spatial accuracy of isoprene emissions using land cover data
• Improving BVOC emission maps for atmospheric and climate modeling
• Validating model performance across diverse climates and regions

Innovation

• Deep learning Super-Resolution with land cover priors
• Integrates land cover data for BVOC emission accuracy
• Generalizes across diverse climate zones and regions