Multisensor Data Fusion for Quantifying Agricultural Fire Impacts on Air Quality and Environmental Degradation

Agricultural fire residue in the Gujranwala Division of Punjab, Pakistan, has significant environmental consequences, with potential downstream impacts on coastal ecosystems through atmospheric pollutant transport. This study introduces a novel multi-sensor fusion methodology that enhances spatial-temporal resolution by 40% compared to single-sensor approaches, demonstrating scalable applications for coastal ecosystem monitoring. The framework's practical applications include real-time air quality assessment, agricultural policy formulation, and climate change adaptation strategies across similar agroecological zones in South Asia. Utilizing the Google Earth Engine (GEE) platform, we integrate heterogeneous datasets from MODIS (Moderate Resolution Imaging Spectroradiometer - thermal anomalies, land surface temperature), FIRMS (Fire Information for Resource Management System - fire activity), Sentinel-2 (vegetation indices: Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR)/differenced NBR (dNBR)), and TROPOMI (TROPOspheric Monitoring Instrument - CH<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub>, CO, NO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> concentrations) to evaluate pre-fire (March–April 2022) and post-fire (May–June 2022) conditions. Key findings reveal a 35% increase in fire activity post-fire (FIRMS values: 1006.4 to 1359.7), a 15% decline in vegetation health (EVI: 0.225 to 0.189), and elevated LST (29.2–44.0°C) correlating with increased greenhouse gas emissions (CH<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub>: 1881–1935 ppb; CO: 0.037–0.042 mol/m²). The dNBR analysis (-0.90 to 0.69) highlighted burn severity heterogeneity, underscoring the need for spatially adaptive mitigation strategies. This work demonstrates how multi-sensor fusion enhances spatialtemporal resolution and accuracy in environmental monitoring a framework directly applicable to coastal zones. For instance, integrating thermal, optical, and gas-emission data can address challenges in coastal mangrove ecosystem resilience, landcover dynamics, and pollutant flux modeling. Our approach provides a scalable model for assessing ecological vulnerabilities in coastal areas, where multi-sensor synergy is critical to capture complex interactions between terrestrial and marine systems. The results advocate for policy reforms to reduce crop residue burning and promote sustainable practices. This study underscores the broader relevance of multi-sensor fusion in advancing remote sensing capabilities for coastal management, particularly in quantifying cross-regional environmental impacts and informing integrated conservation strategies.