A Conceptual Framework for Remote Sensing Solution for Peatland Greenhouse Gas Emission Estimation in Latvia

Abstract Peatlands play a critical role in the global carbon cycle, acting as long-term carbon sinks but becoming significant greenhouse gas sources when drained or degraded. Accurate estimation of carbon dioxide and methane emissions from peatlands is essential for climate change mitigation and land management. Traditional field-based measurement techniques, such as eddy covariance and chamber systems, are spatially limited and resource intensive. Remote sensing provides a scalable and cost-effective alternative for monitoring surface and vegetation characteristics linked to carbon dynamics. This study presents a comprehensive review of remote-sensing-based methods for estimating greenhouse gas emissions from peatlands and evaluates the data readiness in Latvia for national-scale implementation. Two primary approaches are identified: land-cover-based estimation using emission factors and regression-based modelling of carbon fluxes derived from vegetation and temperature indices. Latvia possesses extensive open-access datasets, including Sentinel-2 satellite imagery, Light Detection and Ranging data, and national habitat inventories, which enable reproducible and transparent monitoring. The proposed conceptual framework integrates these resources into a unified, automated workflow for Greenhouse Gas emission estimation, establishing a foundation for operational peatland greenhouse gas monitoring and reporting.