Deep learning of Sentinel-1 SAR for burnt peatland detection in Ireland

• Peatland monitoring through innovative fusion of Sentinel-1 radar imagery and AI. • Sentinel-1 radar data detects diverse phenomena like snowfall and peatland fires. • Escalation in peat fire occurrences, with the largest fire in 2023. Peatlands represent vital carbon reserves; however, once ignited, they release stored carbon, inflicting lasting environmental harm and necessitating prolonged recovery periods. An innovative method merging Sentinel-1 satellite imagery and deep learning (DL) is proposed to monitor peat fires across diverse regions of Ireland, regardless of weather conditions or time of day. A comprehensive dataset was assembled to train deep neural networks (DNN) using Sentinel-2 thermal band data and field measurement, resulting in 80% accuracy in detecting peat fire areas. This was achieved by incorporating inputs of both VV (vertical transmit, vertical receive) and VH (vertical transmit, horizontal receive) from Sentinel-1. Analysis of Sentinel-1 VV backscatter change time series for Wicklow Mountain in 2018 underscores the satellite's capacity to detect various phenomena, including snowfall and peat fire, evident prior to the peat fire event. Furthermore, scrutiny of peat fire occurrences in Wicklow Mountain from 2018 to 2023 via time series and mapping reveals a significant escalation, with the largest fire recorded in 2023 spanning over 40 km².