用自监督学习自动识别野火与烟羽，提升监测效率。
Development and Application of Self-Supervised Machine Learning for Smoke Plume and Active Fire Identification from the FIREX-AQ Datasets
- 融合多源遥感数据，通过自监督学习区分火点与烟羽。
- 成功生成各仪器独立及融合后的烟羽和火点掩码图。
- 适用于野火监测、空气质量管理和气候研究。
野火对区域至全球环境与空气质量的影响（FIREX-AQ）是一项旨在深入理解野火与农业焚烧对空气质量和气候影响的野外实验。该实验于2019年8月开展，包含两架飞机与多个协同卫星观测。本研究针对该实验期间获取的卫星与亚轨道遥感数据，应用并评估了一种自监督机器学习方法，用于主动火点与烟羽的识别与追踪。该方法创新性地结合了具有不同空间与光谱分辨率的遥感观测数据，能够有效区分火点与烟羽像素及背景影像，生成基于单一仪器的烟羽与火点掩码产品，以及来自独立仪器数据融合的掩码结果。该机器学习方法有望提升业务化野火监测系统的性能，并通过快速烟羽识别与追踪，支持空气质量决策；同时通过多源数据融合，助力气候影响研究。
原文摘要 · Abstract (English)
Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) was a field campaign aimed at better understanding the impact of wildfires and agricultural fires on air quality and climate. The FIREX-AQ campaign took place in August 2019 and involved two aircraft and multiple coordinated satellite observations. This study applied and evaluated a self-supervised machine learning (ML) method for the active fire and smoke plume identification and tracking in the satellite and sub-orbital remote sensing datasets collected during the campaign. Our unique methodology combines remote sensing observations with different spatial and spectral resolutions. The demonstrated approach successfully differentiates fire pixels and smoke plumes from background imagery, enabling the generation of a per-instrument smoke and fire mask product, as well as smoke and fire masks created from the fusion of selected data from independent instruments. This ML approach has a potential to enhance operational wildfire monitoring systems and improve decision-making in air quality management through fast smoke plume identification12 and tracking and could improve climate impact studies through fusion data from independent instruments.
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