【摘要】 中国新疆维吾尔自治区、宁夏回族自治区、内蒙古自治区等煤炭资源丰富的地区分布着数百个煤田火区。煤火自燃排放大量温室气体，其无组织性致检测与量化工作面临困难。然而，煤火甲烷在全球温室气体排放中的贡献是不可忽视的。鉴于卫星分辨率的限制，本研究采用地基遥感手段开展煤火源甲烷检测，利用2023年6月在新疆阜康煤田火区采集的宽幅全景地基高光谱影像集，结合甲烷在短波红外的光学敏感特征和高光谱混合像元分解等方法，提出了一套适用于不同地貌特征高温煤火源甲烷逃逸的检测算法，并针对各算法的检测效果进行对比验证和效果评价。结果表明：（1）与已有的甲烷反演指数CH4I（CH₄ Index）相比，本研究提出的修正的最小二乘图像增强算法MLSIE（Modified Least Squares Image Enhancement）、比值导数光谱解混算法RCH4I（Ratio-based Derivative Spectral Unmixing for CH₄ Index）和去阴影甲烷比值指数DSRCH4I（Deshadowed Spectral Ratio CH₄ Index）在煤火源甲烷检测中表现更佳；（2） 2DSRCH4I3、MLSIE（2.3μm）和RCH4I1算法对于地貌复杂的煤火区检测效果较好，其中，2DSRCH4I和MLSIE（2.3μm）算法也适用于地貌相对单一的山地煤火区，而RCH4I1算法更适用于泄露量大（燃烧剧烈）的活跃煤火区；（3） MLSIE（2.3μm）算法具有较强的普适性，2DSRCH4I3算法有效抑制伪影/假阳性，检测效果最佳；（4）本文提出算法检测出的煤火区甲烷羽流以与燃烧的火焰共存和以自由扩散形式逸出共两种形式存在。本研究可为检测煤火甲烷提供一种利用地基短波红外成像光谱仪的新方法，也从甲烷逃逸角度为开展煤火自燃的早期识别与预警提供了新思路。更多还原

【Abstract】 Coal-rich regions such as Xinjiang, Ningxia, and Inner Mongolia frequently experience spontaneous combustion of coal seams. This phenomenon releases large quantities of methane, which is a high-impact greenhouse gas. The unorganized and diffuse nature of these emissions introduces great challenges for detection and quantification, which often contribute to the so-called “missing carbon” sector in greenhouse gas inventories. Owing to the limitations of satellite resolution and the inadequate adaptability of existing methane detection technologies in rugged terrains, this study focuses on coal fire areas characterized by unorganized emissions. Using wide-swath groundbased hyperspectral imagery, we developed a novel detection methodology for methane emissions from individual coal fire sources in diverse terrains. The goal is to analyze methane escape patterns and assess latent risks of underground coal fires, which offer a new framework for early-warning systems.In this work, we investigated methane escape in two representative mountain coal fire areas located in Fukang, Xinjiang, using groundbased hyperspectral wide-swath imagery collected in June 2023. Seven distinct algorithms were applied: first, we proposed a Modified Least Squares Image Enhancement(MLSIE) algorithm by applying the L2-norm least squares regression principle to methane-sensitive spectral windows(1.66 and 2.3 μm) in the SWIR range. Second, we developed the Methane Ratio Derivative Spectral Unmixing(RDSU) algorithm, which is represented by RCH4I1 and RCH4I3. These algorithms incorporate the spectral ratio derivative unmixing technique and the spectral sensitivity of methane to suppress background interference from pseudo-coal fire regions while enhancing the spectral signature of methane. Third, by integrating factors such as cloud shadow detection, mineral content, combustion characteristics, and building-related features, we developed two methane ratio indices: one is 1 DSRCH4I3, which enhances the contrast between methane and artifacts; the other is 2 DSRCH4I3, which mitigates artifact interference and highlights methane-enriched areas.Our evaluation demonstrated the following results.(1) The proposed MLSIE, RDSU, and DSRCH4I algorithms significantly improved methane detection accuracy compared with the existing CH4I algorithm.(2) The 2 DSRCH4I3, MLSIE(2.3 μm), and RCH4I1 algorithms exhibited superior performance in complex terrains, while 2 DSRCH4I and MLSIE(2.3 μm) algorithms were also effective in relatively simple mountainous coal fire areas.(3) MLSIE(2.3 μm) displayed robust generalization ability. Moreover, 2 DSRCH4I3 effectively minimized artifacts and false positives, which led to superior detection accuracy. RCH4I1 also demonstrated clear detection efficacy in coal fire areas with significant methane leakage.(4) Methane plumes were detected in two distinct forms: one coexisting with combustion flames, and the other freely escaping into the surrounding atmosphere.This work presents a novel methodology for detecting methane emissions from high-temperature and panoramic coal fire sources with diverse geomorphic characteristics. The findings provide valuable technical support for the identification and assessment of underground coal fire risks. Furthermore, this work introduces a new approach to early-warning systems for coal fire disasters, with the use of methane escape as a diagnostic indicator of potential fire formation. However, we recognize that the influence of black carbon aerosols, which may interfere with mixed spectral signals, was not addressed. Future research could explore the dynamic quantification of methane plumes by integrating hyperspectral image spectral enhancement techniques to improve detection accuracy.更多还原