煤层气是一种存在于煤层及其周围岩层中的自生自储式非常规清洁能源。研究表明，油气富集区烃类物质会沿着上覆盖层的断裂、节理、微裂隙及孔隙向地表渗漏，这被称为烃微渗漏现象。煤层气富集区的烃微渗漏，改变了土壤的化学组成和化学环境，影响植物根系的呼吸作用，进而阻碍植被叶绿体合成，抑制植被光合作用，最终导致日光诱导叶绿素荧光（Solar-induced chlorophyll Fluorescence，SIF）的异常变化。因此，植被SIF的异常变化，是煤层气潜在富集区遥感勘查的主要依据之一。本文以山西省沁水盆地南部（沁水县）为研究区，使用基于卷积神经网络（Convolutional Neural Networks，CNN）的SIF降尺度方法，得到了2000年至2020年之间研究区的1 km SIF数据，并在此基础上将研究区植被分为农田、草地和林地三种类型，分类讨论了煤层气富集区植被SIF时空变化特征。最后，通过对比研究区与对照区，分析并阐明煤层气富集区植被SIF异常变化的可能原因。本文研究结果表明：三种植被类型中，林地的SIF平均值最高，其次为草地，农田的SIF平均值最低；2000年至2020年研究区内各植被类型均表现出SIF明显增长的趋势，研究区植被6月-9月SIF增速分别为0.0058、0.0052、0.0036、0.0021 W·m-2·μm-1·sr-1·a-1；煤层气富集区受人工因素影响较小的林地SIF值明显低于对照区，2000年至2020年SIF年平均增速也低于对照区，这表明林地受到煤层气富集区烃微渗漏的胁迫作用与其它影响因素相比，可能性更大。这实证了煤层气富集区的烃微渗漏可能对植被SIF有显著影响。

Objective: Coalbed methane (CBM) is a self-generated, self-storage, unconventional clean energy source which exists in coal seams and surrounding rock formations. Studies have shown that hydrocarbon can leak to the surface along the fractures, joints, microfractures, and pores of the upper overburden in oil or gas-rich areas, known as hydrocarbon micro-seepage. In CBM-enriched areas, hydrocarbon micro-seepage changes the chemical composition and chemical environment, and blocks the respiration of plant roots, so that the chloroplast synthesis process will be hindered, inhibiting photosynthesis and vegetation growth, eventually leading to abnormal changes in vegetation solar-induced chlorophyll fluorescence (SIF). Therefore, the abnormality of vegetation SIF can be an essential clue for remote sensing surveys of potential CBM enrichment areas. Method: In this paper, convolutional neural networks (CNNs) are applied to the downscaling of SIF. 1-km SIF data of the study area between 2000 and 2020 are obtained, taking the southern part of Qinshui Basin (Qinshui County) in Shanxi Province as the study area. The vegetation-covered region in the study area is classified into farmland, grassland, and woodland. The spatial and temporal variation characteristics of vegetation SIF in CBM-enriched areas are categorized and discussed. At the same time, the possible reasons for the abnormal variation of vegetation SIF in CBM-enriched areas are analyzed and elucidated through the comparison between the study area and the control area. Result: The results show that among the three vegetation types, the average SIF of woodland is the highest, followed by grassland, and the average SIF of farmland is the lowest; each vegetation type in the study area shows a noticeable trend of SIF growth from 2000 to 2020, and the SIF growth rates of vegetation in the study area from June to September are 0.0058, 0.0052, 0.0036, 0.0021 W·m-2·μm-1·sr-1·a-1; Conclusion: In this paper, it is found that the SIF values of woodland in the CBM-enriched area that are less affected by artificial factors are significantly lower than those of the control area, and the average growth rate from 2000 to 2020 is also lower than that of the control area. This indicates that forest land is more likely to be subjected to the stress of hydrocarbon micro leakage in coalbed methane enriched areas compared to other influencing factors. This empirically shows that hydrocarbon micro-seepage in CBM-enriched areas may have a significant impact on vegetation SIF.