天然气作为一种清洁、 高效的低碳能源, 消费占比日益增大。 无论是地下输气管道还是储气库, 由于管道腐蚀、 老化、 自然灾害, 地下断层、 注入井封存不好等因素, 都会导致天然气泄漏。 从安全、 经济、 环境等方面考虑, 开展地下天然气管道和储气库微泄漏检测是十分必要的。 利用高光谱遥感监测地表植被变化而间接探测天然气微泄漏点, 通过野外可控系统模拟地下储存天然气微泄漏实验, 以冬小麦为研究对象, 采集了9期小麦冠层光谱数据, 通过光谱分析探寻胁迫小麦光谱特征并构建指数识别模型。 首先对小麦冠层光谱进行奇异值剔除和平滑处理, 对连续统去除之后的冠层光谱进行连续小波变换, 选用Mexihat母小波, 在尺度参数为32时, 小波系数有较少的峰值和谷值, 能与原始光谱拟合较好, 且小麦多期数据其峰值和谷值位置都比较稳定。 受胁迫和健康小麦的原始光谱可分性较差, 但小波系数在487, 550和770 nm处受胁迫与健康小麦样本可分性较优, 且具有明显的诊断性特征: (1)受胁迫和健康小麦的小波系数在487 nm处为“吸收谷”, 其小波系数值为负值, 健康小麦小波系数值大于受胁迫小麦的; (2)受胁迫和健康小麦的小波系数在550和770 nm处, 有明显的“反射峰”, 且受胁迫小麦的小波系数值较大。 为更好突出差异性, 增强受胁迫和健康小麦的小波系数差异特征, 构建了CWTmexh(CWTmexh=CW2770/(1-CW487)·CW550)指数用于胁迫与健康小麦的识别; 然后分别与NDVI705, mNDVI705, ARI1, R440/R740, D725/D702指数进行对比分析, 经J-M距离定量检验, 结果显示CWTmexh指数对天然气微泄漏胁迫下的冬小麦具有较好的识别效果, 该指数在天然气胁迫发生20 d后可以稳定区分胁迫和健康两类小麦, 且在全生育期都保持相同的规律, 而NDVI705, mNDVI705, ARI1等指数在整个生育期内无法准确识别健康与胁迫小麦。 CWTmexh指数在稳定性、 普适性与可识别性方面优于其他5个指数。 因此, 高光谱遥感监测地表植被间接识别天然气微泄漏点具有可行性, 研究结果可为星载高光谱遥感监测地下储存天然气泄漏点提供理论依据和技术支持。

As a clean, efficient low-carbon energy source, natural gas accounts for an increasing proportion of consumption. For underground gas pipelines, gas storage, and the like, natural gas leakage will occur due to factors such as pipeline corrosion, aging, natural disasters, underground faults, and the bad sealing of injection-wells. In terms of security, economic, environmental and other considerations, micro-leak detection of underground natural gas pipeline and gas storage is essential. In this paper, we use hyperspectral remote sensing to monitor surface vegetation changes, thus indirectly detecting natural gas micro-leakage points. The field controllable system is used to simulate the underground micro-leakage experiment. Winter wheat is used in this study, and a time series of 9 experiments of canopy spectral collecting were conducted. Spectral analysis was used to identify and exploit the spectral characteristics of stress wheat and thereby constructing an index recognition model. Firstly, the wheat canopy spectrum is subjected to the processing of singular value culling and smoothing, and then the continuous spectrum wavelet transform is performed on the canopy spectrum after continuum removal. Specifically, mother wavelet of Mexihat is selected. When the scale parameter is 32, the wavelet coefficients have fewer peaks and valleys, which can fit well with the original spectrum, and the peak and valley positions of wheat multi-phase data are relatively stable. The original spectrum of stress and healthy wheat was poorly separable, but separability of proposed model using the wavelet coefficients at 487, 550 and 770 nm was better among wheat samples, and had obvious diagnostic characteristics: (1) The wavelet coefficient of stress and healthy wheat having “absorption valley” at 487 nm, the wavelet coefficient value being negative, and the wavelet coefficient of healthy wheat being larger than that of stressed wheat; (2) the wavelet coefficient of stress and healthy wheat is 550 nm at 770 nm, where clear “reflection peak” can be observed, and the wavelet coefficient of stressed wheat is larger. In order to better highlight the differences of wavelet coefficients of stressed and healthy wheat, the index CWTmexh(CWTmexh=CW2770/(1-CW487)·CW550) was constructed for the identification of stress and healthy wheat. Compared with the index NDVI705, mNDVI705, ARI1, R440/R740, D725/D702 and J-M distance quantitative test, the results show that the CWTmexh has a better recognition performance on winter wheat under natural gas micro-leakage stress. The CWTmexh can stably distinguish between stress and health after 20 days of natural gas stress, and maintain the same performance in the whole growth period, while the indexes of NDVI705, mNDVI705, ARI1 and so on, can not accurately identify throughout the growth period. The CWTmexh index is superior to the other five indexes in terms of stability, universality and the ability to recognition. Therefore, it is feasible to indirectly identify natural gas micro-leakage points by monitoring surface vegetation using hyperspectral remote sensing. The results can provide theoretical basis and technical support for monitoring underground gas leakage points by satellite-borne hyperspectral remote sensing.