With the increasing use of natural gas, gas emissions (predominantly methane) from natural gas infrastructure are exacerbating the greenhouse effect. Therefore, it is crucial to detect natural gas leaks in a timely manner. The remote sensing of plants offers the potential to identify leakage from the reflectance responses of the plants to elevated concentrations of natural gas in the soil. Solar-induced chlorophyll fluorescence (SIF) is directly related to photosynthesis and is thus proposed to track the impact of natural gas leakage on vegetation growth. In order to explore the feasibility and advantage of SIF for detection of natural gas leaks, a field experiment of natural gas microleakage is performed, and soybean and grass are chosen as the test plants. Sixteen plots, eight with soybean and eight with grass, are established, and four of each plant are chosen for the injection of natural gas into the soil. The other plots act as controls. We present weekly ground measurements of canopy spectra over plots of gassed and ungassed soybean and grass. SIF at 760 nm (F760) and other published vegetation indices are calculated and analyzed. The indices can identify grasses under natural gas microleakage stress throughout the whole experiment, whereas only F760 can identify soybeans under natural gas microleakage stress during the later stage of the experiment. When the canopy chlorophyll content of soybean is at high levels, F760 can still increase along with it, whereas other indices fail to increase because of saturation. The results suggest that the F760 index can stably and reliably identify plants under natural gas microleakage stress. Therefore, in the future, the location of leakage of natural gas can be detected by monitoring the F760 of plants with hyperspectral remote sensing.