Abstract: Decrease in solar radiation is one of the main components of climate change. Studies aimed at examining the effects of decreased solar radiation on CH₄ emission and estimation of CH₄ emission based on hyperspectral data in paddy fields are still scarce. A field simulation experiment was conducted to investigate the effects of shading intensity on CH₄ emission in a paddy field and rice canopy hyperspectral properties. CH₄ emission flux was estimated with rice canopy hyperspectral data. The shading intensities were set at three levels, i.e. control (CK, no shading), light shading (S₁, 60% of shading rate), and heavy shading (S₂, 84% of shading rate). The results showed that shading significantly reduced CH₄ emission. However, CH₄ emission under heavy shading (S₂) was higher than that under light shading (S₁). The reflectance of the near-infrared spectrum on rice canopy from the jointing stage to grain filling stage was in the sequence of CK>S₂>S₁. The spectral reflectance on rice canopy was significantly and positively correlated with CH₄ flux in the near-infrared band (699-1349 nm), with a correlation coefficient of 0.64 (P<0.01). The six vegetation indices were significantly correlated with CH₄ flux. The correlation coefficient between Ratio Vegetation Index (RVI) and CH₄ flux was the largest, with R²=0.84 (P<0.01). The stepwise regression model with RVI, Normalized Difference Vegetation Index (NDVI), and 507 nm original reflectance (ρ₅₀₇) parameters was the best one (fitting model R²=0.86, prediction model R²=0.85) for estimating CH₄ emission.