Abstract: Abstract: The fast, real-time and non-destructive monitoring of double-cropping rice tiller number has important practical significance for growth diagnosis and yield prediction. Hyperspectral sensing has been proved effective to estimate the rice growth parameters, such as the chlorophyll content, leaf area index and biomass, yet few investigations pay attention to the tiller number. The objective of this study was to establish a regulation model for estimating double-cropping rice tiller number based on the hyperspectral reflectance across a wide range of growth stages (tillering stage, jointing stage, and booting stage). In the presented study, the tiller number and hyperspectral reflectance data were firstly obtained from two double-cropping rice field experiments, which encompassed variations in two years, four cultivars and five nitrogen application rates. Then the sensitive spectral indices and wavelet features were extracted from the hyperspectral reflectance data through spectral indices approach and continuous wavelet analysis, respectively. Finally, the regression models for tiller number estimation based on sensitive spectral indices and wavelet features were developed and validated using independent field experiment datasets. The results suggested that the newly developed spectral indices and sensitive wavelet features with red-edge bands performed better than the published vegetation indices and 'three edge' parameters. The normalized different spectral index named NDSI (ρ975, ρ714) was strongly related to the early rice tiller number. It had a determination coefficient (R2) of 0.724 in calibration and relative root mean square error (RRMSE) of 0.151 invalidations. The ratio spectral index RSI (ρ788, ρ738) strongly related to the late rice tiller number with R2 of 0.792 and RRMSE of 0.142 in calibration and validation, respectively. Compared with the published vegetation indices, 'three edge' parameters and newly developed spectral indices, the sensitive wavelet features observed in the red-edge region with high scales (2^9 and 2^6) performed best in the double-cropping rice tiller number estimation. The wavelet feature named db7 (s9, w735) was strongest related to the early rice tiller number. It had R2 of 0.754 in calibration and RRMSE of 0.128 invalidations. The wavelet feature named mexh (s6, w714) was strongest related to the late rice tiller number. It had R2 of 0.837 in calibration and RRMSE of 0.112 invalidations. Additionally, the sensitive spectral indices and wavelet features also could reduce the saturation effect with low noise equivalent (NE). It meant that in the condition the optical sensors equip few bands, the spectral indices NDSI (ρ975, ρ714) and RSI (ρ788, ρ738) could be used to monitor the early rice and late rice tiller number. Furthermore, the wavelet features db7 (s9, w735) and (s6, w714) could improve the accuracy for monitoring double-cropping rice tiller number based on the hyperspectral reflectance data with monitoring models of TNearly=3.632×db7 (s9, w735)+7.318 and TNlate=-15.351×mexh (s6, w714)+8.173, respectively.