Monitoring crop phenology is of great importance for vegetation classification, yield estimation, and irrigation and fertilization management. To test the ability of ground-based remote sensing in detecting major phenological dates of rice, canopy spectra were collected by two portable spectrometers. The Red-edge Chlorophyll Index (CI red edge ) and Normalized Difference Vegetation Index (NDVI) time-series derived from ground-based spectrometers were employed to detect the main specific phenological dates. CI red edge was obtained from ASD FieldSpec Pro spectrometer, while NDVI was from ASD FieldSpec Pro spectrometer (referred to as NDVI ASD ) and GreenSeeker RT 100 (referred to as NDVI GS ). The phenology detection method consists of two procedures: (i) smoothing the temporal CI red edge and NDVI data with the double logistic regression function to represent intra-annual vegetation dynamics, (ii) determining the phenological dates through extracting the maximum, minimum and zero-crossing points (FD max , FD min and FD zero ) from the first derivative value of the smoothed NDVI and CI red edge temporal profiles. A comparison of remote sensing-based estimates with field observations over three growing seasons with different cultivars, planting densities and nitrogen (N) rates showed that CI red edge can accurately estimate the dates of jointing, middle booting and dough grain. NDVI from both spectrometers can be used to detect the dates of active tillering, middle heading and maturity. Specifically, NDVI GS yielded better performance than NDVI ASD for estimating the three phenological dates. Compared with growing season and planting density, rice cultivar and N rate exhibited more significant impact on the accuracy for phenology detection. This work has great potential to provide valuable support for assessing crop growth status and providing precise management strategy. The dates of active tillering, jointing and maturity detected from a combination of CI red edge and NDVI could be useful for irrigation and fertilization management, and harvest determination, respectively. [ABSTRACT FROM AUTHOR]