For in-season site-specific nitrogen (N) management of rice to be successful, it is crucially important to diagnose rice N status efficiently across large area in a timely fashion. Satellite remote sensing provides a promising technology for crop growth monitoring and precision management over large areas. The FORMOSAT-2 satellite remote sensing imageries with 4 wavebands have been used to estimate rice N status. The objective of this study was to evaluate the potential of using high spatial resolution satellites with red-edge band (RapidEye and WorldView-2) to improve monitoring rice N status in Northeast China. N rate experiments were conducted from 2008 thru 2009 and 2011 at Jiansanjiang, Heilongjiang Province of Northeast China. Field samples and hyperspectral data were collected at thepanicle initiation (PI), stem elongation (SE), and heading (HE)stages.Handheld hyperspectral data measured at canopy scale were used to simulate the wavebands of three satellite sensors-FORMOSAT-2, RapidEye, and WorldView-2. A linear regression analysis using the simulated satellite single band as the variable was applied to assess the potentials of the three satellite sensors for N nutritional status diagnosis. In addition, vegetation indices (VIs) were computed based on the simulated satellite wavebands. The results indicated the NIR1 band was most important for estimating all the N status indicators. According to the R² values, the regression models based on the simulated WorldView-2 wavebands had the highest performance for biomass, plant N uptake (PNU), and nitrogen nutrition index (NNI) estimations, followed by the ones based on the RapidEyewavebands, at each of the three stages. The red-edge band improved biomass, PNU, and NNI estimations at all three stages, especially at the early PI and SE stages. Biomass and PNU were best estimated using data across the stages while NNI and plant nitrogen concentration (PNC) were best estimated at the HE stage. For VI analysis, 30-40% biomass variability was explained using the Chlorophyll Index (CI) at thePI and SE stages. Likewise, 39-52% PNU variability was explained using the CI based on the FORMOSAT-2wavebands. The best VIs based on RapidEye and WorldView-2 wavebands explained 53-64% biomass variability, and 62-65% PNU variability.For the NNI estimation, the N planar domain index (NPDI) based on WorldView-2 wavebands and MERIS terrestrial chlorophyll index (MTCI) based on RapidEyewavebands explained 14-26% more variability.