Abstract: 5 segments moving average, baseline correction, area normalization, and multiplicative scatter correction (MSC) was used to preprocess Visible-NIR reflective spectrum of rice leaf. Successive projection algorithm (SPA) was used in the selecting of effective wavelengths. Multiple linear regression (MLR) models were built based on spectral indexes of RVI, NDVI and effective wavelengths selected by SPA. Principal components regression (PCR) models and Partial least squares regression (PLS) models were built based on all wavelengths in the spectrum. Nitrogen contents of rice leaves were predicted by these models. From comparison, It was found that the predictive validity of models based on SPA effective wavelengths were obviously better than models based on spectral indexes of RVI and NDVI, and slightly worse than PCR and PLS models based on all wavelengths in the spectrum. Models based on MSC preprocessed spectrum and SPA effective wavelengths has the predictive validity of r=0.7943, RMSE=0.4558. It is feasible to use successive projections algorithm in spectral monitoring of rice leaves nitrogen contents.