Abstract: Abstract: Rapid and accurate monitoring leaf chlorophyll content is a critical step to explore the photosynthesis, nitrogen nutrition, and stress status of crops. Furthermore, vegetation index and radiation transfer model are widely used to determine the chlorophyll content in crops using the sun-induced chlorophyll fluorescence (SIF) technology. In this study, a monitoring model of chlorophyll content was established using linear regression and radiation transfer models, further to evaluate the accuracy and applicability for the rice growth and field management. A field plot test was carried out at the experimental base of National Engineering Technology Center for Information Agriculture (NETCIA) in Nantong City, Jiangsu Province, China. Different varieties of rice, densities and nitrogen application rates were set during test from May to October 2018. The radiance and reflectivity spectra of canopy and leaf scales were obtained using two hyperspectral spectrometers, thereby to calculate the chlorophyll content using Dualex instruments. Various SIF and vegetation indices were also calculated, where Fraunhofer line discrimination (FLD) was utilized to extract the canopy fluorescence intensities at 687 and 761 nm. A chlorophyll content model was then constructed using the canopy SIF intensity and leaf SIF index, where a reflectivity vegetation index was utilized to compare with the predicted one. A look-up table was also prepared for the inversion of rice canopy and leaf chlorophyll content using the SCOPE and Fluspect-B models. PROSAIL and PROCWT models were also selected to assess the hyperspectral reflectance inversion. Root mean square error (RMSE) and determination coefficient (R2) were used to evaluate the accuracy of chlorophyll content inversion. The results showed that: 1) The canopy SIF intensity F761 presented the strongest correlation with the canopy chlorophyll content (R2 = 0.72), better than the red edge chlorophyll index (CI red edge, R2=0.63); 2) The downward SIF index at the leaf scale behaved the strongest correlation (R2= 0.77) with the leaf chlorophyll content, compared with the upward SIF index, similar to the best performing vegetation index CIred edge (R2=0.81). 3) The PROSAIL model was used for the inversion of canopy chlorophyll content (R2 = 0.91, RMSE = 22.59 μg/cm), better than the SCOPE model (R2 =0.57, RMSE = 56.54 μg/cm); 4) The PROCWT model was used for the inversion of leaf chlorophyll content (R2 = 0.72, RMSE = 6.42 μg/cm), better than the Fluspect-B model (R2 = 0.55, RMSE = 19.45 μg/cm). Consequently, the SIF index demonstrated a better performance on the chlorophyll content at canopy and leaf scales. An excellent feasibility was also found in the radiation transport model using SIF to invert the chlorophyll content. The findings can provide a promising theoretical support to monitor the crop chlorophyll content for plant photosynthesis using SIF in the future. The next step can be recommended to maximize the number of test samples for a higher inversion accuracy of Fluspect-B and SCOPE radiation transfer models.