Gross primary production (GPP) is a crucial indicator representing the absorption of atmospheric CO2 by vegetation. At present, the estimation of GPP by remote sensing is mainly based on leaf-related vegetation indexes and leaf-related biophysical parameter leaf area index (LAI), which are not completely synchronized in seasonality with GPP. In this study, we proposed chlorophyll content-based light use efficiency model (CC-LUE) to improve GPP estimates, as chlorophyll is the direct site of photosynthesis, and only the light absorbed by chlorophyll is used in the photosynthetic process. The CC-LUE model is constructed by establishing a linear correlation between satellite-derived canopy chlorophyll content (Chlcanopy) and FPAR. This method was calibrated and validated utilizing 7-d averaged in-situ GPP data from 14 eddy covariance flux towers covering deciduous broadleaf forest ecosystems across five different climate zones. Results showed a relatively robust seasonal consistency between Chlcanopy with GPP in deciduous broadleaf forests under different climatic conditions. The CC-LUE model explained 88% of the in-situ GPP seasonality for all validation site-year and 56.0% of in-situ GPP variations through the growing season, outperforming the three widely used LUE models (MODIS-GPP algorithm, Vegetation Photosynthesis Model (VPM), and the eddy covariance-light use efficiency model (EC-LUE)). Additionally, the CC-LUE model (RMSE = 0.50 g C/(m2d)) significantly improved the underestimation of GPP during the growing season in semi-arid region, remarkably decreasing the root mean square error of averaged growing season GPP simulation and in-situ GPP by 75.4%, 73.4%, and 37.5%, compared with MOD17 (RMSE = 2.03 g C/(m2d)), VPM (RMSE = 1.88 g C/(m2d)), and EC-LUE (RMSE = 0.80 g C/(m2d)) model. The chlorophyll-based method proved superior in capturing the seasonal variations of GPP in forest ecosystems, thereby providing the possibility of a more precise depiction of forest seasonal carbon uptake.