摘要： This paper explores the feasibility of deriving multispectral-based effective chlorophyll indicators (MECIs) for foliage chlorophyll concentration (CHLS) estimation. An average fusion method was applied to simulate the multispectral reflectance of the WorldView-3 sensor using hyperspectral data. With the experimental data of CHLS and predictors derived from multispectral reflectance, a series of linear regression analyses were carried out to derive appropriate models for CHLS estimation. Accuracy measures of RMSE and PRMSE were used to evaluate the model performance. Results showed that the coastal-band based MECI (MECIc) and the blue-band based MECI (MECIb) were able to achieve an RMSE of 0.5657 mg/g and 0.5943 mg/g as well as a PRMSE of 36% and 38% respectively. Using the Red edge and Yellow reflectance based NDVI (NDVIREY) as a predictor, the model can reduce uncertainty and achieve an estimation of 0.4089 mg/g and 26% for RMSE and PRMSE respectively. The prediction error made by the CHLS-NDVIREY model and the CHLS-MECI model were 11% and 60% larger than 0.38 mg/g the RMSE of hyperspectral-based CHLS-ECI model. In summary, NDVIREY was able to achieve a better prediction at around a level of 75% accuracy (1-PRMSE) and therefore is able to be an effective indicator of CHLS for forest monitoring.