Abstract. Water deficits can cause chlorophyll degradation which decreases the total concentration of chlorophyll a and b (Chls). Few studies have investigated the effectiveness of spectral indices under water-stressed conditions. Chlorophyll meters have been extensively used for a wide variety of leaf chlorophyll and nitrogen estimations. Since a chlorophyll meter works by sensing leaves absorptance and transmittance, the reading of chlorophyll concentration will be affected by changes in transmittance as if there were a water deficit in the leaves. The overall objective of this paper was to develop a novel and reliable reflectance-based model for estimating Chls of fresh and water-stressed leaves using the reflectance at the absorption bands of chlorophyll a and b and the red edge spectrum. Three independent experiments were designed to collect data from three leaf sample sets for the construction and validation of Chls estimation models. First, a reflectance experiment was conducted to collect foliar Chls and reflectance of leaves with varying water stress using the ASD FieldSpec spectroradiometer. Second, a chlorophyll meter (SPAD-502) experiment was carried out to collect foliar Chls and meter readings. These two data sets were separately used for developing reflectance-based or absorptance-based Chls estimation models using linear and nonlinear regression analysis. Suitable models were suggested mainly based on the coefficient of determination (R2). Finally, an experiment was conducted to collect the third data set for the validation of Chls models using the root mean squared error (RMSE) and the mean absolute error (MAE). In all of the experiments, the observations (real values) of the foliar Chls were extracted from acetone solution and determined by using a Hitachi U-2000 spectrophotometer. The spectral indices in the form of reflectance ratio/difference/slope derived from the Chl b absorption bands (645 and 455) provided Chls estimates with RMSE around 0.400.55 mg g1 for both fresh and water-stressed samples. We improved Chls prediction accuracy by incorporating the reflectance at red edge position (REP) in regression models. An effective chlorophyll indicator with the form of (645455)/REP proved to be the most accurate and stable predictor for foliar Chls concentration. This model was derived with an R2 of 0.90 (P < 0.01) from the training samples and evaluated with RMSE 0.35 and 0.38 mg g1 for the validation samples of fresh and water-stressed leaves, respectively. The average prediction error was within 14% of the mean absolute error.