Plant canopy pigment concentration is a critical variable for agricultural remote sensing due to its close relationship to leaf nitrogen content. The aims of this study were to: (1) compare the prediction performances on chlorophyll, chlorophyll-a and b, and carotenoid concentration in jujube leaf at canopy scale between partial least squares regression (PLSR) and support vector machine (SVM), (2) develop quantitative models to estimate pigment concentration in jujube canopy using hyperspectral data and provide theoretical and technical support for rapidly, non-destructive, less expensive and eco-friendly measuring the concentration. Results from correlation analysis showed that jujube canopy pigment concentration correlated strongly with hyperspectral data. What’s more, the hyperspectral data was better correlated by chlorophyll and chlorophyll-a than chlorophyll-b and carotenoid. Results of independent samples tested in predicting performance indicated that both of the PLSR and SVM models could effectively estimate pigment concentration, however, with different prediction precisions. Additionally, the precision of SVM outperformed PLSR for predicting chlorophyll and carotenoid. Whereas chlorophyll-a and chlorophyll-b were better predicted using PLSR than SVM. Compared among all the pigments’ prediction precisions with corresponding optimal inversion models showed that prediction precisions on chlorophyll, chlorophyll-a and carotenoid were superior to chlorophyll-b. The determination coefficients and residual prediction deviation from predicting chlorophyll, chlorophyll-a and carotenoid were higher than 0.8 and 2.0, respectively, while the mean relative error values were lower than 13%. And the corresponding values from predicting chlorophyll-b were 0.60%, 20.79% and 1.79% respectively.