Increased demand for grazing resources has prompted grassland productivity optimization through fertilization. Despite these initiatives, there is no comprehensive framework for monitoring productivity dynamics in fertilized grasslands. In this regard, we evaluated the potential of field-based hyperspectral data in characterizing foliar chlorophyll content of grass grown under complex fertilizer treatments. Data analysis was done using advanced regression methodologies. Our study showed that chlorophyll content significantly varies among grasses treated with different fertilizer combinations. Further, foliar chlorophyll content estimation results can be accurately derived from the combined use of hyperspectral multiband and vegetation indices. High accuracies were attained as indicated by the mean of squared residuals of 5.41  μg m^−  2, 90.72% of explained variance, root-mean-square error of 4.02  μg m^−  2, and r² of 0.91. In addition, the variable importance modeling depicted sR 435/835 nm; nDVI 415/735, nDVI 545/895, 720 nm, nDVI 515/ 835, and 800 nm as the key foliar chlorophyll predictor variables for the grasses fertilized with 11 combinations of ammonium nitrate and ammonium sulfate combined with lime and phosphorus, as well as a control. These findings underscore the utility of spectroscopic proximal data for the provision of inherent subtle grass characteristics and location-specific information required to inform optimal grassland management strategies.