The quality of certain plants is considered to be a key factor affecting the food habitat or migration of some herbivorous species, and, thus, to estimate the spatial and temporal variation of plant quality is crucial for understanding the grazing and migrating behaviours of these herbivores. This study aimed to explore the possibilities of estimating plant protein and phosphorus contents, with the laboratory-based hyperspectral measurements of fresh Carex leaves, which are the main food source of many wintering bird species in Poyang Lake, China. Fifty-four Carex leaf samples were collected, and their hyperspectral reflectance (at 350–2500 nm) and crude protein and phosphorus contents were measured in the laboratory. The successive projections algorithm (SPA) was applied for spectral dimension reduction, and a multiple linear regression model was calibrated to estimate the crude protein and phosphorus contents from the wavelengths selected with the SPA. The model validation results showed that the root mean square errors (RMSEs) of estimation were 2.51% for crude protein and 0.06% for phosphorus. Compared with a multiple linear model with randomly selected inputs and full-spectrum partial least-square regression (PLSR), the multiple linear regression model combined with the SPA method exhibited a significant advantage in terms of accuracy in estimating the crude protein and phosphorus contents of Carex leaves.