Based on the in situ data collected in August 2010, hyperspectral data models estimating summer chlorophyll-a concentration in Lake Qiandao are presented. A large quantity of hyperspectral reflectance data and water quality data of the typical area of the lake were obtained. Hyperspectral data were measured using ASD FieldSpec3, and were calculated for water-leaving radiance and reflectance of water. Different methods including band ratio model, the first derivative model, three-band-model and BP neural network model were used to estimate chlorophyll-a concentration. Results showed that single band reflectance model gave the worst estimation on chlorophyll-a concentration. Band ratio model with the ratio of reflectance 596 nm/489 nm and the first derivative model of reflectance near 545 nm gave better results with high determination coefficients of 0.782 and 0.590, respectively. By comparison, the three-band-model had higher estimation precision (coefficient of 0.838) than the band ratio model and the first derivative model. BP neural network model performed best with a high determination coefficient of 0.942. The root mean square error between measured and estimated chlorophyll-a concentrations using the four models was 0.89, 1.98, 0.71 and 0.63 μg/L, respectively. Therefore, three-band-model and BP neural network model was recommended to estimate chlorophyll-a concentration with remote sensing data for large area of Lake Qiandao in the summer.