Abstract: Using Hyperion hyperspectral image acquired over Xishuangbanna in Yunnan Province, China, the relationships between first-derivative reflectance of Hyperion and nitrogen and chlorophyll concentration were established using multivariable stepwise linear regression. Results show that Hyperion reflectance after 6S atmospheric correction　is consistent with field-measured canopy reflectance, NDVI computed from reflectance after 6S correction is higher than those from absolute radiance and apparent reflectance, and the former is closest to that from field measured. Most of the selected wavelengths in models predicting nitrogen and chlorophyll are related to absorption of protein, R² are 0.586 and 0.506, respectively. Spatial distribution of nitrogen and chlorophyll concentration at canopy level is produced, the results show that nitrogen concentration of rice is the highest, and it is 2.5%～3.5%; the next are most crops with value of 1.0%～2.5%, and nitrogen concentration of forest is 1.0%～1.5%. As for chlorophyll, rice and potato have the highest chlorophyll concentration, the value is 25%～35%; the next are corn and sugar cane with value of 20%～30%, and chlorophyll concentration of chestnut is 20%～25%. This demonstrates that hyperspectral image is an effective way to estimate biochemical components of vegetation at large scale.