Spatially distributed chlorophyll content of vegetation provide important information to learn the crop's health status.Recent advances in optical remote sensing led to improved methodologies to estimate the chlorophyll content of crops.Vegetation index as the most widely tool can be used for mapping chlorophyll content at region scale.In this paper, an aircraft with a CASI(Compact Airborne Spectral Imager) hyperspectral sensor on board overflew the Heihe River basin, in China obtaining imagerywithaspatialresolutionof~lm suitable to map the distribution of chlorophyllcontent.From the CASI data several spectral vegetation indices were calculated and linked with the laboratory chlorophyll content measurements.The results indicated that, of the selected vegetationindices, the TCARI/OSAVI-based model estimated the chlorophyll content (R2=0.67, RMSE=0.49 and p<0.01) with good accuracy, however, a new ratio spectral index RSI(1041.10, 511.80) developed by waveband combination algorithmobtainedmore higher linear correlation (R2=0.77, RMSE=0.41 and p<0.01) for estimating the chlorophyllcontent.This linear relationship was applied to the CASI image to generate an LNC map, which suggested that more fertilizer should be increased in the northwestregion due to its lower chlorophyll content values.