Crop identification and land cover estimation are essential for farming and land management practices in the precision agriculture field. Conventional measurements are expensive and time-consuming and thus cannot be treated as appropriate for large areas. An automatic crop or land classification should be applied to overcome these problems. Therefore, high-quality data availability is required to feed the classification tools. To fulfill the needs, we have used an airborne system for collecting in the Taiwan agriculture area. A VNIR hyperspectral image has been proven to significantly increasing accuracy compared to an RGB image. With simple discriminant algorithm LD and QD, the classification accuracy of VNIR images reaches 88.14 % and 92.02%, respectively. Meanwhile, RGB images attain 52.73% and 52.27%.