Leaf area index estimation from hyperspectral data using group division method

In this study, optical remote sensing data were used for leaf area index (LAI) estimation. The LAI is an important measure to increase the yield and adjust the quantity of manure. LAI extracted from remotely sensed data may contribute to grasp the yield of rice at an early stage. Therefore, the purpose of this study is to estimate the LAI through remote sensing. For the purpose of our work, we proposed a Group Division Method. The method can decide a set of optical bands for estimating the value of LAI. The index is extracted by comparing the order of ground truth data with that of spectral data. As a result, the effective index to estimate LAI is made by reflectance relations in 545 nm, 1170 nm and 1290 nm from the hyper-spectral data of rice field in Sakata City, Yamagata prefecture. Furthermore, we applied the index to the data set obtained in Furukawa, Miyagi prefecture to verify the effectiveness of the method. Finally the ¿LAI estimate map¿ was made and examined whether this study helped an automatic LAI estimate in wide area.