LAI Estimation from HS-Data using Group Division Method

Abstract

With recent population growth and global warming, stable food supply is an urgent requirement on a global scale. To cope with this demand, effective use of remote sensing data attracts attention. Among all, Leaf Area Index (LAI) extracted from remotely sensed data may contribute to the increase of yield and adjustment of quantity of manure, if we can automatically estimate the LAI over a wide area to grasp a yield of paddy. Therefore the purpose of this study is to estimate the LAI through remote sensing. In this paper, “Group division method” is proposed to decide a set of bands to be used for the LAI estimation, because the information obtained by a hyper spectrum sensor is enormous. This technique is to decide an index by comparing the order of ground truth data with that of the index based on spectral data. An effective index to estimate LAI is made by reflectances in 545nm, 1170nm and 1290nm using the data from the rice field of Sakata City, Yamagata Pref. as training data. Furthermore, we applied the index to the data set obtained in Furukawa, Miyagi Pref. to verify the effectiveness of the method. Finally we show a “LAI estimate map” and examine whether this study can contribute to estimate the LAI distribution over the wide area.