Dynamic analysis of hyperspectral vegetation indices

Crop physiology analysis and growth monitoring are important elements for precision agriculture management. Remote sensing technology supplies us more selections and available spaces in this dynamic change study by producing images of different spatial, spectral and temporal resolutions. Especially, the remote sensing data of high spectral and high temporal resolution will play a key role in land cover studies at national, regional and global scales. In this paper, Multi-temporal Index Image Cube (MIIC) is proposed, which is an effective data structure for the parameterization of multi-dimensions spectral curve. MIIC is very useful for supporting the dynamic analysis on vegetation phenological and physiological characters. Based on multi-temporal meteorological satellite data and multi-temporal ground spectral measurement data, the temporal characters of different vegetation physiological parameters are contrasted and analyzed from temporal index image cube. In addition, MIIC also has very wide use in hyperspectral remote sensing applications.