[Comparison and analysis of hyperspectral remote sensing identifiable models for different vegetation under...

With the global climate warming, flooding disasters frequently occurred and its influence scope constantly increased in China. The objective of the present paper was to study the leaf spectral features of vegetation (maize and beetroot) under waterlogging stress and design a hyperspectral remote sensing model to monitor the flooding disasters through a field simulated experiment. The experiment was carried out in the Sutton Bonington Campus of University of Nottingham (52.8 degrees N, 1. 2 degrees W) from May to August in 2008, and samples were collected one time every week and spectra were measured in the laboratory. The result showed that the reflectance of the maize and beetroot decreased in the 550 and 800-1 300 nm region, and the reflectance slightly increased in the 680 nm region. This paper chose NDVI, SIPI, PRI, SRPI, GNDVI and R800 * R550/R680 to identify the vegetation under waterlogging stress, respectively. The result suggested that the SIPI and R800 * R550/R680 was sensitive for maize under waterlogging stress, and then SIPI and PRI and R800 * R550/R680 was sensitive for beetroot under waterlogging stress. In order to seek the best identifiable model, the normalized distances between means of control and stressed vegetation indices were calculated and analyzed, the result indicated that the distance of R800 * R550/R680 is more than that of indices' in the early stress stage, illustrated that the index identifiable ability for waterlogging stress is better than other indices, then the index has the strong sensitivity and stability. Therefore, the index R800 * R550/R680 could be used to quickly extract flooding disaster area by using hyperspectral remote sensing, and would provide information support for disaster relief decisions.