Flood vulnerabilities of various flooded entities are dependent on flooding process, e.g. time period, flooding intensity, spatial and temporal distribution, especially for growing crops affected. This information can not be obtained by classical methods (like field survey, middle to high-resolution images and hydraulic models) due to factors of laboring, cost, complicacy and accuracy. It is necessary to develop a new approach of lower cost and reasonable accuracy to gain our ends. This paper made an experimental study of linear unmixing method to try to approximate our goal with easy-to-get,
high frequent revisit, though low spatial resolution NOAA AVHRR image in Poyang lake region of Jiangxi province, P.R.China. This region is located at north Jiangxi province; its low flat plain topography has very little multiplicative spectral reflections, and is suitable for linear unmixing application. After analyzing the histograms of NOAA AVHRR images, we decided to use the difference image of band-2, band-1 and minus band-2 to differentiate water from others. After application of linear unmixing, we assumed area fraction of water in each pixel is approximately equal to calculated spectral fraction of water in each pixel (this is virtually true when there is little multiplicative reflection and refraction), then fine geo-referencing and spatial assignment were made based on the relation between the element (water) to be spatially assigned and other high-resolution thematic factor DEM. This study provide a new Earth Observation
approach for continuously monitoring distribution change of relatively homogeneous large-scale features, like waters, desert, oil spillage extension etc.