An illustration of the outcome of this sort of experiment is given as Figure 2A, which plots the oxygen level 1 mm below the surface of an oilseed rape seed. In the absence of light, the level is < 2 ��M, but when illuminated with 673 ��mol quanta m-2 s-1, it rises instantaneously to almost 700 ��M, and stabilizes at ~ 600 ��
Remote sensing is playing an increasingly important role in earth science research and environmental problem solving. A number of earth satellites have been launched to advance our understanding of Earth��s environment. Satellite sensors, both active and passive, capture data from visible to microwave regions of the electromagnetic spectrum.
A wide range of satellite data, including multispectral data and hyperspectral data, such as Landsat Thematic Mapper 5/Enhanced Thematic Mapper (TM/ETM+); Global Imager (GLI); Moderate Resolution Imaging Spectroradiometer (MODIS); and Advanced Land Imager (ALI) and Hyperion, are frequently used in oceanography, hydrology, geology, forestry, and meteorology studies. Different studies and applications require different spatial, spectral, radiant resolution, and time-resolution data [1,2]. Hyperspectral sensors monitor hundreds of spectral bands and can provide near-laboratory quality reflectance spectra. The data produced, referred to as hyperspectral data, contain much more information than multispectral data and have greatly extended the range of remote sensing applications [3,4]. Unfortunately, hyperspectral data are much more difficult and expensive to acquire and were not available prior to the development of operational hyperspectral instruments.
On the other hand, large amounts of accumulated multispectral data have been collected GSK-3 around the world over the past several decades, therefore it is reasonable to examine means of using these multispectral data to simulate or construct hyperspectral data, especially in situations where the latter are necessary but hard to acquire. Many studies have examined methods to simulate or construct hyperspectral and multispectral data spectra from field spectra or to aggregate spectra of hyperspectral bands into multispectral bands. However, few attempts have been made to simulate hyperspectral data from multispectral data [2, 5�C9]. In this paper, we propose a method, based on a spectral reconstruction approach, to simulate hyperspectral data from multispectral data.
Data simulation is widely used in remote sensing. It is often utilized to produce imagery for virtual or new sensors that are in the design stage. Simulated data can be used to assess or evaluate the spectral and spatial characteristics of the sensor, which are critical in the planning of a project [8]. NASA has developed a system to simulate imagery to meet customer needs and costs in a virtual environment (http://www.esad.ssc.nasa.gov/art/).