Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/24/2003
Publication Date: 1/4/2004
Citation: Fitzgerald, G.J. 2004. Portable hyperspectral tunable imaging system (phytis) - for precision agriculture. Agronomy Journal. 96:311-315. Interpretive Summary: Quantifying crop parameters through the use of remote sensing has been on-gong for several decades and has met with mixed success. Multispectral remote sensing has been used for many years to measure light in several discrete parts of the spectrum or wavebands. Hyperspectral remote sensing is an emerging technology where dozens to hundreds of narrow wavebands create a continuous spectrum of a scene permitting a more complete data set than multispectral data. A Portable Hyperspectral Tunable Imaging System (PHyTIS) is described that allows imaging of scene components such as leaves, soil, shade, and plants. The images produced from this camera system along with advanced image processing techniques permit development of spectral "signatures" of these scene components. These signature are unique spectra of each of the scene components. With these signatures, the various parts or fractions of a scene such as sunlit leaves, shaded leaves, sunlit soil, and shaded soil can be quntified. Quantification of these fractions can be used to produce crop cover maps and could be valuable as inputs to plant growth and irrigation models. Therefore, this research will benefit all growers and consumers of food and fiber.
Technical Abstract: Hyperspectral remote sensing system can provide a contiguous spectrum of a scene made up of dozens to hundreds of narrow wavebands across the visible and near-infrared portions of the spectrum. This emerging technology provides spatial and spectral information that can be acquired simultaneously. Presented here for use in agricultural research is the Portable Hyperspectral Tunable Imaging System (PHyTIS). It is a computer-controlled, liquid-crystal-tunable-filter, digital imaging system designed to allow extraction of scene components (endmembers) for spectral mixture analysis and to unmix scenes containing typical agronomic components such as sunlit and shaded leaves and soil. Results from a scene acquired in a cotton (Gossypium sepium) field showed that scene components could be successfully unmixed and quantified. Image processing and hyperspectral remote sensing can identify endmembers to quantify crop biophysical parameters to derive fractional cover maps and could be used as inputs to plant, soil, and evapotranspiration models.