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United States Department of Agriculture

Agricultural Research Service

Title: Shadow Fraction in Spectral Mixture Analysis of a Cotton Canopy

Authors
item Fitzgerald, Glenn
item Pinter Jr, Paul
item Hunsaker, Douglas
item Clarke, Thomas

Submitted to: Proceedings of the International Symposium on Optical Science and Technology
Publication Type: Proceedings
Publication Acceptance Date: July 8, 2004
Publication Date: November 5, 2004
Citation: Fitzgerald, G.J., Pinter Jr, P.J., Hunsaker, D.J., Clarke, T.R. 2004. Shadow fraction in spectral mixture analysis of a cotton canopy. In Wel Gao and David R. Shaw (eds.) Proceedings of SPIE - The International Society for Optical Engfineering, Remote Sensing and Modeling of Ecosystems for Sustainability, August 2-4, 2004, Denver, Colorado, 5544:20-34.

Interpretive Summary: Remotely-sensed images of agricultural fields can provide a diverse array of information about the components in a field, including plant health, soil, stressed plants, and shadows. Spectral mixture analysis is an advanced image processing method that can determine the type and amount of each component within each pixel of an image. The method is generally used to analyze hyperspectral imagery, which is composed of dozens to hundreds of wavebands from which spectra can be extracted. Spectral mixture analysis compares the actual pixel spectra with a reference library of spectra of 'pure' components and determines the fraction of components contained in each pixel. Shadows often confound analysis of imagery, and this approach allows their estimation and separates them from the components of interest, such as plants and soil. In this study, different types of shadows were identified to improve the ability of hyperspectral imagery to estimate the four crop factors, percent cover, crop height, leaf greenness, and leaf chlorophyll. This approach has the potential to map various crop factors simultaneously for use in variable rate application of inputs and as inputs to crop models, allowing them to be extended spatially across a field.

Technical Abstract: Hyperspectral imagery is capable of providing detailed spectral reflectance information of agricultural fields for potential use in site-specific management operations. Analysis of these data are complicated by the large number of spectral bands, the many different components or endmembers (e.g. plant and soil), and the presence of shadows. Unlike simple unmixing approaches which compute the fraction of a fixed number of components, multiple endmember spectral mixture analysis (MESMA) also determines which components are present in each pixel. This study compared whether using different shadow endmembers (EM) in a 4-EM model (sunlit green leaf, sunlit soil, shadowed leaf, shadowed soil) would improve estimates of scene components compared to a 3-EM model (sunlit green leaf, sunlit soil, photometric shade). Results revealed that correlations with percent cover and height were improved when shadow or shade endmembers were included for both models compared to the green leaf fraction alone. The 3-EM model was superior for developing a direct relationship for estimating cover and height but was not able to estimate SPAD or chlorophyll a. The 4-EM model showed the best results for SPAD and chlorophyll a, with r2 values of 0.84 and 0.77, respectively.

Last Modified: 4/20/2014
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