Shadow fraction in spectral mixture analysis of a cotton canopy

Glenn J. Fitzgerald; Paul J. Pinter Jr.; Douglas J. Hunsaker; Thomas R. Clarke

doi:10.1117/12.555769

9 November 2004 Shadow fraction in spectral mixture analysis of a cotton canopy

Glenn J. Fitzgerald, Paul J. Pinter Jr., Douglas J. Hunsaker, Thomas R. Clarke

Proceedings Volume 5544, Remote Sensing and Modeling of Ecosystems for Sustainability; (2004) https://doi.org/10.1117/12.555769
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

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.

Citation Download Citation

Glenn J. Fitzgerald, Paul J. Pinter Jr., Douglas J. Hunsaker, and Thomas R. Clarke "Shadow fraction in spectral mixture analysis of a cotton canopy", Proc. SPIE 5544, Remote Sensing and Modeling of Ecosystems for Sustainability, (9 November 2004); https://doi.org/10.1117/12.555769

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available