APPLYING LINEAR SPECTRAL UNMIXING TO AIRBORNE HYPERSPECTRAL IMAGERY FOR MAPPING CROP YIELD VARIABILITY

被引：0

作者：

Yang, Chenghai ^{[1
]}

Everitt, James H. ^{[1
]}

Bradford, Joe M. ^{[1
]}

机构：

[1] USDA ARS, Kika Garza Subtrop Agr Res Ctr, Weslaco, TX 78596 USA

来源：

2009 FIRST WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING | 2009年

关键词：

Linear spectral unmixing; hyperspectral imagery; narrow-band NDVI; yield variability; MONITOR DATA; SITE;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This study evaluated linear spectral unmixing techniques for mapping the variation in crop yield. Both unconstrained and constrained linear spectral unmixing models were applied to airborne hyperspectral imagery recorded from a grain sorghum field and a cotton field. A pair of plant and soil spectra derived from each image was used as endmember spectra to generate unconstrained and constrained plant and soil cover fractions. Yield was positively related to plant fractions and negatively related to soil fractions. For comparison, all 5151 possible narrow-band normalized difference vegetation indices (NDVIs) were calculated from the 102-band images and related to yield. Plant fractions provided better correlations with yield than the majority of the NDVIs. These results indicate that plant cover fraction maps derived from hyperspectral imagery can be used as relative yield maps to characterize crop yield variability.

引用

页码：187 / 190

页数：4

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