HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (23) Citing Articles via Google Scholar Google Scholar Articles by Zarco-Tejada, P. J. Articles by Whiting, M. L. Search for Related Content PubMed Articles by Zarco-Tejada, P. J. Articles by Whiting, M. L. Agricola Articles by Zarco-Tejada, P. J. Articles by Whiting, M. L. Related Collections Remote Sensing Site-Specific Analysis

Temporal and Spatial Relationships between Within-Field Yield Variability in Cotton and High-Spatial Hyperspectral Remote Sensing Imagery

Cent. for Spatial Technol. and Remote Sensing (CSTARS), Dep. of Land, Air, and Water Resour. (LAWR), One Shields Ave., The Barn, Univ. of California, Davis, CA 95616-8671, USA

View larger version (111K): [in a new window]   Fig. 1. Ikonos image obtained on 7 July 2001 showing the cotton field used for this study in upper center of the frame. The coordinate grid is Universal Transverse Mercator, Zone 11.

View larger version (132K): [in a new window]   Fig. 2. Yield image for the cotton field, collected by the AGRIplan AG700 yield monitor, at 4.5-m spatial resolution.

View larger version (66K): [in a new window]   Fig. 3. Reflectance spectral measurements obtained from the airborne image showing two areas of low and high growth. This black and white reproduction is of a AVNIR image color composite of band centers at 454.92, 455.00, and 804.12 nm.

View larger version (44K): [in a new window]   Fig. 4. Time series of (a and b) hyperspectral reflectance and (c and d) chlorophyll-related indices (TCARI), leaf area index related indices (RDVI and NDVI), and OSAVI calculated for areas of (a and c) low and (b and d) high growth development for the entire growing season.

View larger version (35K): [in a new window]   Fig. 5. Correlation coefficients (r) obtained between spatial yield data and hyperspectral indices as function of time. The best correlation coefficient for any index is labeled MAX compared with indices (a) NDVI, RDVI, and MTVI1; (b) MCARI, TCARI, and OSAVI; (c) red edge parameters p, o, and Rs; and (d) water indices PWI, mNDWI, mSRWI.

View larger version (21K): [in a new window]   Fig. 6. Kappa coefficient () and overall accuracy calculated between segmented yield image data and airborne imagery from spectral indices using an unsupervised K-means clustering method to produce low-, medium-, and high-yield classes.

View larger version (139K): [in a new window]   Fig. 7. Unsupervised K means clustering method for low (red)-, medium (green)-, and high (blue)-yield classes calculated from the hyperspectral airborne image that obtained the highest correlation with yield (5 July 2001). It shows better performance for OSAVI than NDVI index, with poor segmentation performance when 60 absolute reflectance bands are used. Airborne image pixels shown are only those where yield data were collected with the yield monitor.

View larger version (104K): [in a new window]   Fig. 8. Time series of segmented imagery using the OSAVI index for low (red)-, medium (green)-, and high (blue)-yield through K-means method, enabling the visual comparison with classes calculated from the spatial yield data. Airborne image pixels shown are only those where yield data were collected with the yield monitor.