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 Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Kruse, J. K. Articles by Chaplin, M. H. Search for Related Content PubMed Articles by Kruse, J. K. Articles by Chaplin, M. H. Agricola Articles by Kruse, J. K. Articles by Chaplin, M. H. Related Collections Turfgrass Management Nitrogen Remote Sensing Site-Specific Analysis

Remote Sensing of Nitrogen Stress in Creeping Bentgrass

^a Univ. of Wisconsin Extension, Winnebago County, 625 East County Rd. Y, Suite 600, Oshkosh, WI 54901
^b Dep. of Horticulture, Iowa State Univ., 106 Horticulture Hall, Ames, IA 50011

View larger version (13K): [in a new window]   Fig. 1. Graphical illustration of the predicted residual sum of squares (PRESS) vs. the number of factors used in the partial least-squares regression model.

View larger version (15K): [in a new window]   Fig. 2. Mean spectral reflectance for each of the three N treatments on 22 July 2002 in Gilbert, IA, on creeping bentgrass (Agrostis stolonifera L.). Similar trends were observed for all sampling dates in 2002 and 2003.

View larger version (20K): [in a new window]   Fig. 3. Predicted versus actual tissue N concentration for all data collected in 2002 and 2003 from creeping bentgrass (Agrostis stolonifera L.) obtained using partial least-squares regression to relate spectral reflectance data in the visible/near-infrared wavelength range to the reference tissue N concentration values. The graphed line represents a 1:1 relationship. SEP, standard error of prediction.