Access the full text.
Sign up today, get DeepDyve free for 14 days.
(1997)
A true digital imaging system for remote sensing applications
CJ Johannsen, PG Carter, PR Willis, E Owubah, B Erikson, K Ross, N Targulian (1998)
Proceedings of 4th international conference on precision agriculture
(1995)
Digital CCD cameras for airborne remote sensing. In: Mausel PW (ed.) Proceedings of 15th biennial workshop on color photography and videography in resource assessment
D. Escobar, J. Everitt, J. Noriega, I. Cavazos, M. Davis (1998)
A Twelve-Band Airborne Digital Video Imaging System (ADVIS)Remote Sensing of Environment, 66
(1996)
Use of remote sensing imagery for precision farming
P. Pinter, J. Hatfield, J. Schepers, E. Barnes, M. Moran, T. CraigS., Daughtry, D. Upchurch (2003)
Remote Sensing for Crop ManagementPhotogrammetric Engineering and Remote Sensing, 69
G. Senay, A. Ward, J. Lyon, N. Fausey, S. Nokes (1998)
MANIPULATION OF HIGH SPATIAL RESOLUTION AIRCRAFT REMOTE SENSING DATA FOR USE IN SITE-SPECIFIC FARMINGTransactions of the ASABE, 41
J. Everitt, D. Escobar, I. Cavazos, J. Noriega, M. Davis (1995)
A three-camera multispectral digital video imaging systemRemote Sensing of Environment, 54
C Mao, D Kettler (1995)
Proceedings of 15th biennial workshop on color photography and videography in resource assessment
Chenghai Yang, J. Everitt, J. Bradford (2002)
OPTIMUM TIME LAG DETERMINATION FOR YIELD MONITORING WITH REMOTELY SENSED IMAGERYTransactions of the ASABE, 45
(1995)
Digital CCD cameras for airborne remote sensing
Chenghai Yang, G. Anderson (2004)
Airborne Videography to Identify Spatial Plant Growth Variability for Grain SorghumPrecision Agriculture, 1
C. Tucker, B. Holben, J. Elgin, J. McMurtrey (1980)
Relationship of spectral data to grain yield variationPhotogrammetric Engineering and Remote Sensing, 46
P. Thenkabail, A. Ward, J. Lyon (1994)
Landsat-5 Thematic Mapper models of soybean and corn crop characteristicsInternational Journal of Remote Sensing, 15
S. Gopalapillai, L. Tian (1999)
IN-FIELD VARIABILITY DETECTION AND SPATIAL YIELD MODELING FOR CORN USING DIGITAL AERIAL IMAGINGTransactions of the ASABE, 42
C. Wiegand, A. Richardson (1990)
Use of spectral vegetation indices to infer leaf area, evapotranspiration and yield. I. Rationale.Agronomy Journal, 82
(1997)
A true digital imaging system for remote sensing applications. In: Everitt JH, (ed.) Proceedings of 16th biennial workshop on color photography and videography in resource assessment
Chenghai Yang, J. Everitt (2002)
Relationships Between Yield Monitor Data and Airborne Multidate Multispectral Digital Imagery for Grain SorghumPrecision Agriculture, 3
J. Shanahan, J. Schepers, D. Francis, G. Varvel, W. Wilhelm, J. Tringe, M. Schlemmer, D. Major (2001)
Use of Remote-Sensing Imagery to Estimate Corn Grain YieldAgronomy Journal, 93
E. Barnes, M. Baker (2000)
Multispectral data for mapping soil texture: possibilities and limitations.Applied Engineering in Agriculture, 16
Chenghai Yang, J. Everitt, J. Bradford, D. Escobar (2000)
Mapping grain sorghum growth and yield variations using airborne multispectral digital imagery.Transactions of the ASABE, 43
Timely and accurate information on crop conditions obtained during the growing season is of vital importance for crop management. High spatial resolution satellite imagery has the potential for mapping crop growth variability and identifying problem areas within fields. The objectives of this study were to use QuickBird satellite imagery for mapping plant growth and yield patterns within grain sorghum fields as compared with airborne multispectral image data. A QuickBird 2.8-m four-band image covering a cropping area in south Texas, USA was acquired in the 2003 growing season. Airborne three-band imagery with submeter resolution was also collected from two grain sorghum fields within the satellite scene. Yield monitor data collected from the two fields were resampled to match the resolutions of the airborne imagery and the satellite imagery. The airborne imagery was related to yield at original submeter, 2.8 and 8.4 m resolutions and the QuickBird imagery was related to yield at 2.8 and 8.4 m resolutions. The extracted QuickBird images for the two fields were then classified into multiple zones using unsupervised classification and mean yields among the zones were compared. Results showed that grain yield was significantly related to both types of image data and that the QuickBird imagery had similar correlations with grain yield as compared with the airborne imagery at the 2.8 and 8.4 m resolutions. Moreover, the unsupervised classification maps effectively differentiated grain production levels among the zones. These results indicate that high spatial resolution satellite imagery can be a useful data source for determining plant growth and yield patterns for within-field crop management.
Precision Agriculture – Springer Journals
Published: Dec 24, 2005
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.