Access the full text.
Sign up today, get DeepDyve free for 14 days.
C Buschmann, A Nagel (1993)
In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetationInternational Journal of Remote Sensing, 14
AA Gitelson (2005)
10.1029/2005GL022688Geophysical Research Letters, 32
JL Hatfield (2008)
10.2134/agronj2006.0370cAgronomy Journal, 100
AM Blackmer (1998)
10.1071/A97073Australian Journal of Agricultural Research, 49
J Zhang, AM Blackmer, PM Kyveryga, MJ Glady, TM Blackmer (2010)
Temporal patterns in symptoms of nitrogen deficiency as revealed by remote sensing of corn canopyPedosphere, 20
PM Kyveryga, H Tao, TF Morris, TM Blackmer (2010)
Identification of nitrogen management categories by corn stalk nitrate sampling guided by aerial imageryAgronomy Journal, 102
JL Morgan, SE Gergel, NC Coops (2010)
Aerial photography: A rapidly evolving tool for ecological managementBioscience, 60
GD Binford, AM Blackmer, BG Meese (1992)
Optimal concentrations of nitrate in cornstalks at maturityAgronomy Journal, 84
TM Blackmer, JS Schepers, GE Varvel, GE Meyer (1996)
Analysis of aerial photography for nitrogen stress within corn fieldsAgronomy Journal, 88
SM Brouder, DB Mengel, BS Hofmann (2000)
Diagnostic efficiency of the black layer stalk nitrate and grain nitrogen tests for cornAgronomy Journal, 92
JL Hatfield, AA Gitelson, JS Schepers, CL Walthall (2008)
Application of spectral remote sensing for agronomic decisionsAgronomy Journal, 100
J Zhang (2010)
10.1016/S1002-0160(09)60278-2Pedosphere, 20
WW Wilhelm, GE Varvel, JS Schepers (2005)
Corn stalk nitrate concentration profileAgronomy Journal, 97
C Buschmann (1993)
10.1080/01431169308904370International Journal of Remote Sensing, 14
J Zhang (2008)
10.2134/agronj2006.0154Agronomy Journal, 100
SM Brouder (2000)
10.2134/agronj2000.9261236xAgronomy Journal, 92
WW Wilhelm (2005)
10.2134/agronj2005.0085Agronomy Journal, 97
AA Gitelson, A Vina, DC Rundquist, V Ciganda, TJ Arkebauer (2005)
Remote estimation of canopy chlorophyll content in cropsGeophysical Research Letters, 32
KS Balkcom (2003)
10.2134/jeq2003.1015Journal of Environmental Quality, 32
KS Balkcom, AM Blackmer, DJ Hansen, TF Morris, AP Mallarino (2003)
Testing soils and cornstalks to evaluate nitrogen management on the watershed scaleJournal of Environmental Quality, 32
TM Blackmer (1996)
10.2134/agronj1996.00021962008800050008xAgronomy Journal, 88
J Pan, D Li, J Li (2010)
A network-based radiometric equalization approach for digital aerial orthoimagesGeoscience and Remote Sensing Letters, 7
JL Morgan (2010)
10.1525/bio.2010.60.1.9Bioscience, 60
AM Blackmer, SE White (1998)
Using precision farming technologies to improve management of soil and fertilizer nitrogenAustralian Journal of Agricultural Research, 49
GD Binford (1992)
10.2134/agronj1992.00021962008400050022xAgronomy Journal, 84
J Zhang, AM Blackmer, JW Ellsworth, PM Kyveryga, TM Blackmer (2008)
Luxury production of leaf chlorophyll and mid-season recovery from nitrogen deficiencies in cornAgronomy Journal, 100
SM Samborski, N Tremblay, E Fallon (2009)
Strategies to make use of plant sensors-based diagnostic information for nitrogen recommendationsAgronomy Journal, 98
(2005)
Release 9.2
PM Kyveryga (2010)
10.2134/agronj2009.0401Agronomy Journal, 102
J Pan (2010)
10.1109/LGRS.2009.2037442Geoscience and Remote Sensing Letters, 7
Using uncalibrated digital aerial imagery (DAI) for diagnosing in-season nitrogen (N) status of corn (Zea mays L.) is challenging because of the dynamic nature of corn growth and the difficulty of obtaining timely imagery. Late-season DAI is more accurate for identifying areas deficient in N than early-season imagery. Even so, the quantitative use of the imagery across many fields is still limited because DAI is often not radiometrically calibrated. This study tested whether spectral characteristics of corn canopy derived from normalized uncalibrated late-season DAI could predict final corn N status. Color and near-infrared (NIR) imagery was collected in late August or early September across Iowa from 683 corn fields in 2006, 824 in 2007, and 828 fields in 2007. Four sampling areas (one within a target-deficient area) were selected within each field for conducting the end-of-season corn stalk nitrate test (CSNT). Each image was enhanced to increase the dynamic range within each field and to normalize reflectance values across all fields within a year. The reflectance values of individual bands and three vegetation indices were used to predict corn N status expressed as Deficient and Sufficient (a combination of marginal, optimal, and excessive CSNT categories) using a binary logistic regression (BLR). The green reflectance had the highest prediction rate, which was 70, 64, and 60% in 2006, 2007, and 2008, respectively. The results suggest that the normalized (enhanced) late-season uncalibrated DAI can be used to predict final corn N status in large-scale on-farm evaluation studies.
Precision Agriculture – Springer Journals
Published: Jun 19, 2011
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.