Application of day and night digital photographs for estimating maize biophysical characteristics

Application of day and night digital photographs for estimating maize biophysical characteristics In this study, an inexpensive camera-observation system called the Crop Phenology Recording System (CPRS), which consists of a standard digital color camera (RGB cam) and a modified near-infrared (NIR) digital camera (NIR cam), was applied to estimate green leaf area index (LAI), total LAI, green leaf biomass and total dry biomass of stalks and leaves of maize. The CPRS was installed for the 2009 growing season over a rainfed maize field at the University of Nebraska-Lincoln Agricultural Research and Development Center near Mead, NE, USA. The vegetation indices called Visible Atmospherically Resistant Index (VARI) and two green–red–blue (2g–r–b) were calculated from day-time RGB images taken by the standard commercially-available camera. The other vegetation index called Night-time Relative Brightness Index in NIR (NRBINIR) was calculated from night-time flash NIR images taken by the modified digital camera on which a NIR band-pass filter was attached. Sampling inspections were conducted to measure bio-physical parameters of maize in the same experimental field. The vegetation indices were compared with the biophysical parameters for a whole growing season. The VARI was found to accurately estimate green LAI (R2 = 0.99) and green leaf biomass (R2 = 0.98), as well as track seasonal changes in maize green vegetation fraction. The 2g–r–b was able to accurately estimate total LAI (R2 = 0.97). The NRBINIR showed the highest accuracy in estimation of the total dry biomass weight of the stalks and leaves (R2 = 0.99). The results show that the camera-observation system has potential for the remote assessment of maize biophysical parameters at low cost. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Application of day and night digital photographs for estimating maize biophysical characteristics

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Publisher
Springer Journals
Copyright
Copyright © 2011 by Springer Science+Business Media, LLC
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-011-9246-1
Publisher site
See Article on Publisher Site

Abstract

In this study, an inexpensive camera-observation system called the Crop Phenology Recording System (CPRS), which consists of a standard digital color camera (RGB cam) and a modified near-infrared (NIR) digital camera (NIR cam), was applied to estimate green leaf area index (LAI), total LAI, green leaf biomass and total dry biomass of stalks and leaves of maize. The CPRS was installed for the 2009 growing season over a rainfed maize field at the University of Nebraska-Lincoln Agricultural Research and Development Center near Mead, NE, USA. The vegetation indices called Visible Atmospherically Resistant Index (VARI) and two green–red–blue (2g–r–b) were calculated from day-time RGB images taken by the standard commercially-available camera. The other vegetation index called Night-time Relative Brightness Index in NIR (NRBINIR) was calculated from night-time flash NIR images taken by the modified digital camera on which a NIR band-pass filter was attached. Sampling inspections were conducted to measure bio-physical parameters of maize in the same experimental field. The vegetation indices were compared with the biophysical parameters for a whole growing season. The VARI was found to accurately estimate green LAI (R2 = 0.99) and green leaf biomass (R2 = 0.98), as well as track seasonal changes in maize green vegetation fraction. The 2g–r–b was able to accurately estimate total LAI (R2 = 0.97). The NRBINIR showed the highest accuracy in estimation of the total dry biomass weight of the stalks and leaves (R2 = 0.99). The results show that the camera-observation system has potential for the remote assessment of maize biophysical parameters at low cost.

Journal

Precision AgricultureSpringer Journals

Published: Sep 15, 2011

References

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