Turfgrass spectral reflectance: simulating satellite monitoring of spectral signatures of main C3 and C4 species

Turfgrass spectral reflectance: simulating satellite monitoring of spectral signatures of main C3... In recent years, within the European Union several legislative, monitoring and coordinating actions have been undertaken to encourage sustainable use of resources, reduction in the use of chemicals and improvement of the urban environment. In this respect, two concepts that are strictly related to most of the aspects above are: “precision agriculture” and “precision conservation” and more specifically “precision turfgrass management.” Optical sensing has become a crucial part of precision turfgrass management and spectral reflectance in particular has been an active area of research for many years. However, while turfgrass status evaluation by proximity-sensed spectral reflectance appears to be an established and reliable practice, much more could be achieved in terms of monitoring of large turfgrass areas through remote sensing, and in particular through satellite imagery. This paper reports the results of a trial attempting to evaluate the spectral signatures of several turfgrass species and cultivars, for future use in turfgrass satellite monitoring. Our experimental study focused on 20 turfgrass species/varieties including perennial ryegrasses, tall fescues, kentucky bluegrasses, bermudagrass ecotypes, seeded commercial bermudagrasses, vegetatively propagated bermudagrasses, Zoysia japonica and non-japonica zoysiagrasses. Various biological and agronomical parameters were studied and turfgrass spectral reflectance for all entries was gathered. Vegetation indices were calculated by simulating the available wavelengths deriving from World View 2 satellite imagery. Results showed that within the same species selected vegetation indices are often able to discriminate between different varieties that have been established and maintained with identical agronomical practices. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Turfgrass spectral reflectance: simulating satellite monitoring of spectral signatures of main C3 and C4 species

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Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer Science+Business Media New York
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/s11119-014-9376-3
Publisher site
See Article on Publisher Site

Abstract

In recent years, within the European Union several legislative, monitoring and coordinating actions have been undertaken to encourage sustainable use of resources, reduction in the use of chemicals and improvement of the urban environment. In this respect, two concepts that are strictly related to most of the aspects above are: “precision agriculture” and “precision conservation” and more specifically “precision turfgrass management.” Optical sensing has become a crucial part of precision turfgrass management and spectral reflectance in particular has been an active area of research for many years. However, while turfgrass status evaluation by proximity-sensed spectral reflectance appears to be an established and reliable practice, much more could be achieved in terms of monitoring of large turfgrass areas through remote sensing, and in particular through satellite imagery. This paper reports the results of a trial attempting to evaluate the spectral signatures of several turfgrass species and cultivars, for future use in turfgrass satellite monitoring. Our experimental study focused on 20 turfgrass species/varieties including perennial ryegrasses, tall fescues, kentucky bluegrasses, bermudagrass ecotypes, seeded commercial bermudagrasses, vegetatively propagated bermudagrasses, Zoysia japonica and non-japonica zoysiagrasses. Various biological and agronomical parameters were studied and turfgrass spectral reflectance for all entries was gathered. Vegetation indices were calculated by simulating the available wavelengths deriving from World View 2 satellite imagery. Results showed that within the same species selected vegetation indices are often able to discriminate between different varieties that have been established and maintained with identical agronomical practices.

Journal

Precision AgricultureSpringer Journals

Published: Sep 7, 2014

References

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