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

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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.

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Abbreviations

PA:

Precision agriculture

PC:

Precision conservation

GPS:

Global positioning systems

GIS:

Geographic information systems

PTM:

Precision turfgrass management

LAI:

Leaf area index

NDVI:

Normalized difference vegetation index

NIR:

Near infrared

NPCI:

Normalized pigment chlorophyll index

SIPI:

Structure intensive pigment index

PRI:

Photochemical reflectance index

GNDVI:

Green normalized difference vegetation index

MCARI:

Modified chlorophyll absorption in reflectance index

YI:

Yellowness index

SR:

Simple ratio

WI:

Water index

RVI:

Ratio vegetation index

WV2:

WorldView-2

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Conflict of interest

The authors declare no conflict of interest.

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Authors and Affiliations

  1. Department of Agriculture, Food and Environment (DAFE), University of Pisa, Via del Borghetto n. 80, 56124, Pisa, Italy

    Lisa Caturegli, Lara Foschi, Lorenzo Guglielminetti & Marco Volterrani

  2. Turf Europe R&D, Via Malasoma n. 24, 56121, Pisa, Italy

    Filippo Lulli

  3. Sant’Anna School of Advanced Studies, Piazza Martiri della Libertà n. 33, 56127, Pisa, Italy

    Enrico Bonari

Authors
  1. Lisa Caturegli
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  2. Filippo Lulli
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  3. Lara Foschi
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  4. Lorenzo Guglielminetti
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  5. Enrico Bonari
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  6. Marco Volterrani
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Correspondence to Lisa Caturegli.

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Caturegli, L., Lulli, F., Foschi, L. et al. Turfgrass spectral reflectance: simulating satellite monitoring of spectral signatures of main C3 and C4 species. Precision Agric 16, 297–310 (2015). https://doi.org/10.1007/s11119-014-9376-3

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