Monitoring turfgrass species and cultivars by spectral reflectance

European Journal of Horticultural Science

Volumn 79, Issue 3, 2014, Pages 97-107

  Caturegli, L ^a   Lulli, F ^b   Foschi, L ^a   Guglielminetti, L ^a   Bonari, E ^c   Volterrani, M ^a  

^a UNIVERSITY OF PISA   (Italy)

^b Turf Europe R and D   (Italy)

^c SCUOLA SUPERIORE SANT ANNA   (Italy)

Author keywords

Cynodon; Festuca; Lolium; Poa; Spectroradiometry; Vegetation indices; Zoysia

Indexed keywords

EID: 84901801639     PISSN: 16114434     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (39)

1. AGATI, G., L. FOSCHI, N. GROSSI, L. GUGLIELMINETTI, Z.G. CEROVIC and M. VOLTERRANI 2013: Fluorescence-basedversus reflectance proximal sensing of nitrogen contentin Paspalum vaginatum and Zoysia matrella turfgrasses. Europ. J. Agr. 45, 39-51.
2. BAUSCH, W.C., A.D. HALVORSON and J. CIPRA 2008: Quickbirdsatellite and ground-based multispectral data correlationswith agronomic parameters of irrigated maizegrown in small plots. Biosys. Engin. 101, 306-315.
3. BELL, G.E., B.M. HOWELL, G.V. JOHNSON, W.R. RAUN, J.B. SOLIE and M.L. STONE 2004: Optical sensing of turfgrasschlorophyll content and tissue nitrogen. HortSci. 39,1130-1132.
4. BELL, G.E. and X. XIONG 2008: The history, role, and potentialof optical sensing for practical turf management. In PESSARAKI, E. (ed.): Handbook of turfgrass managementand physiology. Boca Raton, FL: CRC Press, pp.641-660.
5. BREMER, D.J., H. LEE, K. SU and S.J. KEELEY 2011: Relationshipsbetween normalized difference vegetation indexand visual quality in cool-season turfgrass: II. Factorsaffecting ndvi and its component reflectances. Crop Sci.51, 2219-2227.
6. CARROW, R.N., J.M. KRUM, I. FLITCROFT and V. CLINE 2010:Precision turfgrass management: challenges and fieldapplications for mapping turfgrass soil and stress. PrecisionAgric. 11, 115-134.
7. CHO, M.A. and A.K. SKIDMORE 2006: A new technique forextracting the red edge position from hyperspectraldata: The linear extrapolation method. Remote Sens. ofEnviron. 101, 181-193.
8. COSTAT 2008: Manual version 6.400, Copyright© 1998-2008 CoHort Software
9. CROCE, P., A. DE LUCA, M. MOCIONI, M. VOLTERRANI and J.B. BEARD 2004: Adaptability of warmseason turfgrassspecies and cultivars in a Mediterranean climate. ActaHort. 661, 365-368.
10. DARVISHSEFAT, A.A., M. ABBASI and M.E. SCHAEPMAN 2011:Evaluation of spectral reflectance of seven Iranian ricevarieties canopies. J. Agric. Sci. and Techn. 13, 1091-1104.
11. DAUGHTRY, C.S.T., C.L. WALTHALL, M.S. KIM and E. BROWNDE COLSTOUN 2000: Estimating corn leaf chlorophyllconcentration from leaf and canopy reflectance. RemoteSens. Environ. 74, 229-239.
12. DETTMAN-KRUSE J.K., N.E. CHRISTIANS and M.H CHAPLIN 2008: Predicting soil water content through remotesensing of vegetative characteristics in a turfgrasssystem. Crop Sci. 48, 763-770.
13. FINKE, P.A. 1992: Integration of remote sensing data in thesimulation of spatially variable yield of potatoes. SoilTechn. 5, 257-270.
14. GATES, D.M., H.J. KEEGAN, J.C. SCHLETER and V.R. WEIDNER1965: Spectral properties of plants. Appl. Optics. 4, 11-20.
15. GITELSON, A.A. and M.N. MERZLYAK 1996: Signature analysisof leaf reflectance spectra: algorithm developmentfor remote sensing of chlorophyll. J. Plant Phys. 148,494-500.
16. GROSSI, N., M. VOLTERRANI, S. MAGNI and S. MIELE 2004:Tall fescue turf quality and soccer playing characteristicsas affected by mowing height. Acta Hort. 661, 319-322.
17. HABOUDANE, D., J.R. MILLER, N. TREMBLAY, P.J. ZARCO-TEJADAand L. DEXTRAZE 2002: Integration of hyperspectral vegetationindices for prediction of crop chlorophyll contentfor application to precision agriculture. Remote Sens.Environ. 81, 416-426.
18. INOUE, Y., J. PEÑUELAS, A. MIYATA and M. MANO 2008:Normalized difference spectral indices for estimatingphotosynthetic effieciency and capacity at a conopyscale derived from hyperspectral and CO2 fluxmeasurement in rice. Remote Sens. Environ. 122,156-172.
19. JIANG, Y. and R.N. CARROW 2007: Broadband spectralreflectance models of turfgrass species and cultivars todrought stress. Crop Sci. 47, 1611-1618.
20. LEE, H. 2008: Estimation of visual quality and canopy characteristicsof turfgrass using spectral reflectance anddigital imagery. Kansas State University, Manhattan,Kansas, USA
21. LI, Y., T.H. DEMETRIADES-SHAH, E.T. KANEMASU, J.K. SHULTISand M.B. KIRKHAM 1993: Use of second derivatives ofcanopy reflectance for monitoring prairie vegetationover different soil backgrounds. Remote Sens. Environ.44, 81-87.
22. LULLI, F., M. VOLTERRANI, N. GROSSI, R. ARMENI, S. STEFANINIand L. GUGLIELMINETTI 2012: Physiological and morphologicalfactors influencing wear resistance and recoveryin C3 and C4 turfgrass species. Functional Plant Biol. 39,214-221.
23. KRUM, J.H., R.N. CARROW and K. KARNOK 2010: Spatialmapping of complex turfgrass sites: Site-specificmanagement units and protocols. Crop Sci. 50, 301-315.
24. MACOLINO, S., M. SERENA, B. LEINAUER and U. ZILIOTTO 2010:Preliminary finding on the correlation between watersolublecarbohydrate content in stolons and first yeargreen-up of seeded bermudagrass cultivars. HortTechn.20, 758-763.
25. MEER, F.V.D. and S.M. DE JONG 2006: Imaging spectrometryfor agriculture applications. In: CLEVERS, J.G.P.W. andR. JONGSCHAAP (eds.): Imaging spectrometry: Basic principaland prospective application. Springer, Dordrecht,Netherlands, pp. 157-197.
26. MUNDEN, R., P.J. CURRAN and J.A. CATT 1994: The relationshipbetween red edge and chlorophyll concentration inthe broadbalk winter wheat experiment at Rothamsted.Int. J. Remote Sens. 15, 705-709.
27. PATTON, A.J. and Z.J. REICHER 2007: Zoysiagrass speciesand genotypes differ in their winter injury and freezetolerance. Crop Sci. 47, 1619-1627
28. PEÑUELAS, J., I. FILELLA and F. BARRET 1995: Semi-empiricalindices to assess carotenoids/chlorophyll a ratio fromleaf spectral reflectance. Photosynthetic 31, 221-230.
29. PEÑUELAS, J., I. FILLELLA, L. SERRANO and R. SAVÉ 1996: Cellwall elasticity and water index (R970 nm/R900 nm) inwheat under different nitrogen availabilities. Int. J. RemoteSens. 17, 373-382.
30. POMPEIANO, A., N. GROSSI and M. VOLTERRANI 2012: Vegetativeestablishment rate and stolon growth characteristicsof 10 zoysiagrasses in Southern Europe. HortTechn.22, 114-120.
31. QI, J., A. CHEHBOUNI, A.R. HUETE and Y.H. KERR 1994:Modified Soil Adjusted Vegetation Index (MSAVI). RemoteSens Environ 48, 119-126.
32. RESOP, J.P., J.S. CUNDIFF and C.D. HEATWOLE 2011: Spatialanalysis to site satellite storage locations for herbaceousbiomass in the piedmont of the southeast. Appl. Engin.Agric. 27, 25-32.
33. RONDEAUX, G., M. STEVEN and F. BARRET 1996: Optimizationof soil-adjusted vegetation indices. Remote Sens. Environ.55, 95-107.
34. ROUSE, J.W., R.H. HAAS, J.A. SCHELL, D.W. DEERING and J.C. HARLAN 1974: Monitoring the vernal advancements andretrogradation of natural vegetation. NASA/GSFC FinalReport MD, USA Greenbelt, 371 pp
35. STOWELL, L. and W. GELERNTER 2006: Sensing the future.Golf Course Manage, 74(3), 107-110.
36. THENKABAIL, P.S., E.A. ENCLONA, M.S. ASHTON and B.V.D. MEER 2004: Accuracy assessments of hyperspectralwaveband performance for vegetation analysis applications. Remote Sens. Environ. 91, 354-376.
37. TRENHOLM, L.E., G. SCLOSSBERG, G. LEE and W. PARKS 2000:An evaluation of multi-spectral responses on selectedturfgrass species. Int. J. Remote Sens. 21, 709-721.
38. Zarco-Tejada, P.J, A. BERJÓN and J.R. MILLER 2001: Stressdetection in crops with hyperspectral remote sensingand physical simulation models. http://hdl.handle.net/10261/10582.
39. ZHANG, J.E. and M.B. KIRKHAM 1996: Antioxidant responsesto drought in sunflower and sorghum seedlings. NewPhytol. 132, 361-373.