doi:10.48130/GR-2023-0022

[1]

Harlan JR, de Wet JMJ. 1969. Sources of variation in Cynodon dactylon (L). Pers. Crop Science 9:774−78

doi: 10.2135/cropsci1969.0011183x000900060031x

[2]

Cui F, Taier G, Li M, Dai X, Hang N, et al. 2021. The genome of the warm-season turfgrass African bermudagrass (Cynodon transvaalensis). Horticulture Research 8:93

doi: 10.1038/s41438-021-00519-w

[3]

Wu YQ, Taliaferro CM, Bai GH, Martin DL, Anderson JA, et al. 2006. Genetic analyses of Chinese Cynodon accessions by flow cytometry and AFLP markers. Crop Science 46:917−26

doi: 10.2135/cropsci2005.08.0256

[4]

Beard JB. 2002. Turf management for golf courses. 2nd edition. Chelsea, MI: Wiley.

[5]

Beard JB. 2005. Beard's Turfgrass Encyclopedia for Golf Courses, Grounds, Lawns, Sports Fields. Illustrated edition. MI: Michigan State University Press. 513pp.

[6]

Kowalewski AR, Schwartz BM, Grimshaw AL, Sullivan DG, Peake JB. 2015. Correlations between hybrid bermudagrass morphology and wear tolerance. HortTechnology 25:725−30

doi: 10.21273/horttech.25.6.725

[7]

Araus JL, Cairns JE. 2014. Field high-throughput phenotyping: the new crop breeding frontier. Trends in Plant Science 19:52−61

doi: 10.1016/j.tplants.2013.09.008

[8]

Taliaferro CM, Martin DL, Anderson JA, Anderson MP, Bell GE, et al. 2003. Registration of 'yukon' bermudagrass. Crop Science 43:1131−32

doi: 10.2135/cropsci2003.1131

[9]

Wanga MA, Shimelis H, Mashilo J, Laing MD. 2021. Opportunities and challenges of speed breeding: a review. Plant Breeding 140:185−94

doi: 10.1111/pbr.12909

[10]

Kumar L, Schmidt K, Dury S, Skidmore A. 2002. Imaging spectrometry and vegetation science. In Imaging Spectrometry, eds. van der Meer FD, De Jong SM. Dordrecht: Springer Netherlands. RDIP,volume 4, pp 111−55 https://doi.org/10.1007/978-0-306-47578-8_5

[11]

Ogburn RM, Edwards EJ. 2010. The ecological water-use strategies of succulent plants. Advances in Botanical Research 55:179−225

doi: 10.1016/b978-0-12-380868-4.00004-1

[12]

van Bezouw RFHM, Keurentjes JJB, Harbinson J, Aarts MGM. 2019. Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency. The Plant Journal 97:112−33

doi: 10.1111/tpj.14190

[13]

Grzybowski M, Wijewardane NK, Atefi A, Ge Y, Schnable JC. 2021. Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: progress and challenges. Plant Communications 2:100209

doi: 10.1016/j.xplc.2021.100209

[14]

Ustin SL, Jacquemoud S. 2020. How the optical properties of leaves modify the absorption and scattering of energy and enhance leaf functionality. In Remote Sensing of Plant Biodiversity, eds. Cavender-Bares J, Gamon JA, Townsend PA. Cham: Springer International Publishing. pp 349−84 https://doi.org/10.1007/978-3-030-33157-3_14

[15]

Curran PJ. 1989. Remote sensing of foliar chemistry. Remote Sensing of Environment 30:271−78

doi: 10.1016/0034-4257(89)90069-2

[16]

Martin ME, Aber JD. 1997. High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes. Ecological Applications 7:431−43

doi: 10.1890/1051-0761(1997)007[0431:hsrrso]2.0.co;2

[17]

Peñuelas J, Filella I. 1998. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science 3:151−56

doi: 10.1016/S1360-1385(98)01213-8

[18]

Knipling EB. 1970. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment 1:155−59

doi: 10.1016/S0034-4257(70)80021-9

[19]

Lobos GA, Hancock JF. 2015. Breeding blueberries for a changing global environment: a review. Frontiers in Plant Science 6:782

doi: 10.3389/fpls.2015.00782

[20]

Carter GA. 1994. Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing 15:697−703

doi: 10.1080/01431169408954109

[21]

Peñuelas J, Gamon JA, Fredeen AL, Merino J, Field CB. 1994. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sensing of Environment 48:135−46

doi: 10.1016/0034-4257(94)90136-8

[22]

Yendrek CR, Tomaz T, Montes CM, Cao Y, Morse AM, et al. 2017. High-throughput phenotyping of maize leaf physiological and biochemical traits using hyperspectral reflectance. Plant Physiology 173:614−26

doi: 10.1104/pp.16.01447

[23]

Eshkabilov S, Lee A, Sun X, Lee CW, Simsek H. 2021. Hyperspectral imaging techniques for rapid detection of nutrient content of hydroponically grown lettuce cultivars. Computers and Electronics in Agriculture 181:105968

doi: 10.1016/j.compag.2020.105968

[24]

Adams ML, Norvell WA, Philpot WD, Peverly JH. 2000. Spectral detection of micronutrient deficiency in 'Bragg' soybean. Agronomy Journal 92:261−68

doi: 10.2134/agronj2000.922261x

[25]

Shu M, Zhou L, Chen H, Wang X, Meng L, et al. 2022. Estimation of amino acid contents in maize leaves based on hyperspectral imaging. Frontiers in Plant Science 13:885794

doi: 10.3389/fpls.2022.885794

[26]

Bell GE, Martin DL, Koh K, Han HR. 2009. Comparison of turfgrass visual quality ratings with ratings determined using a handheld optical sensor. HortTechnology 19:309−16

doi: 10.21273/horttech.19.2.309

[27]

Carrow RN, Krum JM, Flitcroft I, Cline V. 2010. Precision turfgrass management: challenges and field applications for mapping turfgrass soil and stress. Precision Agriculture 11:115−34

doi: 10.1007/s11119-009-9136-y

[28]

Leinauer B, VanLeeuwen DM, Serena M, Schiavon M, Sevostianova E. 2014. Digital image analysis and spectral reflectance to determine turfgrass quality. Agronomy Journal 106:1787−94

doi: 10.2134/agronj14.0088

[29]

Fitz-Rodríguez E, Choi CY. 2002. Monitoring turfgrass quality using multispectral radiometry. Transactions of the ASABE 45:865−71

doi: 10.13031/2013.8839

[30]

Bremer DJ, Lee H, Su K, Keeley SJ. 2011. Relationships between normalized difference vegetation index and visual quality in cool-season turfgrass: I. variation among species and cultivars. Crop Science 51:2212−18

doi: 10.2135/cropsci2010.12.0728

[31]

Haghverdi A, Reiter M, Sapkota A, Singh A. 2021. Hybrid bermudagrass and tall fescue turfgrass irrigation in central California: I. assessment of visual quality, soil moisture and performance of an ET-based smart controller. Agronomy 11:1666

doi: 10.3390/agronomy11081666

[32]

Wold S, Sjöström M, Eriksson L. 2001. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58:109−30

doi: 10.1016/s0169-7439(01)00155-1

[33]

Burnett AC, Anderson J, Davidson KJ, Ely KS, Lamour J, et al. 2021. A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression. Journal of Experimental Botany 72:6175−89

doi: 10.1093/jxb/erab295

[34]

Tibshirani R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58:267−88

doi: 10.1111/j.2517-6161.1996.tb02080.x

[35]

Furbank RT, Silva-Perez V, Evans JR, Condon AG, Estavillo GM, et al. 2021. Wheat physiology predictor: predicting physiological traits in wheat from hyperspectral reflectance measurements using deep learning. Plant Methods 17:108

doi: 10.1186/s13007-021-00806-6

[36]

Mohd Asaari MS, Mertens S, Verbraeken L, Dhondt S, Inzé D, et al. 2022. Non-destructive analysis of plant physiological traits using hyperspectral imaging: a case study on drought stress. Computers and Electronics in Agriculture 195:106806

doi: 10.1016/j.compag.2022.106806

[37]

Ge Y, Atefi A, Zhang H, Miao C, Ramamurthy RK, et al. 2019. High-throughput analysis of leaf physiological and chemical traits with VIS-NIR-SWIR spectroscopy: a case study with a maize diversity panel. Plant Methods 15:66

doi: 10.1186/s13007-019-0450-8

[38]

Fan J, Zhang W, Amombo E, Hu L, Kjorven JO, et al. 2020. Mechanisms of environmental stress tolerance in turfgrass. Agronomy 10:522

doi: 10.3390/agronomy10040522

[39]

He Y, Guo X, Wilmshurst J. 2006. Studying mixed grassland ecosystems I: suitable hyperspectral vegetation indices. Canadian Journal of Remote Sensing 32:98−107

doi: 10.5589/m06-009

[40]

Kokhan S, Vostokov A. 2020. Using vegetative indices to quantify agricultural crop characteristics. Journal of Ecological Engineering 21:120−27

doi: 10.12911/22998993/119808

[41]

Basso B, Cammarano D, De Vita P. 2004. Remotely sensed vegetation indices: theory and application for crop management. Journal of Agrometeorology 1:36−53

[42]

Gamon JA, Serrano L, Surfus JS. 1997. The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 112:492−501

doi: 10.1007/s004420050337

[43]

Herrmann I, Karnieli A, Bonfil DJ, Cohen Y, Alchanatis V. 2010. SWIR-based spectral indices for assessing nitrogen content in potato fields. International Journal of Remote Sensing 31:5127−43

doi: 10.1080/01431160903283892

[44]

Jiang J, Chen Y, Huang W. 2010. Using hyperspectral remote sensing to estimate canopy chlorophyll density of wheat under yellow rust stress. Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu 30:2243−47

doi: 10.3964/j.issn.1000-0593(2010)08-2243-05

[45]

Cai Y, Miao Y, Wu H, Wang D. 2021. Hyperspectral estimation models of winter wheat chlorophyll content under elevated CO₂. Frontiers in Plant Science 12:642917

doi: 10.3389/fpls.2021.642917

[46]

Raun WR, Solie JB, Johnson GV, Stone ML, Lukina EV, et al. 2001. In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal 93:131−38

doi: 10.2134/agronj2001.931131x

[47]

Gitelson A, Merzlyak MN. 1994. Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology B: Biology 22:247−52

doi: 10.1016/1011-1344(93)06963-4

[48]

Gitelson AA, Viña A, Ciganda V, Rundquist DC, Arkebauer TJ. 2005. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters 32:L08403

doi: 10.1029/2005gl022688

[49]

Gitelson AA, Kaufman YJ, Stark R, Rundquist D. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment 80:76−87

doi: 10.1016/S0034-4257(01)00289-9

[50]

Dash J, Curran PJ. 2004. The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing 25:5403−13

doi: 10.1080/0143116042000274015

[51]

Long DS, Eitel JUH, Huggins DR. 2009. Assessing nitrogen status of dryland wheat using the canopy chlorophyll content index. Crop Management 8:1−8

doi: 10.1094/cm-2009-1211-01-rs

[52]

R Core Team. 2018. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

[53]

Olivoto T, Nardino M. 2021. MGIDI: toward an effective multivariate selection in biological experiments. Bioinformatics 37:1383−89

doi: 10.1093/bioinformatics/btaa981

[54]

Ainsworth EA, Serbin SP, Skoneczka JA, Townsend PA. 2014. Using leaf optical properties to detect ozone effects on foliar biochemistry. Photosynthesis Research 119:65−76

doi: 10.1007/s11120-013-9837-y

[55]

MacKinney G. 1941. Absorption of light by chlorophyll solutions. Journal of Biological Chemistry 140:315−22

doi: 10.1016/s0021-9258(18)51320-x

[56]

Baker NR. 2008. Chlorophyll fluorescence: a probe of photosynthesis in vivo. Annual Review of Plant Biology 59:89−113

doi: 10.1146/annurev.arplant.59.032607.092759

[57]

Li Y, He N, Hou J, Xu L, Liu C, et al. 2018. Factors influencing leaf chlorophyll content in natural forests at the biome scale. Frontiers in Ecology and Evolution 6:64

doi: 10.3389/fevo.2018.00064

[58]

Peñuelas J, Filella I, Gamon JA. 1995. Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytologist 131:291−96

doi: 10.1111/j.1469-8137.1995.tb03064.x

[59]

Sullivan D, Zhang J, Kowalewski AR, Peake JB, Anderson WF, et al. 2017. Evaluating hybrid bermudagrass using spectral reflectance under different mowing heights and trinexapac-ethyl applications. HortTechnology 27:45−53

doi: 10.21273/horttech03436-16

[60]

Horler DNH, Dockray M, Barber J. 1983. The red edge of plant leaf reflectance. International Journal of Remote Sensing 4:273−88

doi: 10.1080/01431168308948546

[61]

Filella I, Penuelas J. 1994. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing 15:1459−70

doi: 10.1080/01431169408954177

[62]

Bell GE, Howell BM, Johnson GV, Raun WR, Solie JB, et al. 2004. Optical sensing of turfgrass chlorophyll content and tissue nitrogen. HortScience 39:1130−32

doi: 10.21273/hortsci.39.5.1130

[63]

Nagata N, Tanaka R, Tanaka A. 2007. The major route for chlorophyll synthesis includes[3, 8-divinyl]-chlorophyllide a reduction in Arabidopsis thaliana. Plant and Cell Physiology 48:1803−8

doi: 10.1093/pcp/pcm153

[64]

Zhao Y, Han Q, Ding C, Huang Y, Liao J, et al. 2020. Effect of low temperature on chlorophyll biosynthesis and chloroplast biogenesis of rice seedlings during greening. International Journal of Molecular Sciences 21:1390

doi: 10.3390/ijms21041390

[65]

Taghvaeian S, Chávez JL, Hattendorf MJ, Crookston MA. 2013. Optical and thermal remote sensing of turfgrass quality, water stress, and water use under different soil and irrigation treatments. Remote Sensing 5:2327−47

doi: 10.3390/rs5052327

[66]

Huete AR. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25:295−309

doi: 10.1016/0034-4257(88)90106-X

[67]

Gitelson AA, Stark R, Grits U, Rundquist D, Kaufman Y, et al. 2002. Vegetation and soil lines in visible spectral space: a concept and technique for remote estimation of vegetation fraction. International Journal of Remote Sensing 23:2537−62

doi: 10.1080/01431160110107806

[68]

Bartlett DS, Whiting GJ, Hartman JM. 1989. Use of vegetation indices to estimate indices to estimate intercepted solar radiation and net carbon dioxide exchange of a grass canopy. Remote Sensing of Environment 30:115−28

doi: 10.1016/0034-4257(89)90054-0

[69]

Frels K, Guttieri M, Joyce B, Leavitt B, Baenziger PS. 2018. Evaluating canopy spectral reflectance vegetation indices to estimate nitrogen use traits in hard winter wheat. Field Crops Research 217:82−92

doi: 10.1016/j.fcr.2017.12.004

[70]

Smith HF. 1936. A discriminant function for plant selection. Annals of Eugenics 7:240−50

doi: 10.1111/j.1469-1809.1936.tb02143.x

[71]

Lopez-Cruz M, Olson E, Rovere G, Crossa J, Dreisigacker S, et al. 2020. Regularized selection indices for breeding value prediction using hyper-spectral image data. Scientific Reports 10:8195

doi: 10.1038/s41598-020-65011-2

[72]

Reynolds M, Chapman S, Crespo-Herrera L, Molero G, Mondal S, et al. 2020. Breeder friendly phenotyping. Plant Science 295:110396

doi: 10.1016/j.plantsci.2019.110396