| [1] |
Araus JL, Cairns JE. 2014. Field high-throughput phenotyping: the new crop breeding frontier. |
| [2] |
Zhang J, He Y, Liu J, Fan J, Shang J, et al. 2024. Integrating spectral data and phylogeographic patterns to study plant genetic variation: a review. |
| [3] |
Vines PL, Zhang J. 2022. High-throughput plant phenotyping for improved turfgrass breeding applications. |
| [4] |
Morris KN, Shearman RC. 1998. NTEP turfgrass evaluation guidelines. In NTEP Turfgrass Evaluation Workshop. Beltsville MD: National Turfgrass Evaluation Program. pp. 1−5 www.ntep.org/pdf/ratings.pdf |
| [5] |
Bremer DJ, Lee H, Su K, Keeley SJ. 2011. Relationships between normalized difference vegetation index and visual quality in cool-season turfgrass: II. Factors affecting NDVI and its component reflectances. |
| [6] |
Jiang Y, Huang B. 2001. Drought and heat stress injury to two cool-season turfgrasses in relation to antioxidant metabolism and lipid peroxidation. |
| [7] |
Mutlu SS, Sönmez NK, Çoşlu M, Türkkan HR, Zorlu D. 2023. UAV-based imaging for selection of turfgrass drought resistant cultivars in breeding trials. |
| [8] |
Rouse JW, Haas RH, Schell JA, Deering DW. 1974. Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite-1 Symposium, Washington, DC, USA, 10−14 December, 1973, eds. Freden SC, Mercanti EP, Becker MA. NASA SP-351. Washington, DC: NASA Special Publication. pp. 309−317 https://ui.adsabs.harvard.edu/abs/1974NASSP.351.309R/abstract |
| [9] |
Gitelson A, Merzlyak MN. 1994. Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. |
| [10] |
Merzlyak MN, Gitelson AA, Chivkunova OB, Rakitin VY. 1999. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. |
| [11] |
Gopinath L, Moss JQ, Wu Y, Schwartz BM. 2022. Drought response of 10 bermudagrass genotypes under field and controlled environment conditions. |
| [12] |
Zuffo AM, Steiner F, Aguilera JG, Ratke RF, Barrozo LM, et al. 2022. Selected indices to identify water-stress-tolerant tropical forage grasses. |
| [13] |
Katuwal KB, Yang H, Huang B. 2023. Evaluation of phenotypic and photosynthetic indices to detect water stress in perennial grass species using hyperspectral, multispectral and chlorophyll fluorescence imaging. |
| [14] |
Qian YL, Engelke MC. 1999. Performance of five turfgrasses under linear gradient irrigation. |
| [15] |
Fitz–Rodríguez E, Choi CY. 2002. Monitoring turfgrass quality using multispectral radiometry. |
| [16] |
Hong M, Bremer DJ, van der Merwe D. 2019. Using small unmanned aircraft systems for early detection of drought stress in turfgrass. |
| [17] |
Badzmierowski MJ, McCall DS, Evanylo G. 2019. Using hyperspectral and multispectral indices to detect water stress for an urban turfgrass system. |
| [18] |
Gao S, Zhong R, Yan K, Ma X, Chen X, et al. 2023. Evaluating the saturation effect of vegetation indices in forests using 3D radiative transfer simulations and satellite observations. |
| [19] |
Anderegg J, Yu K, Aasen H, Walter A, Liebisch F, et al. 2020. Spectral vegetation indices to track senescence dynamics in diverse wheat germplasm. |
| [20] |
Moon H, Kim H, Cho Y, Jo E, Ryu J, et al. 2024. Differences in vegetation index values using measurements from two azimuth and multiple zenith viewing angles. |
| [21] |
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. |
| [22] |
Lowe A, Harrison N, French AP. 2017. Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. |
| [23] |
Nguyen A, Sharma A, Prasad R. 2025. Understanding Vegetation Indices Used in Precision Agriculture. Alabama Cooperative Extension System. www.aces.edu/blog/topics/crop-production/understanding-vegetation-indices-used-in-precision-agriculture |
| [24] |
Penuelas J, Baret F, Filella I. 1995. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica 31:221−230 |
| [25] |
Gitelson A. 2020. Towards a generic approach to remote non-invasive estimation of foliar carotenoid-to-chlorophyll ratio. |
| [26] |
Gamon JA, Field CB, Goulden ML, Griffin KL, Hartley AE, et al. 1995. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. |