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Frontiers of Agricultural Science and Engineering

Front. Agr. Sci. Eng.    2019, Vol. 6 Issue (3) : 296-308     https://doi.org/10.15302/J-FASE-2019269
RESEARCH ARTICLE
Spectral reflectance indices as proxies for yield potential and heat stress tolerance in spring wheat: heritability estimates and marker-trait associations
Caiyun LIU1, Francisco PINTO1(), C. Mariano COSSANI2,3, Sivakumar SUKUMARAN1(), Matthew P. REYNOLDS1
1. International Maize and Wheat Improvement Center (CIMMYT), Texcoco 56237, Mexico
2. South Australian Research and Development Institute–SARDI, Adelaide 5001, Australia
3. School of Agriculture, Food and Wine, The University of Adelaide, Urrbrae SA 5064, Australia
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Abstract

The application of spectral reflectance indices (SRIs) as proxies to screen for yield potential (YP) and heat stress (HS) is emerging in crop breeding programs. Thus, a comparison of SRIs and their associations with grain yield (GY) under YP and HS conditions is important. In this study, we assessed the usefulness of 27 SRIs for indirect selection for agronomic traits by evaluating an elite spring wheat association mapping initiative (WAMI) population comprising 287 elite lines under YP and HS conditions. Genetic and phenotypic analysis identified 11 and 9 SRIs in different developmental stages as efficient indirect selection indices for yield in YP and HS conditions, respectively. We identified enhanced vegetation index (EVI) as the common SRI associated with GY under YP at booting, heading and late heading stages, whereas photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI) were the common SRIs under booting and heading stages in HS. Genome-wide association study (GWAS) using 18704 single nucleotide polymorphisms (SNPs) from Illumina iSelect 90K identified 280 and 43 marker-trait associations for efficient SRIs at different developmental stages under YP and HS, respectively. Common genomic regions for multiple SRIs were identified in 14 regions in 9 chromosomes: 1B (60–62 cM), 3A (15, 85–90, 101– 105 cM), 3B (132–134 cM), 4A (47–51 cM), 4B (71– 75 cM), 5A (43–49, 56–60, 89–93 cM), 5B (124–125 cM), 6A (80–85 cM), and 6B (57–59, 71 cM). Among them, SNPs in chromosome 5A (89–93 cM) and 6A (80–85 cM) were co-located for yield and yield related traits. Overall, this study highlights the utility of SRIs as proxies for GY under YP and HS. High heritability estimates and identification of marker-trait associations indicate that SRIs are useful tools for understanding the genetic basis of agronomic and physiological traits.

Keywords genome-wide association study (GWAS)      heat tolerance      spectral reflectance      spring wheat     
Corresponding Authors: Francisco PINTO,Sivakumar SUKUMARAN   
Just Accepted Date: 15 May 2019   Online First Date: 10 July 2019    Issue Date: 26 July 2019
 Cite this article:   
Caiyun LIU,Francisco PINTO,C. Mariano COSSANI, et al. Spectral reflectance indices as proxies for yield potential and heat stress tolerance in spring wheat: heritability estimates and marker-trait associations[J]. Front. Agr. Sci. Eng. , 2019, 6(3): 296-308.
 URL:  
http://journal.hep.com.cn/fase/EN/10.15302/J-FASE-2019269
http://journal.hep.com.cn/fase/EN/Y2019/V6/I3/296
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Caiyun LIU
Francisco PINTO
C. Mariano COSSANI
Sivakumar SUKUMARAN
Matthew P. REYNOLDS
SRIs Equation
Vegetation indices
Simple ratio(SR) RNIR/RRED[34]
Normalized difference vegetation index_670 (NDVI_670) (R780 – R670)/(R780 + R670)[14]
Normalized difference vegetation index_670 (NDVI_705) (R750 – R705)/(R750 + R705)[37]
Enhanced vegetation index (EVI) 2.5(RNIR – RRED)/(RNIR + 6RRED - 7.5RBLUE + 1)[38]
MERIS terrestrial chlorophyll index 1 (MTCI1) (R754 – R709)/(R709 – R681)[39]
MERIS terrestrial chlorophyll index 2 (MTCI2) (RNIR – R748)/(R748 – RRED)[39]
Optimized soil adjusted vegetation index (OSAVI) (RNIR – RRED)/(RNIR + RRED + 0.16)[42]
Transformed chlorophyll absorption in reflectance index (TCARI) 3((R700 – R670) – 0.2(R700 – R550) (R700/R670))[43]
Water indices
Water index (WI) R970/R900[45]
Normalized water index_1 (NWI_1) (R970 – R900)/(R970 + R900)[23]
Normalized water index_2 (NWI_2) (R970 – R850)/(R970 + R850)[23]
Normalized water index_3 (NWI_3) (R970 – R880)/(R970 + R880)[47]
Normalized water index_4 (NWI_4) (R970 – R920)/(R970 + R920)[47]
Red edge indices
Normalized phaeophytinization index (NPQI) (R415 – R435)/(R415 + R435)[49]
Chlorophyll indices
Chlorophyll ratio (AB ratio) RARSa/RARSb[35]
Ratio analysis of reflectance spectra for chlorophyll a (RARSa) R675/R700[36]
Ratio analysis of reflectance spectra for chlorophyll b (RARSb) (R675/R650× R700)[36]
Ratio analysis of reflectance spectra for carotenoids (RARSc) R760/R500[36]
Pigment specific simple ratio a (PSSRa) R800/R680[40]
Anthocyanin reflectance index 1 (ARI1) 1/R550– 1/R700[41]
Anthocyanin reflectance index 2 (ARI2) R800((1/R550) – (1/R700))[41]
Carotenoid reflectance index 1 (CRI1) 1/R510– 1/R550[44]
Carotenoid reflectance index 2 (CRI2) 1/R510– 1/R700[44]
Caroenoide
Normalized difference pigment index (NDPI) (R680−R430)/(R680 + R430)[45]
Plant senescence reflectance index (PSRI) (R680 – R500)/R750[46]
Structure insensitive pigment index 2 (SIPI2) (R800 – R445)/(R800 + R680)[48]
Radiation use efficiency
Photochemical reflectance index (PRI) (R531 – R570)/(R531 + R570)[15]
Tab.1  SRIs measured on the WAMI population under YP and HS environments in Ciudad Obregon, Mexico during the 2015–2016 growing season
SRIs YP HS
Booting Heading Heading+ 7 days Booting Heading
Mean H2 r Mean H2 r Mean H2 r Mean H2 r Mean H2 r
SR 25.84 0.45 0.01 23.06 0.63 -0.11 16.86 0.11 -0.02 19.83 0.68 0.18 14.39 0.77 0.29**
NDVI_670 0.92 0.07 0.13 0.91 0.70 -0.11 0.89 0.47 0.06 0.90 0.71 0.18 0.87 0.78 0.30**
NDVI_705 0.74 0.32 0.25** 0.73 0.54 0.09 0.70 0.34 0.19 0.66 0.69 0.26** 0.65 0.74 0.39**
EVI 0.66 0.26 0.23** 0.68 0.58 0.15** 0.61 0.34 0.23** 0.65 0.77 0.14 0.58 0.64 0.21
MTCI1 3.67 0.52 0.18 3.55 0.54 0.04 2.91 0.02 0.09 2.56 0.67 0.24** 2.36 0.73 0.38**
MTCI2 0.23 0.52 0.36** 0.27 0.45 0.26** 0.28 0.21 0.21 0.19 0.72 0.08 0.25 0.73 0.22**
OSAVI 0.70 0.07 0.28** 0.71 0.54 0.22** 0.68 0.40 0.30** 0.70 0.77 0.14 0.67 0.63 0.21
TCARI 0.08 0.50 -0.08 0.08 0.54 -0.03 0.10 0.04 0.07 0.13 0.84 -0.08 0.10 0.48 -0.19
WI 0.86 0.21 -0.23** 0.84 0.48 -0.22** 0.80 0.04 -0.13 0.88 0.54 -0.30** 0.88 0.63 -0.42**
NWI_1 -0.08 0.22 -0.23** -0.09 0.49 -0.22** -0.09 0.01 -0.12 -0.07 0.54 -0.30** -0.07 0.63 -0.42**
NWI_2 -0.07 0.07 -0.22** -0.08 0.46 -0.24** -0.08 0.03 -0.08 -0.06 0.63 -0.34** -0.05 0.58 -0.43**
NWI_3 -0.07 0.09 -0.23** -0.09 0.48 -0.24** -0.09 0.06 -0.07 -0.07 0.52 -0.30** -0.06 0.57 -0.42**
NWI_4 -0.07 0.22 -0.22** -0.08 0.50 -0.21 -0.08 0.08 -0.08 -0.06 0.55 -0.30** -0.06 0.62 -0.41**
AB ratio 0.02 0.45 0.02 0.03 0.68 0.20 0.06 0.01 0.08 0.03 0.69 -0.16 0.04 0.52 -0.28**
PSSRa 25.42 0.45 0.21 22.87 0.62 0.30** 17.11 0.11 0.17 20.10 0.68 -0.01 14.67 0.78 -0.07
RARSa 0.45 0.43 0.06 0.49 0.85 -0.09 0.52 0.22 -0.09 0.39 0.87 0.11 0.51 0.87 0.22**
RARSb 20.59 0.58 -0.05 18.30 0.59 -0.15 16.23 0.03 -0.06 13.02 0.75 0.15 13.63 0.43 0.22**
RARSc 21.51 0.51 0.02 19.77 0.74 -0.11 15.77 0.18 -0.01 16.85 0.63 0.16 13.91 0.76 0.29**
ARI1 -0.81 0.38 -0.35** -0.43 0.71 -0.22** 0.34 0.56 -0.23** -0.34 0.80 -0.18 0.34 0.75 -0.21
ARI2 -0.41 0.50 -0.39** -0.24 0.76 -0.26** 0.17 0.49 -0.18 -0.19 0.78 -0.18 0.16 0.76 -0.22**
CRI1 19.26 0.55 -0.16 14.70 0.80 -0.24** 13.39 0.41 -0.21 14.96 0.65 0.05 10.86 0.66 0.12
CRI2 18.45 0.59 -0.18 14.26 0.83 -0.25** 13.70 0.47 -0.23** 14.63 0.68 0.01 11.20 0.66 0.06
NPQI -0.07 0.56 0.33** -0.04 0.56 0.32** -0.02 0.02 0.09 -0.09 0.62 0.02 -0.06 0.45 0.03
NDPI -0.01 0.51 -0.42** 0.01 0.82 -0.30** 0.10 0.12 -0.02 0.02 0.82 -0.15 0.04 0.78 -0.21
PSRI -0.005 0.04 -0.21 -0.005 0.86 -0.21 0.03 0.01 0.07 -0.01 0.83 -0.13 -0.001 0.81 -0.21
SIPI2 0.92 0.21 0.07 0.91 0.74 -0.15 0.90 0.49 0.01 0.90 0.67 0.15 0.88 0.76 0.27**
PRI 0.01 0.24 0.32** 0.01 0.53 0.24** 0.03 0.04 0.08 0.01 0.83 0.29** 0.001 0.80 0.30**
Tab.2  Mean values, broad sense heritability (H2), and correlation coefficients (r) of SRIs with GY of the WAMI population under YP and HS environments in Ciudad Obregon, Mexico during the 2015–2016 growing season
Fig.1  Common and different spectral reflectance indices (SRIs) showed significant correlations with grain yield (GY) when measured at booting, heading, and heading plus 7 days stages under yield potential (YP) (a) and heat stress (HS) (b) environments in Cd. Obregon, Mexico during the 2015–2016 season. NDPI, normalized difference pigment index; PRI, photochemical reflectance index; NPQI, normalized phaeophytinization index; MTCI, MERIS terrestrial chlorophyll index; NWI, normalized water index; ARI, anthocyanin reflectance index; EVI, enhanced vegetation index; NDVI, normalized difference vegetation index; CRI, carotenoid reflectance index; OSAVI, optimized soil adjusted vegetation index; RARS, ratio analysis of reflectance spectra for chlorophyll; AB ratio, chlorophyll ratio; WI, water index; PSSR, pigment specific simple ratio.
Fig.2  Manhattan plots for spectral reflectance indices (SRIs) of wheat association mapping initiative (WAMI) population at booting, heading, and heading plus 7 days under yield potential (YP) ((a)–(f)) and heat stress (HS) ((g)–(j)) environments. NDPI, normalized difference pigment index; EVI, enhanced vegetation index; CRI, carotenoid reflectance index; ARI, anthocyanin reflectance index; NDVI, normalized difference vegetation index; NWI, normalized water index; AB ratio, chlorophyll ratio.
Fig.3  Genomic regions associated with agronomic traits and spectral reflectance indices (SRIs) in the wheat association mapping initiative (WAMI) population at booting (purple), heading (green), and heading plus 7 days (blue) under yield potential (YP) and at booting (orange) and heading (red) stages under heat stress (HS) environments. NDVI, normalized difference vegetation index; ARI, anthocyanin reflectance index; GN, grain number; RARS, ratio analysis of reflectance spectra for chlorophyll; PH, plant height; MTCI, MERIS terrestrial chlorophyll index; DTH, days to heading; GY, grain yield; TGW, thousand-grain weight; DTM, days to maturity; NDPI, normalized difference pigment index; PRI, photochemical reflectance index; CRI, carotenoid reflectance index; PSSR, pigment specific simple ratio; OSAVI, optimized soil adjusted vegetation index; EVI, enhanced vegetation index; NWI, normalized water index; NPQI, normalized phaeophytinization index; AB ratio, chlorophyll ratio; WI, water index.
Marker Chromosome Position/cM Associated traits
BS00075119_51 3A 15 NDVI_705 (YPB), MTCI2 (YPB)
BobWhite_c9468_453 3A 88 NDPI (YPB, YPH), ARI2 (YPB, YPH)
BobWhite_c9468_478 3A 88 NDPI (YPB, YPH), ARI2 (YPB, YPH)
BS00070511_51 3A 88 NDPI (YPB), ARI2 (YPB)
IAAV1334 3A 88 NDPI (YPB), ARI2 (YPB)
TA001068-0306-w 3A 88 NDPI (YPB, YPH), ARI2 (YPB, YPH)
wsnp_BE406587A_Ta_2_1 3A 88 NDPI (YPB, YPH), ARI2 (YPB, YPH)
wsnp_Ex_c22766_31972202 3A 88 NDPI (YPB, YPH), ARI2 (YPB, YPH)
wsnp_Ex_c24293_33532428 3A 88 NDPI (YPB, YPH), ARI2 (YPB, YPH)
wsnp_Ex_c9468_15697512 3A 88 NDPI (YPB, YPH), ARI2 (YPB, YPH)
BS00040798_51 3A 89 NDPI (YPB, YPH), ARI2 (YPB)
BS00048031_51 3A 89 NDPI (YPB), ARI2 (YPB)
Excalibur_c29205_537 3A 89 NDPI (YPB, YPH), ARI2 (YPB, YPH)
Excalibur_c7181_813 3A 89 NDPI (YPB, YPH), ARI2 (YPB)
Excalibur_c854_1459 3A 89 NDPI (YPB, YPH), ARI2 (YPB)
Excalibur_rep_c76510_255 3A 89 NDPI (YPB, YPH), ARI2 (YPB, YPH)
Kukri_c101770_328 3A 89 NDPI (YPB, YPH), ARI2 (YPH)
Kukri_c82097_197 3A 89 NDPI (YPB, YPH), ARI2 (YPB)
RAC875_c10669_714 3A 89 NDPI (YPB), ARI2 (YPB)
wsnp_CAP11_c318_261649 3A 89 NDPI (YPB), ARI2 (YPB)
wsnp_Ex_c2331_4369782 3A 89 NDPI (YPB), ARI2 (YPB)
wsnp_Ex_c25668_34932560 3A 89 NDPI (YPB), ARI2 (YPB)
wsnp_Ex_rep_c66865_65262277 3A 89 NDPI (YPB), ARI2 (YPB)
wsnp_Ex_rep_c66865_65262612 3A 89 NDPI (YPB), ARI2 (YPB)
wsnp_Ex_rep_c66865_65263145 3A 89 NDPI (YPB), ARI2 (YPB)
wsnp_Ra_c10669_17515792 3A 89 NDPI (YPB), ARI2 (YPB)
wsnp_Ra_c29280_38672141 3A 89 NDPI (YPB, YPH), ARI2 (YPH)
wsnp_RFL_Contig4814_5829093 3A 89 NDPI (YPB, YPH), ARI2 (YPB)
BobWhite_c43681_334 3A 90 NDPI (YPB), ARI2 (YPB)
BS00110405_51 3A 90 NDPI (YPB), ARI2 (YPB)
GENE-3343_183 3A 90 NDPI (YPB), ARI2 (YPB)
IAAV8924 3A 90 NDPI (YPB), PRI (YPB)
Kukri_c25564_185 3A 90 NDPI (YPB), PRI (YPB), ARI2 (YPB)
RAC875_c842_1516 3A 90 NDPI (YPB), ARI2 (YPB)
RAC875_rep_c117959_132 3A 90 NDPI (YPB), PRI (YPB)
RFL_Contig1034_2351 3A 90 NDPI (YPB), ARI2 (YPB)
wsnp_Ex_c35073_43285821 3A 90 NDPI (YPB), PRI (YPB)
wsnp_JD_c2743_3678590 3A 90 NDPI (YPB), ARI2 (YPB)
wsnp_JD_c3034_4017676 3A 90 NDPI (YPB), ARI2 (YPB)
wsnp_Ku_c3286_6111360 3A 90 NDPI (YPB), ARI2 (YPB)
BS00000445_51 3A 101 CRI2 (YPH, YPH7)
BS00001478_51 3A 101 CRI2 (YPH, YPH7)
BS00061179_51 3A 101 CRI2 (YPH, YPH7)
BS00080879_51 3A 101 CRI2 (YPH, YPH7)
Excalibur_c39248_485 3A 101 CRI2 (YPH, YPH7)
wsnp_Ex_c26887_36107413 3A 103 CRI2 (YPH, YPH7), ARI2 (YPH)
BobWhite_c13704_244 3A 104 CRI2 (YPH, YPH7)
BS00056089_51 3A 104 NDPI (YPH), ARI2 (YPH), CRI2 (YPH7)
RAC875_rep_c109433_782 3A 104 CRI2 (YPH, YPH7)
BS00061173_51 3A 105 CRI2 (YPH, YPH7)
wsnp_Ex_c9483_15722127 3A 105 CRI2 (YPH, YPH7)
RAC875_rep_c72275_185 3B 132 NDPI (YPB), PRI (YPH)
Excalibur_c82684_66 4B 71 EVI (YPH7), OSAVI (YPH7)
RAC875_c103110_275 4B 71 EVI (YPH7), OSAVI (YPH7)
RAC875_c24550_1150 4B 71 EVI (YPH7), OSAVI (YPH7)
IACX3657 5A 43 NDVI_705 (YPB), PRI (YPB)
RAC875_c13931_205 5A 89 ARI2 (HSH), PRI (HSH)
RAC875_rep_c112818_870 5B 125 CRI2 (YPH), MTCI2 (YPH)
BobWhite_c13091_385 6B 71 NDVI_705 (HSH), AB ratio (HSH)
Ex_c100170_579 6B 71 NDVI_705 (HSH), AB ratio (HSH)
Excalibur_rep_c70364_129 6B 71 NDVI_705 (HSH), AB ratio (HSH)
IAAV1816 6B 71 NDVI_705 (HSH), AB ratio (HSH)
Tab.3  Significant (P<0.0001) markers associated with multiple spectral reflectance indices of the WAMI population at booting (YPB), heading (YPH), and 7 days after heading (YPH7) under YP, and at booting (HSB), heading (HSH) under HS in Ciudad Obregon, Mexico during the 2015–2016 growing season
1 J J Pereira. Climate change 2014—impacts, adaptation and vulnerability. Part B: regional aspects. In: Contribution of Working Group II to the Fifth Assessment Report of the IPCC. Cambridge: Cambridge University Press, 2014
2 P Prasad, K Boote, L Allen Jr, J Sheehy, J Thomas. Species, ecotype and cultivar differences in spikelet fertility and harvest index of rice in response to high temperature stress. Field Crops Research, 2006, 95(2–3): 398–411
https://doi.org/10.1016/j.fcr.2005.04.008
3 S P Loss, K H M Siddique. Morchological and physiological traits associated with wheat yield increases in Mediterranean environment. In: Sparks D, ed. Advances in Agronomy. San Diego: Academic Press, 1994, 229–276
4 J Ferrio, D Villegas, J Zarco, N Aparicio, J Araus, C Royo. Assessment of durum wheat yield using visible and near-infrared reflectance spectra of canopies. Field Crops Research, 2005, 94(2–3): 126–148
https://doi.org/10.1016/j.fcr.2004.12.002
5 M Reynolds, S Rajaram, K Sayre. Physiological and genetic changes of irrigated wheat in the post-green revolution period and approaches for meeting projected global demand. Crop Science, 1999, 39(6): 1611–1621
https://doi.org/10.2135/cropsci1999.3961611x
6 C M Cossani, M P Reynolds. Physiological traits for improving heat tolerance in wheat. Plant Physiology, 2012, 160(4): 1710–1718
https://doi.org/10.1104/pp.112.207753 pmid: 23054564
7 M Reynolds, A Pask, D Mullan. Physiological breeding I: interdisciplinary approaches to improve crop adaptation. Mexico, D.F.: CIMMYT, 2012
8 M Tattaris, M P Reynolds, S C Chapman. A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Frontiers of Plant Science, 2016, 7: 1131
https://doi.org/10.3389/fpls.2016.01131 pmid: 27536304
9 J L Araus, S C Kefauver, M Zaman-Allah, M S Olsen, J E Cairns. Translating high-throughput phenotyping into genetic gain. Trends in Plant Science, 2018, 23(5): 451–466
https://doi.org/10.1016/j.tplants.2018.02.001 pmid: 29555431
10 E B Knipling. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment, 1970, 1(3): 155–159
https://doi.org/10.1016/S0034-4257(70)80021-9
11 R Shorter, R Lawn, G Hammer. Improving genotypic adaptation in crops—a role for breeders, physiologists and modellers. Experimental Agriculture, 1991, 27(2): 155–175
https://doi.org/10.1017/S0014479700018810
12 M Babar, M Van Ginkel, A Klatt, B Prasad, M Reynolds. The potential of using spectral reflectance indices to estimate yield in wheat grown under reduced irrigation. Euphytica, 2006, 150(1–2): 155–172
https://doi.org/10.1007/s10681-006-9104-9
13 C F Jordan. Derivation of leaf-area index from quality of light on the forest floor. Ecology, 1969, 50(4): 663–666
https://doi.org/10.2307/1936256
14 J Rouse, R Haas, J Schell, D Deerin. Monitoring vegetation systems in the great plains with ERTS. In: NASA. Goddard Space Flight Center 3d ERTS-1 Symposium, College Station. Washington: NASA, 1974, 309–317
15 J A Gamon, L Serrano, J S Surfus. The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia, 1997, 112(4): 492–501
https://doi.org/10.1007/s004420050337 pmid: 28307626
16 J Penuelas, J A Gamon, K L Griffin, C B Field. Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sensing of Environment, 1993, 46(2): 110–118
https://doi.org/10.1016/0034-4257(93)90088-F
17 J Peñuelas, J Pinol, R Ogaya, I Filella. Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 1997, 18(13): 2869–2875
https://doi.org/10.1080/014311697217396
18 P Sellers. Canopy reflectance, photosynthesis, and transpiration: II. The role of biophysics in the linearity of their interdependence. Remote Sensing of Environment, 1987, 21(2): 143–183
https://doi.org/10.1016/0034-4257(87)90051-4
19 C L Wiegand, A J Richardson. Use of spectral vegetation indices to infer leaf area, evapotranspiration and yield: I. Rationale. Agronomy Journal, 1990, 82(3): 623–629
https://doi.org/10.2134/agronj1990.00021962008200030037x
20 F Baret, G Guyot. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 1991, 35(2–3): 161–173
https://doi.org/10.1016/0034-4257(91)90009-U
21 E W Chappelle, M S Kim, J E McMurtrey III. Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sensing of Environment, 1992, 39(3): 239–247
https://doi.org/10.1016/0034-4257(92)90089-3
22 N Aparicio, D Villegas, J Araus, J Casadesus, C Royo. Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Science, 2002, 42(5): 1547–1555
https://doi.org/10.2135/cropsci2002.1547
23 M Babar, M Reynolds, M Van Ginkel, A Klatt, W Raun, M Stone. Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Science, 2006, 46(3): 1046–1057
https://doi.org/10.2135/cropsci2005.0211
24 W R Raun, J B Solie, G V Johnson, M L Stone, E V Lukina, W E Thomason, J S Schepers. In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal, 2001, 93(1): 131–138
https://doi.org/10.2134/agronj2001.931131x
25 M Hassan, M Yang, A Rasheed, X Jin, X Xia, Y Xiao, Z He. Time-series multispectral indices from unmanned aerial vehicle imagery reveal senescence rate in bread wheat. Remote Sensing, 2018, 10(6): 809
https://doi.org/10.3390/rs10060809
26 M A Hassan, M Yang, A Rasheed, G Yang, M Reynolds, X Xia, Y Xiao, Z He. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Science, 2019, 282: 95–103
https://doi.org/10.1016/j.plantsci.2018.10.022 pmid: 31003615
27 S A Gizaw, J G V Godoy, M O Pumphrey, A H Carter. Spectral reflectance for indirect selection and genome-wide association analyses of grain yield and drought tolerance in North American spring wheat. Crop Science, 2018, 58(6): 2289–2301
https://doi.org/10.2135/cropsci2017.11.0690
28 S A Gizaw, J G V Godoy, K Garland-Campbell, A H Carter. Using spectral reflectance indices as proxy phenotypes for genome-wide association studies of yield and yield stability in Pacific Northwest winter wheat. Crop Science, 2018, 58(3): 1232–1241
https://doi.org/10.2135/cropsci2017.11.0710
29 S Wang, D Wong, K Forrest, A Allen, S Chao, B E Huang, M Maccaferri, S Salvi, S G Milner, L Cattivelli, A M Mastrangelo, A Whan, S Stephen, G Barker, R Wieseke, J Plieske, M Lillemo, D Mather, R Appels, R Dolferus, G Brown-Guedira, A Korol, A R Akhunova, C Feuillet, J Salse, M Morgante, C Pozniak, M C Luo, J Dvorak, M Morell, J Dubcovsky, M Ganal, R Tuberosa, C Lawley, I Mikoulitch, C Cavanagh, K J Edwards, M Hayden, E Akhunov. Characterization of polyploid wheat genomic diversity using a high-density 90000 single nucleotide polymorphism array. Plant Biotechnology Journal, 2014, 12(6): 787–796
https://doi.org/10.1111/pbi.12183 pmid: 24646323
30 M S Lopes, S Dreisigacker, R J Peña, S Sukumaran, M P Reynolds. Genetic characterization of the wheat association mapping initiative (WAMI) panel for dissection of complex traits in spring wheat. Theoretical and Applied Genetics, 2015, 128(3): 453–464
https://doi.org/10.1007/s00122-014-2444-2 pmid: 25540818
31 S Sukumaran, S Dreisigacker, M Lopes, P Chavez, M P Reynolds. Genome-wide association study for grain yield and related traits in an elite spring wheat population grown in temperate irrigated environments. Theoretical and Applied Genetics, 2015, 128(2): 353–363
https://doi.org/10.1007/s00122-014-2435-3 pmid: 25490985
32 S Sukumaran, M P Reynolds, M S Lopes, J Crossa. Genome-wide association study for adaptation to agronomic plant density: a component of high yield potential in spring wheat. Crop Science, 2015, 55(6): 2609–2619
https://doi.org/10.2135/cropsci2015.03.0139
33 M Reynolds, M Balota, M Delgado, I Amani, R Fischer. Physiological and morphological traits associated with spring wheat yield under hot, irrigated conditions. Functional Plant Biology, 1994, 21(6): 717–730
https://doi.org/10.1071/PP9940717
34 G S Birth, G R McVey. Measuring the color of growing turf with a reflectance spectrophotometer 1. Agronomy Journal, 1968, 60(6): 640–643
https://doi.org/10.2134/agronj1968.00021962006000060016x
35 M Dale, D Causton. Use of the chlorophyll a/b ratio as a bioassay for the light environment of a plant. Functional Ecology, 1992, 6(2): 190–196
https://doi.org/10.2307/2389754
36 E Chapelle, M Kim, I McMurtrey. Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of Chl a, b and carotenoids in soybean leaves. Remote Sensing of Environment, 1992, 39: 239–247
https://doi.org/10.1016/0034-4257(92)90089-3
37 A Gitelson, M N Merzlyak. Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology B: Biology, 1994, 22(3): 247–252
https://doi.org/10.1016/1011-1344(93)06963-4
38 H Q Liu, A Huete. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(2): 457–465
https://doi.org/10.1109/36.377946
39 J Dash, P Curran. Evaluation of the MERIS terrestrial chlorophyll index (MTCI). Advances in Space Research, 2007, 39(1): 100–104
https://doi.org/10.1016/j.asr.2006.02.034
40 G A Blackburn. Quantifying chlorophylls and caroteniods at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sensing of Environment, 1998, 66(3): 273–285
https://doi.org/10.1016/S0034-4257(98)00059-5
41 A A Gitelson, M N Merzlyak, O B Chivkunova. Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology, 2001, 74(1): 38–45
https://doi.org/10.1562/0031-8655(2001)074<0038:OPANEO>2.0.CO;2 pmid: 11460535
42 G Rondeaux, M Steven, F Baret. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 1996, 55(2): 95–107
https://doi.org/10.1016/0034-4257(95)00186-7
43 D Haboudane, J R Miller, N Tremblay, P J Zarco-Tejada, L Dextraze. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 2002, 81(2–3): 416–426
https://doi.org/10.1016/S0034-4257(02)00018-4
44 A A Gitelson, Y Zur, O B Chivkunova, M N Merzlyak. Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochemistry and Photobiology, 2002, 75(3): 272–281
https://doi.org/10.1562/0031-8655(2002)075<0272:ACCIPL>2.0.CO;2 pmid: 11950093
45 J Peñuelas, I Filella, C Biel, L Serrano, R Save. The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing, 1993, 14(10): 1887–1905
https://doi.org/10.1080/01431169308954010
46 M N Merzlyak, A A Gitelson, O B Chivkunova, V Y Rakitin. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum, 1999, 106(1): 135–141
https://doi.org/10.1034/j.1399-3054.1999.106119.x
47 B Prasad, B F Carver, M L Stone, M Babar, W R Raun, A R Klatt. Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under great plains conditions. Crop Science, 2007, 47(4): 1426–1440
https://doi.org/10.2135/cropsci2006.07.0492
48 J Penuelas, I Filella, J A Gamon. Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytologist, 1995, 131(3): 291–296
https://doi.org/10.1111/j.1469-8137.1995.tb03064.x
49 J Peñuelas, I Filella, P Lloret, F Mun Oz, M Vilajeliu. Reflectance assessment of mite effects on apple trees. International Journal of Remote Sensing, 1995, 16(14): 2727–2733
https://doi.org/10.1080/01431169508954588
50 M Vargas, E Combs, G Alvarado, G Atlin, K Mathews, J Crossa. META: a suite of SAS programs to analyze multienvironment breeding trials. Agronomy Journal, 2013, 105(1): 11–19
https://doi.org/10.2134/agronj2012.0016
51 J Yu, G Pressoir, W H Briggs, I Vroh Bi, M Yamasaki, J F Doebley, M D McMullen, B S Gaut, D M Nielsen, J B Holland, S Kresovich, E S Buckler. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics, 2006, 38(2): 203–208
https://doi.org/10.1038/ng1702 pmid: 16380716
52 S D Turner. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. bioRxiv, 2014: 005165
53 R Valluru, M P Reynolds, W J Davies, S Sukumaran. Phenotypic and genome-wide association analysis of spike ethylene in diverse wheat genotypes under heat stress. New Phytologist, 2017, 214(1): 271–283
https://doi.org/10.1111/nph.14367 pmid: 27918628
54 S Sukumaran, M Lopes, S Dreisigacker, M Reynolds. Genetic analysis of multi-environmental spring wheat trials identifies genomic regions for locus-specific trade-offs for grain weight and grain number. Theoretical and Applied Genetics, 2018, 131(4): 985–998
https://doi.org/10.1007/s00122-017-3037-7 pmid: 29218375
55 J L Araus, G A Slafer, M P Reynolds, C Royo. Plant breeding and drought in C3 cereals: what should we breed for? Annals of Botany, 2002, 89(7): 925–940
https://doi.org/10.1093/aob/mcf049 pmid: 12102518
56 B Prasad, B F Carver, M L Stone, M Babar, W R Raun, A R Klatt. Genetic analysis of indirect selection for winter wheat grain yield using spectral reflectance indices. Crop Science, 2007, 47(4): 1416–1425
https://doi.org/10.2135/cropsci2006.08.0546
57 N Aparicio, D Villegas, J Casadesus, J L Araus, C Royo. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal, 2000, 92(1): 83–91
https://doi.org/10.2134/agronj2000.92183x
58 C Royo, N Aparicio, D Villegas, J Casadesus, P Monneveux, J Araus. Usefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions. International Journal of Remote Sensing, 2003, 24(22): 4403–4419
https://doi.org/10.1080/0143116031000150059
59 G E Condorelli, M Maccaferri, M Newcomb, P Andrade-Sanchez, J W White, A N French, G Sciara, R Ward, R Tuberosa. Comparative aerial and ground based high throughput phenotyping for the genetic dissection of NDVI as a proxy for drought adaptive traits in durum wheat. Frontiers of Plant Science, 2018, 9: 893
https://doi.org/10.3389/fpls.2018.00893 pmid: 29997645
60 C Liu, S Sukumaran, E Claverie, C Sansaloni, S Dreisigacker, M Reynolds. Genetic dissection of heat and drought stress QTLs in phenology-controlled synthetic-derived recombinant inbred lines in spring wheat. Molecular Breeding, 2019, 39(3): 34
https://doi.org/10.1007/s11032-019-0938-y
61 R Mittler. Abiotic stress, the field environment and stress combination. Trends in Plant Science, 2006, 11(1): 15–19
https://doi.org/10.1016/j.tplants.2005.11.002 pmid: 16359910
62 M Jamsheer K, A Laxmi. Expression of Arabidopsis FCS-Like Zinc finger genes is differentially regulated by sugars, cellular energy level, and abiotic stress. Frontiers of Plant Science, 2015, 6: 746
https://doi.org/10.3389/fpls.2015.00746 pmid: 26442059
63 J U Hwang, W Y Song, D Hong, D Ko, Y Yamaoka, S Jang, S Yim, E Lee, D Khare, K Kim, M Palmgren, H S Yoon, E Martinoia, Y Lee. Plant ABC transporters enable many unique aspects of a terrestrial plant’s lifestyle. Molecular Plant, 2016, 9(3): 338–355
https://doi.org/10.1016/j.molp.2016.02.003 pmid: 26902186
64 F De Rienzo, R R Gabdoulline, M C Menziani, R C Wade. Blue copper proteins: a comparative analysis of their molecular interaction properties. Protein Science, 2000, 9(8): 1439–1454
https://doi.org/10.1110/ps.9.8.1439 pmid: 10975566
65 S Ambawat, P Sharma, N R Yadav, R C Yadav. MYB transcription factor genes as regulators for plant responses: an overview. Physiology and Molecular Biology of Plants, 2013, 19(3): 307–321
https://doi.org/10.1007/s12298-013-0179-1 pmid: 24431500
66 L McHale, X Tan, P Koehl, R W Michelmore. Plant NBS-LRR proteins: adaptable guards. Genome Biology, 2006, 7(4): 212
https://doi.org/10.1186/gb-2006-7-4-212 pmid: 16677430
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