基于无人机多源影像数据的灌浆期人工合成小麦抗旱性评价

doi:10.3864/j.issn.0578-1752.2024.09.005

中国农业科学 ›› 2024, Vol. 57 ›› Issue (9): 1674-1686.doi: 10.3864/j.issn.0578-1752.2024.09.005

• 专题：小麦抗旱性鉴定及基因资源挖掘 • 上一篇下一篇

基于无人机多源影像数据的灌浆期人工合成小麦抗旱性评价

燕雯¹^,²(), 金秀良², 李龙², 徐子涵³, 苏悦¹^,², 张跃强³, 景蕊莲², 毛新国²(), 孙黛珍¹()

¹ 山西农业大学农学院，山西太谷 030801
² 中国农业科学院作物科学研究所/作物基因资源与育种全国重点实验室，北京 100081
³ 新疆农业科学院核技术生物技术研究所/农业农村部荒漠绿洲区作物生理生态与耕作重点实验室/新疆作物生物技术重点实验室，乌鲁木齐 830091

收稿日期:2024-01-05 接受日期:2024-03-11 出版日期:2024-05-01 发布日期:2024-05-09
通信作者:
孙黛珍，E-mail：sdz64@126.com。
毛新国，E-mail：maoxinguo@caas.cn。
联系方式: 燕雯，E-mail：yanwenzzl@163.com。
基金资助:
国家重点研发计划(2023YFD1201003); 国家重点研发计划(2022YFD1200201); 国家小麦产业技术体系(CARS-03-5)

Drought Resistance Evaluation of Synthetic Wheat at Grain Filling Using UAV-Based Multi-Source Imagery Data

YAN Wen¹^,²(), JIN XiuLiang², LI Long², XU ZiHan³, SU Yue¹^,², ZHANG YueQiang³, JING RuiLian², MAO XinGuo²(), SUN DaiZhen¹()

¹ College of Agronomy, Shanxi Agricultural University, Taigu 030801, Shanxi
² Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/State Key Laboratory of Crop Gene Resources and Breeding, Beijing 100081
³ Institute of Nuclear and Biological Technologies, Xinjiang Academy of Agricultural Sciences/Key Laboratory of Crop Ecophysiology and Farming System in Desert Oasis Region, Ministry of Agriculture and Rural Affairs/Xinjiang Key Laboratory of Crop Biotechnology, Urumqi 830091

Received:2024-01-05 Accepted:2024-03-11 Published:2024-05-01 Online:2024-05-09

摘要/Abstract

摘要：

【目的】基于无人机多源影像及产量数据评价人工合成小麦种质的抗旱性，优选高通量抗旱性鉴定指标，发掘抗旱人工合成小麦种质资源，为加快拓展小麦抗旱遗传资源、提升旱地小麦育种水平提供技术支撑和种质材料。【方法】以80份人工合成小麦种质及对照小麦品种新春37为试验材料，在田间进行小区播种，设置干旱和灌溉2种水分处理；利用无人机搭载多光谱及热红外相机采集试验材料灌浆期多源影像进行拼接处理，通过阈值分割等方法提取各试验材料的光谱指数；利用相关性分析和主成分分析鉴选抗旱相关光谱指标，结合单指标及综合评价方法鉴定人工合成小麦种质的抗旱性。【结果】基于无人机多源影像数据提取了80份人工合成小麦种质的19种光谱指数。不同光谱指数抗旱系数与小区产量抗旱指数的相关性分析结果表明，OSAVI的抗旱系数与抗旱指数的关联度最高，NDVI、CI_re和NDRE的抗旱系数与抗旱指数的关联度较高。部分光谱指数的抗旱系数间相关性较高，存在冗余信息，通过主成分分析，将19个光谱指数的抗旱系数转换为3个相互独立的综合指标，3个综合指标的贡献度分别为59.6%、12.0%和9.6%。利用加权隶属函数法聚合综合指标，通过公式计算获得各人工合成小麦种质的综合抗旱性度量值。基于抗旱指数鉴定出6份强抗旱人工合成小麦种质，基于综合抗旱性度量值鉴定出5份强抗旱种质，其中，SW004和SW009在2种方法的评价结果中均被评为强抗旱种质。基于OSAVI的抗旱系数对80份人工合成小麦种质进行抗旱性分级，分级结果与基于综合抗旱性度量值的分级结果基本一致。根据OSAVI的抗旱系数鉴定出的6份强抗旱种质中，有5份在基于综合抗旱性度量值分级中也被鉴定为强抗旱种质。【结论】基于无人机多源影像提取的光谱指数NDVI、OSAVI、CI_re和NDRE，以及基于光谱指数的综合抗旱性度量值均可用于辅助鉴定小麦种质抗旱性。

关键词: 多源影像, 光谱指数, 人工合成小麦, 抗旱性, 灌浆期

Abstract:

【Objective】To evaluate the drought resistance of synthetic wheat germplasm based on multi-source images collected by unmanned aerial vehicle (UAV) and yield data, explore high-throughput indices for drought resistance evaluation, and identify synthetic wheat germplasm resources with drought resistance. This provides technical support and germplasm materials for accelerating the expansion of drought-resistant genetic resources for wheat and enhancing the level of breeding for dryland wheat.【Method】Eighty synthetic wheat germplasm and the control variety Xin Chun 37 were used as plant materials, which were sown in the field and treated with a water regime of drought stress and irrigation. Multi-source images of test materials during filling stage were collected by multi-spectral and thermal infrared cameras equipped with unmanned aerial vehicle, and the spectral index of each test material was extracted by threshold segmentation. The analyses of Pearson’s correlation and principal component were performed to identify drought resistance-related spectral indices, and the drought resistance of each synthetic wheat germplasm was determined by single index and comprehensive evaluation methods. 【Result】The drought resistance coefficients of 19 spectral indices of 80 synthetic wheat germplasm were calculated based on multisource imagery data obtained from unmanned aerial vehicles. The correlation analysis between the spectral indices and the yield-based drought index (DRI) showed that among the drought resistance coefficients of the 19 spectral indices, OSAVI exhibited the highest correlation with the drought index, while NDVI, CI_re, and NDRE demonstrated relatively strong associations with the drought index. However, the different drought indices showed a high correlation, resulting in redundant information. The drought resistance coefficients of the 19 spectral indices were transformed into three independent comprehensive indicators through principal component analysis, with contribution rates of 59.6%, 12.0% and 9.6%, respectively. The comprehensive drought resistance index (D) for each synthetic wheat germplasm were calculated by aggregating the three independent comprehensive indicators using the weighted membership function method. 6 and 5 synthetic wheat germplasms with strong drought resistance were identified based on DRI and D, respectively. Among them, 2 germplasms (SW004 and SW009) with high drought resistance were detected based on both DRI and D. Furthermore, the drought resistance of the 80 synthetic wheat germplasms was graded based on the drought resistance coefficient of OSAVI, and the grading results were found to be consistent with that based on the D value. Among the six strongly drought-resistant germplasms identified based on the drought resistance coefficient of OSAVI, five of them were also classified as strongly drought-resistant germplasms based on comprehensive drought resistance evaluation.【Conclusion】The spectral indices NDVI, OSAVI, CI_re and NDRE extracted from UAV-based multi-source images, as well as the drought resistance comprehensive evaluation value can be used to assist in the identification of drought resistance of wheat germplasm.

Key words: multi-source images, spectral index, synthetic wheat, drought resistance, grain filling

燕雯, 金秀良, 李龙, 徐子涵, 苏悦, 张跃强, 景蕊莲, 毛新国, 孙黛珍. 基于无人机多源影像数据的灌浆期人工合成小麦抗旱性评价[J]. 中国农业科学, 2024, 57(9): 1674-1686.

YAN Wen, JIN XiuLiang, LI Long, XU ZiHan, SU Yue, ZHANG YueQiang, JING RuiLian, MAO XinGuo, SUN DaiZhen. Drought Resistance Evaluation of Synthetic Wheat at Grain Filling Using UAV-Based Multi-Source Imagery Data[J]. Scientia Agricultura Sinica, 2024, 57(9): 1674-1686.

0
/ / 推荐

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2024.09.005

https://www.chinaagrisci.com/CN/Y2024/V57/I9/1674

图/表 8

图1

表1

图2

图3

表2

图4

表3

图5

参考文献 46

[1]	QIN W L, WANG J, MA L F, WANG F L, HU N Y, YANG X Y, XIAO Y Y, ZHANG Y H, SUN Z C, WANG Z M, YU K. UAV-based multi-temporal thermal imaging to evaluate wheat drought resistance in different deficit irrigation regimes. Remote Sensing, 2022, 14(21): 5608. doi: 10.3390/rs14215608
[2]	BAPELA T, SHIMELIS H, TSILO T J, MATHEW I. Genetic improvement of wheat for drought tolerance: Progress, challenges and opportunities. Plants, 2022, 11(10): 1331. doi: 10.3390/plants11101331
[3]	廖祥政, 王瑾, 周荣华, 任正隆, 贾继增. 发掘人工合成小麦中千粒重QTL的有利等位基因. 作物学报, 2008, 34(11): 1877-1884. doi: DOI: 10.3724/SP.J.1006.2008.01877
	LIAO X Z, WANG J, ZHOU R H, REN Z L, JIA J Z. Mining favorable alleles of QTLs conferring 1000-grain weight from synthetic wheat. Acta Agronomica Sinica, 2008, 34(11): 1877-1884. (in Chinese)
[4]	王晓鸣, 邱丽娟, 景蕊莲, 任贵兴, 李英慧, 李春辉, 秦培友, 谷勇哲, 李龙. 作物种质资源表型性状鉴定评价: 现状与趋势. 植物遗传资源学报, 2022, 23(1): 12-20. doi: 10.13430/j.cnki.jpgr.20210802001
	WANG X M, QIU L J, JING R L, REN G X, LI Y H, LI C H, QIN P Y, GU Y Z, LI L. Evaluation on phenotypic traits of crop germplasm: Status and development. Journal of Plant Genetic Resources, 2022, 23(1): 12-20. (in Chinese)
[5]	李龙, 毛新国, 王景一, 李超男, 柳玉平, 景蕊莲. 小麦抗旱性研究进展与展望. 干旱地区农业研究, 2023, 41(3): 11-20.
	LI L, MAO X G, WANG J Y, LI C N, LIU Y P, JING R L. Progress and prospect of wheat research on drought resistance. Agricultural Research in the Arid Areas, 2023, 41(3): 11-20. (in Chinese)
[6]	白志英, 李存东, 孙红春, 赵金锋. 小麦代换系抗旱生理指标的主成分分析及综合评价. 中国农业科学, 2008, 41(12): 4264-4272. doi: 10.3864/j.issn.0578-1752.2008.12.043.
	BAI Z Y, LI C D, SUN H C, ZHAO J F. Principal component analysis and comprehensive evaluation on physiological indices of drought resistance in wheat substitution lines. Scientia Agricultura Sinica, 2008, 41(12): 4264-4272. doi: 10.3864/j.issn.0578-1752.2008.12.043. (in Chinese)
[7]	王士强, 胡银岗, 佘奎军, 周琳璘, 孟凡磊. 小麦抗旱相关农艺性状和生理生化性状的灰色关联度分析. 中国农业科学, 2007, 40(11): 2452-2459. doi: 10.3864/j.issn.0578-1752.at-2006-8460.
	WANG S Q, HU Y G, SHE K J, ZHOU L L, MENG F L. Gray relational grade analysis of agronomical and physi-biochemical traits related to drought tolerance in wheat. Scientia Agricultura Sinica, 2007, 40(11): 2452-2459. doi: 10.3864/j.issn.0578-1752.at-2006-8460. (in Chinese)
[8]	张军, 魏国, 彭彦珉, 汪洪涛, 黄修利, 吴金芝, 黄明, 李友军. 8份强筋小麦品种抗旱性评价. 麦类作物学报, 2023. https://link.cnki.net/urlid/61.1359.S.20231107.0924.006.
	ZHANG J, WEI G, PENG Y M, WANG H T, HUANG X L, WU JI Z, HUANG M, LI Y J. Drought resistance evaluation of eight strong gluten wheat varieties. Journal of Triticeae Crops, 2023. https://link.cnki.net/urlid/61.1359.S.20231107.0924.006. (in Chinese)
[9]	马玉慧, 张小虎, 马小乐, 司二静, 孟亚雄, 姚立蓉, 王化俊, 汪军成, 李葆春. 干旱胁迫下春小麦品种(系)萌发期抗旱性鉴定与评价. 分子植物育种, 2022, 20(19): 6459-6473.
	MA Y H, ZHANG X H, MA X L, SI E J, MENG Y X, YAO L R, WANG H J, WANG J C, LI B C. Identification and evaluation of drought resistance of spring wheat varieties (lines) at germination under drought stress. Molecular Plant Breeding, 2022, 20(19): 6459-6473. (in Chinese)
[10]	孟雨, 温鹏飞, 丁志强, 田文仲, 张学品, 贺利, 段剑钊, 刘万代, 冯伟. 基于热红外图像的小麦品种抗旱性鉴定与评价. 中国农业科学, 2022, 55(13): 2538-2551. doi: 10.3864/j.issn.0578-1752.2022.13.005.
	MENG Y, WEN P F, DING Z Q, TIAN W Z, ZHANG X P, HE L, DUAN J Z, LIU W D, FENG W. Identification and evaluation of drought resistance of wheat varieties based on thermal infrared image. Scientia Agricultura Sinica, 2022, 55(13): 2538-2551. doi: 10.3864/j.issn.0578-1752.2022.13.005. (in Chinese)
[11]	DAS S, CHRISTOPHER J, CHOUDHURY M R, APAN A, CHAPMAN S W, MENZIES N P, DANG Y P. Evaluation of drought tolerance of wheat genotypes in rain-fed sodic soil environments using high-resolution UAV remote sensing techniques. Biosystems Engineering, 2022, 217: 68-82. doi: 10.1016/j.biosystemseng.2022.03.004
[12]	WANG F M, HUANG J F, TANG Y L, WANG X Z. New vegetation index and its application in estimating leaf area index of rice. Rice Science, 2007, 14(3): 195-203. doi: 10.1016/S1672-6308(07)60027-4
[13]	GITELSON A A, KEYDAN G P, MERZLYAK M N. Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophysical Research Letters, 2006, 33(11): 11402.
[14]	RICHARDSON A, WIEGAND C. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 1977, 43(12): 1541-1552.
[15]	GILLESPIE A R, KAHLE A B, WALKER R E. Color enhancement of highly correlated images: ⅡChannel ratio and “chromaticity” transformation techniques. Remote Sensing of Environment, 1987, 22(3): 343-365. doi: 10.1016/0034-4257(87)90088-5
[16]	GITELSON A A, KAUFMAN Y J, MERZLYAK M N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 1996, 58(3): 289-298. doi: 10.1016/S0034-4257(96)00072-7
[17]	DAUGHTRY C S T, WALTHALL C L, KIM M S, DE COLSTOUN E B, MCMURTREY J E. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 2000, 74(2): 229-239. doi: 10.1016/S0034-4257(00)00113-9
[18]	JAY S, GORRETTA N, MOREL J, MAUPAS F, BENDOULA R, RABATEL G, DUTARTRE D, COMAR A, BARET F. Estimating leaf chlorophyll content in sugar beet canopies using millimeter to centimeter-scale reflectance imagery. Remote Sensing of Environment, 2017, 198: 173-186. doi: 10.1016/j.rse.2017.06.008
[19]	DASH J, CURRAN P J. The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing, 2004, 25(23): 5403-5413. doi: 10.1080/0143116042000274015
[20]	FITZGERALD G J, RODRIGUEZ D, CHRISTENSEN L K, BELFORD R, SADRAS V O, CLARKE T R. Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environments. Precision Agriculture, 2006, 7(4): 233-248. doi: 10.1007/s11119-006-9011-z
[21]	ROUSE J W J, HAAS R H, SCHELL J A, DEERING D W. Monitoring vegetation systems in the great Plains with ERTS. National Aeronautics and Space Administration Special Publication, 1974, 351(1): 309.
[22]	XU F X, GAO Z Q, JIANG X P, SHANG W T, NING J C, SONG D B, AI J Q. A UAV and S2A data-based estimation of the initial biomass of green algae in the South Yellow Sea. Marine Pollution Bulletin, 2018, 128: 408-414. doi: S0025-326X(18)30073-0 pmid: 29571390
[23]	PEÑUELAS J, GAMON J A, FREDEEN A L, MERINO J, FIELD C B. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sensing of Environment, 1994, 48(2): 135-146. doi: 10.1016/0034-4257(94)90136-8
[24]	ELSAYED S, RISCHBECK P, SCHMIDHALTER U. Comparing the performance of active and passive reflectance sensors to assess the normalized relative canopy temperature and grain yield of drought- stressed barley cultivars. Field Crops Research, 2015, 177: 148-160. doi: 10.1016/j.fcr.2015.03.010
[25]	RONDEAUX G, STEVEN M, BARET F. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 1996, 55(2): 95-107. doi: 10.1016/0034-4257(95)00186-7
[26]	PENUELAS J, BARET F, FILELLA I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 1995, 31(2): 221-230.
[27]	JORDAN C F. Derivation of leaf‐area index from quality of light on the forest floor. Ecology, 1969, 50(4): 663-666. doi: 10.2307/1936256
[28]	BROGE N, LEBLANC E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 2001, 76(2): 156-172. doi: 10.1016/S0034-4257(00)00197-8
[29]	李龙, 毛新国, 王景一, 昌小平, 柳玉平, 景蕊莲. 小麦种质资源抗旱性鉴定评价. 作物学报, 2018, 44(7): 988-999. doi: 10.3724/SP.J.1006.2018.00988
	LI L, MAO X G, WANG J Y, CHANG X P, LIU Y P, JING R L. Drought tolerance evaluation of wheat germplasm resources. Acta Agronomica Sinica, 2018, 44(7): 988-999. (in Chinese) doi: 10.3724/SP.J.1006.2018.00988
[30]	胡芬, 赵聚宝, 姜雁北, 梅旭荣, 钟兆站. 小麦灌浆期土壤干旱对子粒发育影响的研究. 中国农业气象, 1993, 14(4): 8-11.
	HU F, ZHAO J B, JIANG Y B, MEI X R, ZHONG Z Z. Study on the effects of soil drought on grain development during wheat filling stage. Chinese Journal of Agrometeorology, 1993, 14(4): 8-11. (in Chinese)
[31]	YANG J C, ZHANG J H. Grain filling of cereals under soil drying. The New Phytologist, 2006, 169(2): 223-236. doi: 10.1111/nph.2006.169.issue-2
[32]	FAROOQ M, HUSSAIN M, SIDDIQUE K H M. Drought stress in wheat during flowering and grain-filling periods. Critical Reviews in Plant Sciences, 2014, 33(4): 331-349. doi: 10.1080/07352689.2014.875291
[33]	吕金印, 山仑, 高俊凤, 覃凤云, 杨淑慎. 干旱对小麦灌浆期旗叶光合等生理特性的影响. 干旱地区农业研究, 2003, 21(2): 77-81.
	LÜ J Y, SHAN L, GAO J F, QIN F Y, YANG S S. Effect of drought stress on photosynthesis and some other physiological characteristics in flag leaf during grain filling of wheat. Agricultural Research in the Arid Areas, 2003, 21(2): 77-81. (in Chinese)
[34]	王淑荣, 费德友, 唐宗祥. 干旱胁迫对四川小麦品种产量及农艺性状的影响. 农业与技术, 2020, 40(13): 14-17.
	WANG S R, FEI D Y, TANG Z X. Effects of drought stress on yield and agronomic traits of wheat varieties in Sichuan. Agriculture and Technology, 2020, 40(13): 14-17. (in Chinese)
[35]	CANDIAGO S, REMONDINO F, DE GIGLIO M, DUBBINI M, GATTELLI M. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing, 2015, 7(4): 4026-4047. doi: 10.3390/rs70404026
[36]	SHAO G M, HAN W T, ZHANG H H, LIU S Y, WANG Y, ZHANG L Y, CUI X. Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices. Agricultural Water Management, 2021, 252: 106906. doi: 10.1016/j.agwat.2021.106906
[37]	李映雪, 朱艳, 田永超, 姚霞, 秦晓东, 曹卫星. 小麦叶片氮含量与冠层反射光谱指数的定量关系. 作物学报, 2006, 32(3): 358-362.
	LI Y X, ZHU Y, TIAN Y C, YAO X, QIN X D, CAO W X. Quantitative relationship between leaf nitrogen concentration and canopy reflectance spectra. Acta Agronomica Sinica, 2006, 32(3): 358-362. (in Chinese)
[38]	李升东, 王法宏, 司纪升, 孔令安, 冯波, 张宾. 不同基因型小麦NDVI值与产量的关系. 干旱地区农业研究, 2008, 26(6): 47-50.
	LI S D, WANG F H, SI J S, KONG L A, FENG B, ZHANG B. The relationship between NDVI values and yield of different genotype wheat varieties. Agricultural Research in the Arid Areas, 2008, 26(6): 47-50. (in Chinese)
[39]	李春情, 刘翔宇, 闫鹏, 周六千, 卢霖, 董志强, 徐江. 不同玉米品种抗旱性的生理鉴定与综合评价. 作物杂志, 2023. https://link.cnki.net/urlid/11.1808.S.20230926.1429.006.
	LI C Q, LIU X Y, YAN P, ZHOU L Q, LU L, DONG Z Q, XU J. Physiological identification and comprehensive evaluation of drought resistance of different maize varieties. Crop Journal, 2023. https://link.cnki.net/urlid/11.1808.S.20230926.1429.006. (in Chinese)
[40]	王倩, 董孔军, 薛亚鹏, 刘少雄, 王若楠, 杨佳琪, 陆平, 王瑞云, 杨天育, 刘敏轩. 糜子核心种质成熟期抗旱性鉴定评价与抗旱种质筛选. 中国农业科学, 2023, 56(21): 4163-4174. doi:10.3864/j.issn.0578-1752.2023.21.003.
	WANG Q, DONG K J, XUE Y P, LIU S X, WANG R N, YANG J Q, LU P, WANG R Y, YANG T Y, LIU M X. Drought resistance identification and evaluation of mature stage in foxtail millet core germplasm and selection of drought-resistant germplasm. Scientia Agricultura Sinica, 2023, 56(21): 4163-4174. doi:10.3864/j.issn.0578-1752.2023.21.003. (in Chinese)
[41]	张青显. 野生遗传基因种质资源在小麦育种中的应用及遗传特征. 农业科技通讯, 2022(7): 183-184, 188.
	ZHANG Q X. Application and genetic characteristics of wild genetic gene germplasm resources in wheat breeding. Bulletin of Agricultural Science and Technology, 2022(7): 183-184, 188. (in Chinese)
[42]	EVENSON R E, GOLLIN D. Assessing the impact of the green revolution, 1960 to 2000. Science, 2003, 300(5620): 758-762. doi: 10.1126/science.1078710 pmid: 12730592
[43]	DRECCER M F, BORGOGNONE M G, OGBONNAYA F C, TRETHOWAN R M, WINTER B. CIMMYT-selected derived synthetic bread wheats for rainfed environments: Yield evaluation in Mexico and Australia. Field Crops Research, 2007, 100(2/3): 218-228. doi: 10.1016/j.fcr.2006.07.005
[44]	RATTEY A, SHORTER R. Evaluation of CIMMYT conventional and synthetic spring wheat germplasm in rainfed sub-tropical environments: I. Grain yield. Field Crops Research, 2010, 118(3): 273-281. doi: 10.1016/j.fcr.2010.06.006
[45]	张泽全, 舒长生, 赖运平, 董雪芳, 李俊, 魏会廷, 彭正松, 杨武云. 人工合成小麦幼苗耐低温特性鉴定. 中国农学通报, 2010, 26(12): 135-138.
	ZHANG Z Q, SHU C S, LAI Y P, DONG X F, LI J, WEI H T, PENG Z S, YANG W Y. Evaluation of low-temperature tolerant germplasm resource on seedling traits of synthetic hexaploid wheats. Chinese Agricultural Science Bulletin, 2010, 26(12): 135-138. (in Chinese) doi: 10.11924/j.issn.1000-6850.2010-0049
[46]	王秋云, 冯雯杰, 张巧玲, 赵凯, 张宪军, 刘淑珍, 鲍印广. 56份人工合成六倍体小麦的遗传多样性分析. 麦类作物学报, 2021, 41(8): 960-968.
	WANG Q Y, FENG W J, ZHANG Q L, ZHAO K, ZHANG X J, LIU S Z, BAO Y G. Genetic diversity analysis of 56 synthetic hexaploid wheats. Journal of Triticeae Crops, 2021, 41(8): 960-968. (in Chinese)

光谱指数Spectral index	公式Formula^a	参考文献Reference
蓝色归一化植被指数BNDVI	(R_NIR－R_Blue)/(R_NIR＋R_Blue)	[12]
绿边叶绿素指数CI_green	R_NIR/R_Green－1	[13]
红边叶绿素指数CI_re	R_NIR/R_Re－1	[13]
差值植被指数DVI	R_NIR－R_Red	[14]
蓝-绿归一化植被指数GBNDVI	[R_NIR－(R_Green+R_Blue)]/[R_NIR+(R_Green+R_Blue)]	[12]
绿色色度坐标GCC	R_Green/(R_Red＋R_Blue＋R_Green)	[15]
绿色归一化植被指数GNDVI	(R_NIR－R_Green)/(R_NIR＋R_Green)	[16]
修正叶绿素调节植被指数MCARI	[(R_Re－R_red)－0.2×(R_Re－R_Green)](R_Re/R_Red)	[17]
修正蓝光归一化植被指数mNDblue	(R_Blue－R_Red)/(R_Blue＋R_NIR)	[18]
MERIS陆地叶绿素指数MTCI	(R_NIR－R_Re)/(R_Re－R_Red)	[19]
标准化差分红边指数NDRE	(R_NIR－R_Re)/(R_NIR＋R_Re)	[20]
归一化植被指数NDVI	(R_NIR－R_Red)/(R_NIR＋R_Red)	[21]
归一化绿蓝差异指数NGBDI	(R_Green－R_Blue)/(R_Green＋R_Blue)	[22]
归一化叶绿素比值指数NPCI	(R_Red－R_Blue)/(R_Red＋R_Blue)	[23]
归一化相对冠层温度NRCT	(CT_i－CT_min)/(CT_max－CT_min)	[24]
优化土壤调整植被指数OSAVI	(R_NIR－R_Red)/(R_NIR＋R_Red＋0.16)	[25]
结构不敏感色素指数SIPI	(R_NIR－R_Blue)/(R_NIR－R_Red)	[26]
简单比值植被指数SR	R_NIR/R_Red	[27]
三角形植被指数TVI	0.5×[120×(R_NIR－R_Green)－200×(R_Red－R_Green)]	[28]

人工合成小麦 Synthetic wheat	2022			2023
人工合成小麦 Synthetic wheat	对照WW	干旱DS	差值Difference	对照WW	干旱DS	差值Difference
SW004	5875.5	6346.5	-471.0	7420.5	7378.5	42.0
SW009	6148.5	6258.0	-109.5	6961.5	7066.5	-105.0
SW019	6342.0	6280.5	61.5	7587.0	7378.5	208.5
SW034	6540.0	6946.5	-406.5	8754.0	6336.0	2418.0
SW037	5512.5	6663.0	-1151.5	8463.0	5586.0	2877.0
SW051	6687.0	6780.0	-93.0	8587.5	6177.0	2410.5

性状 Trait	主成分Principle factor
性状 Trait	PC1	PC2	PC3
BNDVI-DC	0.84	0.31	0.4
CI_green-DC	0.87	0.4	0.11
CI_re-DC	0.94	0.23	-0.17
DVI-DC	0.78	-0.45	0.14
GBNDVI-DC	0.22	0.28	0.6
GCC-DC	0.88	-0.12	-0.22
GNDVI-DC	0.86	0.36	0.25
MCARI-DC	0.79	-0.51	0.15
mNDblue-DC	-0.73	-0.16	0.64
MTCI-DC	0.37	0.83	-0.25
NDRE-DC	0.95	0.19	-0.1
NDVI-DC	0.95	-0.14	0.02
NGBDI-DC	0.68	0.19	0.47
NPCI-DC	-0.58	0.06	0.61
NRCT-DC	0.01	0.19	-0.15
OSAVI-DC	0.95	-0.26	0.04
SIPI-DC	-0.83	0.35	0.06
SR-DC	0.82	0.02	-0.06
TVI-DC	0.81	-0.49	0.1
特征值Eigen value	11.31	2.28	1.81
贡献率Contribution rate (%)	59.55	12.00	9.55
累积贡献率Cumulative contribution (%)	59.55	71.55	81.10

基于无人机多源影像数据的灌浆期人工合成小麦抗旱性评价

Drought Resistance Evaluation of Synthetic Wheat at Grain Filling Using UAV-Based Multi-Source Imagery Data

RichHTML

PDF

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 46

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	张余周, 王一钊, 高茹茜, 刘逸凡. 小麦根系构型及抗旱性研究进展[J]. 中国农业科学, 2024, 57(9): 1633-1645.
[2]	周全, 路秋梅, 赵张晨, 武宸冉, 符笑歌, 赵玉娇, 韩勇, 蔺怀龙, 陈微林, 牟丽明, 李兴茂, 王长海, 胡银岗, 陈亮. 244份春小麦苗期抗旱性的鉴定[J]. 中国农业科学, 2024, 57(9): 1646-1657.
[3]	刘泽厚, 王琴, 叶美金, 万洪深, 杨宁, 杨漫宇, 杨武云, 李俊. 人工合成小麦和地方品种穗发芽抗性育种利用效率[J]. 中国农业科学, 2024, 57(7): 1255-1266.
[4]	倪书辉, 史东梅, 盘礼东, 叶青, 伍俊豪. 紫色土坡耕地耕层持水抗旱性能及生产力对侵蚀程度的响应[J]. 中国农业科学, 2024, 57(7): 1350-1362.
[5]	高晨曦, 郝陆洋, 胡悦, 李永祥, 张登峰, 李春辉, 宋燕春, 石云素, 王天宇, 黎裕, 刘旭洋. 干旱条件下玉米转座子插入关联的表观调控分析[J]. 中国农业科学, 2024, 57(6): 1034-1048.
[6]	石浩磊, 曹红霞, 张伟杰, 朱珊, 何子建, 张泽. 基于无人机多光谱的棉花多生育期叶面积指数反演[J]. 中国农业科学, 2024, 57(1): 80-95.
[7]	罗正英, 胡鑫, 刘新龙, 吴才文, 吴转娣, 刘家勇, 曾千春. 外源海藻糖提高甘蔗幼苗抗旱能力并促进植株生长[J]. 中国农业科学, 2023, 56(21): 4208-4218.
[8]	李燕, 陶柯宇, 胡悦, 李永祥, 张登峰, 李春辉, 何冠华, 宋燕春, 石云素, 黎裕, 王天宇, 邹华文, 刘旭洋. 玉米ZCN7在调控花期抗旱性中的作用[J]. 中国农业科学, 2023, 56(16): 3051-3061.
[9]	刘自刚, 魏家萍, 崔俊美, 武泽峰, 方彦, 董小云, 郑国强. 我国冬油菜北移的现状、问题与对策[J]. 中国农业科学, 2023, 56(15): 2854-2862.
[10]	冯向前, 殷敏, 王孟佳, 马横宇, 褚光, 刘元辉, 徐春梅, 章秀福, 张运波, 王丹英, 陈松. 南方稻区“早籼晚粳”栽培模式晚季灌浆期气象因子对晚粳稻品质的影响[J]. 中国农业科学, 2023, 56(1): 46-63.
[11]	魏广辉,李执,陈强,李阳,陈诗豪,裴英,周勇,程梦萍,唐豪,王际睿,魏育明,刘登才,陈黎,郑有良,蒲至恩. 人工合成小麦SHW-L1高硒含量KASP分子标记开发及其应用[J]. 中国农业科学, 2020, 53(20): 4103-4112.
[12]	万华方,魏帅,冯宇霞,钱伟. 以六倍体（AⁿAⁿCⁿCⁿC^oC^o）为桥梁创制抗旱新型甘蓝型油菜（AⁿA^rCⁿC^o）[J]. 中国农业科学, 2020, 53(16): 3225-3234.
[13]	马岩川, 刘浩, 陈智芳, 张凯, 余轩, 王景雷, 孙景生. 基于高光谱指数的棉花冠层等效水厚度估算[J]. 中国农业科学, 2019, 52(24): 4470-4483.
[14]	王玉斌, 平俊爱, 牛皓, 楚建强, 杜志宏, 吕鑫, 李慧明, 张福耀. 粒用高粱种质中后期抗旱性鉴定筛选与分类指标评价[J]. 中国农业科学, 2019, 52(22): 4039-4049.
[15]	徐熙，任明见，李鲁华，杨喜翠，徐如宏. 贵紫麦1号籽粒色素形成相关基因的差异表达[J]. 中国农业科学, 2018, 51(2): 203-216.