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

Abstract

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

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.

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References 46

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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

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

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