基于无人机多光谱影像反演不同生育期小麦光合参数分析

doi:10.6048/j.issn.1001-4330.2023.06.002

新疆农业科学 ›› 2023, Vol. 60 ›› Issue (6): 1308-1318.DOI: 10.6048/j.issn.1001-4330.2023.06.002

• 新疆主要粮食作物绿色丰产提质增效专栏 • 上一篇下一篇

基于无人机多光谱影像反演不同生育期小麦光合参数分析

董德誉¹(), 程宇坤¹, 王睿¹, 雷钧杰², 王伟¹, 陈传信², 张永强², 耿洪伟¹()

1.新疆农业大学农学院/优质专用麦类作物工程技术研究中心,乌鲁木齐 830052
2.新疆农业科学院粮食作物研究所/农业农村部荒漠绿洲作物生理生态与耕作重点实验室,乌鲁木齐 830091

收稿日期:2022-10-30 出版日期:2023-06-20 发布日期:2023-06-20
通信作者: 耿洪伟(1978-),男,新疆库尔勒人,教授,博士,硕士生导师,研究方向为小麦遗传育种,(E-mail)hw-geng@163.com
作者简介:董德誉(1995-),男,广西人,硕士研究生,研究方向为小麦遗传育种,(E-mail)deyudong@sina.com
基金资助:
新疆维吾尔自治区重点研发项目(2021B02002);新疆维吾尔自治区重点研发项目(2021B02002-1);新疆维吾尔自治区“天山雪松计划”人选(科技创新领军人才后备人选)(2019XS16);国家现代农业产业技术体系建设专项(CARS-03-94)

Retrieval of wheat photosynthetic parameters at different growth stages based on UAV multispectral images

DONG Deyu¹(), CHENG Yukun¹, WANG Rui¹, LEI Junjie², WANG Wei¹, CHEN Chuanxin², ZHENG Yongqiang², GENG Hongwei¹()

1. High Quality Special Wheat Crops Engineering Technology Research Center/College of Agriculture, Xinjiang Agricultural University, Urumqi 830052, China
2. Key Laboratory of Desert-Oasis Crop Physiology,Ecology and Cultivation,MOARA/ Institute of Food Crops, Xinjiang Academy of Agricultural Sciences,Urumqi 830091,China

Received:2022-10-30 Published:2023-06-20 Online:2023-06-20
Supported by:
Key R & D Program Project of Xinjiang Uygur Autonomous Region(2021B02002);Key R & D Program Project of Xinjiang Uygur Autonomous Region(2021B02002-1);Candidates for the "Tianshan Cedar Plan" of Xinjiang Autonomous Region (Reserve Candidates for Leading Scientific and Technological Innovation Talents)(2019XS16);Earmarked Fund for CARS(CARS-03-94)

摘要/Abstract

摘要：

【目的】研究无人机多光谱影像对冬小麦光合作用实时监测的可行性,分析不同长势差异的冬小麦对估算模型的影响。【方法】选用16个冬小麦品种作为材料,在无氮处理N₀(0 kg N/667m²)和正常施氮处理N₁(15 kg N/667m²)下获得孕穗期、开花期和灌浆期无人机蓝(B)、绿(G)、红(R)、红边(RE)和近红外(NIR)5个波段的光谱遥感影像,结合同时期4种光合参数胞间CO₂浓度(Ci)、气孔导度(Gs)、净光合速率(Pn)和蒸腾速率(Ti),采用梯度增强回归和岭回归方法建立正常施氮处理下开花期和全生育期4个光合参数的估算模型,并用该估算模型估算无氮处理下开花期和全生育期4个光合参数。【结果】梯度增强回归可以较好的预测施氮下开花期净光合速率(Pn),决定系数(R²)为0.82,Ci、Gs和Ti的预测反演精度分别为0.44、0.64和0.48,在无氮处理下,该模型估算精度大于0.5的为Pn、Gs和Ti。【结论】岭回归在估算正常施氮下全生育期的4个光合参数时,R²> 0.5的是Pn、Gs和Ti,而在无氮处理水平下,该估算模型的预测决定系数R²> 0.5的是Gs和Pn。对不同生育期的小麦光合参数的实时监测,可以通过无人机多光谱影像获取的植被指数结合梯度增强回归和岭回归方法来实现。

关键词: 小麦; 光合参数; 无人机; 数据建模

Abstract:

【Objective】 To study the feasibility of real-time monitoring of winter wheat photosynthesis based on UAV (unmanned aerial vehicle) multi-spectral image and the influence of winter wheat with different growth status on the estimation model.【Methods】 In this study, 16 winter wheat varieties were selected as experimental materials, based on the UAV multispectral image data obtained at different growth stages under the nitrogen free level of N₀(0 kg N/667m²) and the normal nitrogen application level of N₁(15 kg N/667m²).【Results】 Combined with the measured data of four photosynthetic parameters (intercellular carbon dioxide (Ci), stomatal conductance (Ti), net photosynthetic rate (Pn) and transpiration rate (Gs) in the same period, the estimation models of four photosynthetic parameters under normal nitrogen application were established by gradient enhanced regression and ridge regression, respectively.Then, the estimation model was used to estimate the four photosynthetic parameters of anthesis and whole growth period under nitrogen free treatment.The results showed: that gradient enhanced regression could better predict the net photosynthetic rate (Pn) at flowering stage under nitrogen application, and the determination coefficient (R²) was 0.82.The prediction accuracy of Ci, Gs and Ti were 0.44, 0.64 and 0.48, respectively.In the absence of nitrogen application, the estimation accuracy of Pn, Gs and Ti was greater than 0.5.In ridge regression, Pn, Gs and Ti with R²>0.5 were the four photosynthetic parameters during the whole growth period under normal N application, while Gs and Pn with R²>0.5 were the prediction determination coefficients under no N treatment.【Conclusion】 The real-time monitoring of wheat photosynthetic parameters at different growth stages can be achieved by combining vegetation index obtained from UAV multi-spectral images with gradient enhanced regression and ridge regression.

Key words: wheat; photosynthetic parameters; UAV; data modeling

中图分类号:

S512.1

董德誉, 程宇坤, 王睿, 雷钧杰, 王伟, 陈传信, 张永强, 耿洪伟. 基于无人机多光谱影像反演不同生育期小麦光合参数分析[J]. 新疆农业科学, 2023, 60(6): 1308-1318.

DONG Deyu, CHENG Yukun, WANG Rui, LEI Junjie, WANG Wei, CHEN Chuanxin, ZHENG Yongqiang, GENG Hongwei. Retrieval of wheat photosynthetic parameters at different growth stages based on UAV multispectral images[J]. Xinjiang Agricultural Sciences, 2023, 60(6): 1308-1318.

图/表 10

表1 研究选用的小麦品种

Tab.1 Wheat cultivars used in this study

编号 Number	品种名称 Variety	编号 Number	品种名称 Variety
1	石新633	9	新冬46号
2	新冬17号	10	新冬48号
3	新冬18号	11	新冬50号
4	新冬22号	12	新冬51号
5	新冬36号	13	新冬52号
6	新冬37号	14	新冬53号
7	新冬38号	15	新粮201
8	新冬41号	16	新冬60号

表2 滴灌小麦不同施氮肥总量及不同时期追氮量

Tab.2 Total nitrogen fertilizer amount and nitrogen application amount at different stages of drip irrigation for wheat (kg/667m2)

施氮时期 N stages	基肥 Basal nutrition	起身期 Rising stage	拔节期 Jointing stage	孕穗期 Booting stage
施肥比例Fertilization ratio(%)	40	10	40	10
施尿素量 Urea application amount(kg/667m²)	13.00	3.26	13.00	3.26

图1 田间试验设计

Fig.1 Field test design

表3 无人机多光谱影像获取时参数

Tab.3 Parameters of uav multispectral image acquisition

参数 Parameter	参数值 Parameter values
飞行高度Altitude	12 m
飞行速度Speed	5.4 km/h
航向重叠率Course overlap ratio	75%
旁向重叠率Lateral overlap rate	75%
光谱类型Spectral types	R、G、B、RE、NIR

表4 植被指数

Tab.4 Vegetation index

植被指数 Vegetation index	计算公式 Formula to calculate
归一化植被指数(NDVI)^[17]	(ρ_NIR-ρ_R)/(ρ_NIR+ρ_R)
绿色归一化植被指数(GNDVI)^[17]	(ρ_NIR-ρ_G)/(ρ_NIR+ρ_G)
归一化绿蓝差指数(NGBDI)^[18]	(ρ_G-ρ_B)/(ρ_G+ρ_B)
归一化绿红差指数(NGRDI)^[18]	(ρ_G-ρ_R)/(ρ_G+ρ_R)
红边重归一化植被指数(RERDVI)^[19]	(ρ_NIR-ρ_RE)/(ρ_NIR+ρ_RE)
土壤调节植被指数(SAVI)^[20]	2.5(ρ_NIR-ρ_R)/(ρ_NIR+ρ_R+0.5)
绿波段优化土壤调节植被指数(GOSAVI)^[20]	1.16[(ρ_NIR-ρ_G)/(ρ_NIR+ρ_G+0.16)]
红边优化土壤调节植被指数(REOSAVI)^[19]	1.16[(ρ_NIR-ρ_R)/(ρ_NIR+ρ_R+0.16)]
优化土壤调节植被指数(OSAVI)^[20]	(ρ_NIR-ρ_R)/(ρ_NIR+ρ_R+0.16)
比值植被指数(RVI)^[21]	ρ_NIR/ρ_R
差值植被指数(DVI)^[21]	ρ_NIR-ρ_R
绿波段比值植被指数(GRVI)^[21]	ρ_NIR/ρ_G
过绿指数(EXG)^[22]	2ρ_G-ρ_R-ρ_B
三角植被指数(TVI)^[23]	0.5[120(ρ_NIR-ρ_G)-200(ρ_R-ρ_G)
叶绿素吸收比值植被指数(CARI)^[20]	(ρ_RE-ρ_R)-0.2(ρ_RE+ρ_R)
绿波段大气阻抗植被指数(VARIgreen)^[21]	(ρ_G-ρ_R)/(ρ_G+ρ_R-ρ_B)
红波段大气阻抗植被指数(VARIred)^[21]	(ρ_RE-1.7ρ_R+0.7ρ_B)/(ρ_RE+2.3ρ_R-1.3ρ_B)
增强型植被指数(EVI)^[23]	2.5(ρ_NIR-ρ_R)/(ρ_NIR+6ρ_R-7.5ρ_B+1)

图2 不同氮素处理下各小麦品种4种光合参数的变化注:N0和N1分别表示无氮处理水平N0(0 kg N/667m2)和正常施氮处理水平N1(15 kg N/667m2)

Fig 2 Changes of four photosynthetic parameters of wheat varieties under different nitrogen treatments Note:N0 and N1 respectively represent nitrogen free treatment levels N0(0 kg N/667m2) and normal nitrogen application level N1(15 kg N/667m2)

表5 开花期植被指数和光合参数的相关系数

Tab.5 Correlation coefficient between vegetation index and photosynthetic parameters at flowering stage

植被指数 Vegetation index	N₁开花期 N₁ Flowering stage				N₀开花期 N₀ Flowering stage
植被指数 Vegetation index	胞间CO₂ 浓度(Ci)	气孔导度 (Gs)	净光合速率 (Pn)	蒸腾速率 (Ti)	胞间CO₂ 浓度(Ci)	气孔导度 (Gs)	净光合速率 (Pn)	蒸腾速率 (Ti)
叶绿素吸收比值指数CARI	-0.01	-0.48	-0.56^*	-0.51^*	-0.05	0.16	0.42	-0.14
差值植被指数DVI	0.06	-0.10	-0.13	-0.09	-0.12	0.09	0.51^*	-0.24
增强型植被指数EVI	0.02	-0.23	-0.32	-0.26	0.24	0.06	0.55^*	0.08
过绿指数EXG	0.43	0.29	0.11	0.16	-0.14	0.42	0.39	-0.14
绿色归一化植被指数GNDVI	-0.41	-0.6 ${7^{}}^{}$	-0.59^*	-0.59^*	0.05	-0.15	0.34	-0.11
绿波段优化土壤调节植被指数GOSAVI	-0.41	-0.68^**	-0.60^*	-0.59^*	0.04	-0.14	0.36	-0.13
绿波段比值植被指数GRVI	-0.33	-0.69^**	-0.64^**	-0.61^*	0.01	-0.07	0.45	-0.05
归一化绿红差异指数NGRGI	0.23	-0.07	-0.23	-0.16	0.14	0.20	0.52^*	0.20
归一化植被指数NDVI	-0.09	-0.41	-0.47	-0.42	0.16	0.01	0.57^*	-0.08
归一化蓝绿差异指数NGBDI	-0.01	-0.52^*	-0.55^*	-0.52^*	-0.33	-0.07	0.28	-0.33
优化土壤调节植被指数OSAVI	0.38	0.85^**	0.79^**	0.80^**	0.04	0.09	-0.27	0.15
绿波段优化土壤调节植被指数REOSAVI	-0.09	-0.39	-0.44	-0.40	0.15	0.02	0.56*	-0.08
红边重归一化植被指数RERDVI	-0.07	0.49	0.59*	0.54*	0.23	-0.14	0.20	-0.10
比值植被指数RVI	0.05	-0.31	-0.41	-0.35	0.10	-0.01	0.63^**	0.09
土壤调节植被指数SAVI	-0.10	-0.40	-0.45	-0.41	0.17	0.01	0.57^*	-0.09
三角植被指数TVI	0.09	-0.10	-0.15	-0.11	-0.09	0.13	0.55^*	-0.23
绿波段大气阻抗植被指数VARIgreen	0.21	-0.04	-0.18	-0.12	0.23	0.16	0.53^*	0.09
红波段大气阻抗植被指数VARIred	-0.03	-0.45	-0.53^*	-0.47	0.11	0.09	0.50	0.06

表5 开花期植被指数和光合参数的相关系数

Tab.5 Correlation coefficient between vegetation index and photosynthetic parameters at flowering stage

植被指数 Vegetation index	N₁开花期 N₁ Flowering stage				N₀开花期 N₀ Flowering stage
植被指数 Vegetation index	胞间CO₂ 浓度(Ci)	气孔导度 (Gs)	净光合速率 (Pn)	蒸腾速率 (Ti)	胞间CO₂ 浓度(Ci)	气孔导度 (Gs)	净光合速率 (Pn)	蒸腾速率 (Ti)
叶绿素吸收比值指数CARI	-0.01	-0.48	-0.56^*	-0.51^*	-0.05	0.16	0.42	-0.14
差值植被指数DVI	0.06	-0.10	-0.13	-0.09	-0.12	0.09	0.51^*	-0.24
增强型植被指数EVI	0.02	-0.23	-0.32	-0.26	0.24	0.06	0.55^*	0.08
过绿指数EXG	0.43	0.29	0.11	0.16	-0.14	0.42	0.39	-0.14
绿色归一化植被指数GNDVI	-0.41	-0.6 ${7^{}}^{}$	-0.59^*	-0.59^*	0.05	-0.15	0.34	-0.11
绿波段优化土壤调节植被指数GOSAVI	-0.41	-0.68^**	-0.60^*	-0.59^*	0.04	-0.14	0.36	-0.13
绿波段比值植被指数GRVI	-0.33	-0.69^**	-0.64^**	-0.61^*	0.01	-0.07	0.45	-0.05
归一化绿红差异指数NGRGI	0.23	-0.07	-0.23	-0.16	0.14	0.20	0.52^*	0.20
归一化植被指数NDVI	-0.09	-0.41	-0.47	-0.42	0.16	0.01	0.57^*	-0.08
归一化蓝绿差异指数NGBDI	-0.01	-0.52^*	-0.55^*	-0.52^*	-0.33	-0.07	0.28	-0.33
优化土壤调节植被指数OSAVI	0.38	0.85^**	0.79^**	0.80^**	0.04	0.09	-0.27	0.15
绿波段优化土壤调节植被指数REOSAVI	-0.09	-0.39	-0.44	-0.40	0.15	0.02	0.56*	-0.08
红边重归一化植被指数RERDVI	-0.07	0.49	0.59*	0.54*	0.23	-0.14	0.20	-0.10
比值植被指数RVI	0.05	-0.31	-0.41	-0.35	0.10	-0.01	0.63^**	0.09
土壤调节植被指数SAVI	-0.10	-0.40	-0.45	-0.41	0.17	0.01	0.57^*	-0.09
三角植被指数TVI	0.09	-0.10	-0.15	-0.11	-0.09	0.13	0.55^*	-0.23
绿波段大气阻抗植被指数VARIgreen	0.21	-0.04	-0.18	-0.12	0.23	0.16	0.53^*	0.09
红波段大气阻抗植被指数VARIred	-0.03	-0.45	-0.53^*	-0.47	0.11	0.09	0.50	0.06

表6 全生育期植被指数和光合参数的相关系数

Tab.6 Correlation coefficient between vegetation index and photosynthetic parameters in whole growth period

植被指数 Vegetation index	N₁全生育期 N₁ The whole growth period				N₀全生育期 N₀ The whole growth period
植被指数 Vegetation index	胞间CO₂ 浓度(Ci)	气孔导度 (Gs)	净光合速率 (Pn)	蒸腾速率 (Ti)	胞间CO₂ 浓度(Ci)	气孔导度 (Gs)	净光合速率 (Pn)	蒸腾速率 (Ti)
叶绿素吸收比值指数CARI	-0.34^**	-0.05	0.21	0.48^**	-0.37^**	-0.32^**	-0.07	0.23^*
差值植被指数DVI	-0.40^**	0.13	0.41^**	0.62^**	-0.40^**	-0.23^*	0.04	0.24^*
增强型植被指数EVI	-0.37^**	0.16	0.40^**	0.52^**	-0.29^**	-0.14	0.09	0.34^**
过绿指数EXG	0.11	-0.49^**	-0.41^**	-0.02	-0.16	-0.48^**	-0.43^**	0.04
绿色归一化植被指数GNDVI	-0.43^**	0.49^**	0.67^**	0.54^**	-0.18	0.24^*	0.41^**	0.24^*
绿波段优化土壤调节植被指数GOSAVI	-0.43^**	0.48^**	0.66^**	0.54^**	-0.19	0.24^*	0.41^**	0.24^*
绿波段比值植被指数GRVI	-0.42^**	0.39^**	0.59^**	0.51^**	-0.22	0.16	0.35^**	0.24^*
归一化绿红差异指数NGRGI	-0.17	-0.18	0.02	0.28^*	-0.26^*	-0.37^**	-0.21	0.24^*
归一化植被指数NDVI	-0.41^**	0.2	0.45^**	0.56^**	-0.31^**	-0.09	0.14	0.31^**
归一化蓝绿差异指数NGBDI	0.28^*	-0.25^*	-0.43^**	-0.55^**	0.19	0.03	-0.17	-0.18
优化土壤调节植被指数OSAVI	-0.41^**	0.2	0.46^**	0.56^**	-0.31^**	-0.09	0.14	0.31^**
绿波段优化土壤调节植被指数REOSAVI	-0.41^**	0.2	0.45^**	0.56^**	-0.30^**	-0.09	0.14	0.31^**
红边重归一化植被指数RERDVI	-0.19	0.54^**	0.58^**	0.30^**	0.08	0.52^**	0.54^**	0.16
比值植被指数RVI	-0.33^**	0.13	0.35^**	0.44^**	-0.26^*	-0.13	0.08	0.31^**
土壤调节植被指数SAVI	-0.41^**	0.2	0.45^**	0.56^**	-0.31^**	-0.09	0.14	0.31^**
三角植被指数TVI	-0.38^**	0.08	0.36^**	0.59^**	-0.39^**	-0.26^*	0.01	0.25^*
绿波段大气阻抗植被指数VARIgreen	-0.17	-0.17	0.03	0.28^*	-0.25^*	-0.36^**	-0.2	0.25^*
红波段大气阻抗植被指数VARIred	-0.34^**	-0.01	0.25^*	0.46^**	-0.35^**	-0.29^**	-0.05	0.30^**

表6 全生育期植被指数和光合参数的相关系数

Tab.6 Correlation coefficient between vegetation index and photosynthetic parameters in whole growth period

植被指数 Vegetation index	N₁全生育期 N₁ The whole growth period				N₀全生育期 N₀ The whole growth period
植被指数 Vegetation index	胞间CO₂ 浓度(Ci)	气孔导度 (Gs)	净光合速率 (Pn)	蒸腾速率 (Ti)	胞间CO₂ 浓度(Ci)	气孔导度 (Gs)	净光合速率 (Pn)	蒸腾速率 (Ti)
叶绿素吸收比值指数CARI	-0.34^**	-0.05	0.21	0.48^**	-0.37^**	-0.32^**	-0.07	0.23^*
差值植被指数DVI	-0.40^**	0.13	0.41^**	0.62^**	-0.40^**	-0.23^*	0.04	0.24^*
增强型植被指数EVI	-0.37^**	0.16	0.40^**	0.52^**	-0.29^**	-0.14	0.09	0.34^**
过绿指数EXG	0.11	-0.49^**	-0.41^**	-0.02	-0.16	-0.48^**	-0.43^**	0.04
绿色归一化植被指数GNDVI	-0.43^**	0.49^**	0.67^**	0.54^**	-0.18	0.24^*	0.41^**	0.24^*
绿波段优化土壤调节植被指数GOSAVI	-0.43^**	0.48^**	0.66^**	0.54^**	-0.19	0.24^*	0.41^**	0.24^*
绿波段比值植被指数GRVI	-0.42^**	0.39^**	0.59^**	0.51^**	-0.22	0.16	0.35^**	0.24^*
归一化绿红差异指数NGRGI	-0.17	-0.18	0.02	0.28^*	-0.26^*	-0.37^**	-0.21	0.24^*
归一化植被指数NDVI	-0.41^**	0.2	0.45^**	0.56^**	-0.31^**	-0.09	0.14	0.31^**
归一化蓝绿差异指数NGBDI	0.28^*	-0.25^*	-0.43^**	-0.55^**	0.19	0.03	-0.17	-0.18
优化土壤调节植被指数OSAVI	-0.41^**	0.2	0.46^**	0.56^**	-0.31^**	-0.09	0.14	0.31^**
绿波段优化土壤调节植被指数REOSAVI	-0.41^**	0.2	0.45^**	0.56^**	-0.30^**	-0.09	0.14	0.31^**
红边重归一化植被指数RERDVI	-0.19	0.54^**	0.58^**	0.30^**	0.08	0.52^**	0.54^**	0.16
比值植被指数RVI	-0.33^**	0.13	0.35^**	0.44^**	-0.26^*	-0.13	0.08	0.31^**
土壤调节植被指数SAVI	-0.41^**	0.2	0.45^**	0.56^**	-0.31^**	-0.09	0.14	0.31^**
三角植被指数TVI	-0.38^**	0.08	0.36^**	0.59^**	-0.39^**	-0.26^*	0.01	0.25^*
绿波段大气阻抗植被指数VARIgreen	-0.17	-0.17	0.03	0.28^*	-0.25^*	-0.36^**	-0.2	0.25^*
红波段大气阻抗植被指数VARIred	-0.34^**	-0.01	0.25^*	0.46^**	-0.35^**	-0.29^**	-0.05	0.30^**

表7 不同光合参数用于数据建模的植被指数

Tab.7 Vegetation indices with different photosynthetic parameters for data modeling

开花期Flowering stage				全生育期The whole growth period
Ci	Gs	Pn	Ti	Ci	Gs	Pn	Ti
EVI	CARI	GRVI	CARI	CARI	EXG	EXG	EVI
EXG	EXG	NDVI	GNDVI	DVI	GNDVI	GNDVI	NDVI
NGBDI	GNDVI	REOSAVI	GOSAVI	NDVI	GOSAVI	GOSAVI	OSAVI
NGRDI	GOSAVI	RVI	NGBDI	TVI	GRVI	GRVI	RVI
VARIgreen	VARIgreen	SAVI	OSAVI	VARIred	RERDVI	RERDVI	SAVI

表8 不同生长势差异的小麦光合参数对模型影响

Tab.8 Effects of wheat photosynthetic parameters with different growth potential differences on the model

光合参数 Photosynthetic parameters	N₁开花期 N₁ Flowering stage			N₀开花期 N₀Flowering stage			N₁全生育期 N₁ The whole growth period			N₀全生育期 N₀The whole growth period
光合参数 Photosynthetic parameters	R²	RMSE	RE	R²	RMSE	RE	R²	RMSE	RE	R²	RMSE	RE
Ci (μmol/(m²·s))	0.44	31.87	0.12	0.39	21.933	0.08	0.22	17.3	0.06	0.47	17.85	0.06
Gs (mol/(m²·s))	0.64	55.31	0.26	0.78	80.74	0.3	0.57	61.51	0.24	0.62	70.94	0.29
Pn (mol/(m²·s))	0.82	5.23	0.26	0.78	4.99	0.2	0.74	3.59	0.18	0.54	4.39	0.23
Ti (μmol/(m²·s))	0.48	0.88	0.14	0.8	0.9	0.13	0.54	0.8	0.16	0.16	1.14	0.28

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基于无人机多光谱影像反演不同生育期小麦光合参数分析

Retrieval of wheat photosynthetic parameters at different growth stages based on UAV multispectral images

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