基于高光谱数据的玉米叶面积指数和生物量评估

doi:10.12133/j.smartag.2021.3.1.202102-SA004

摘要/Abstract

摘要：

利用高光谱技术获取玉米农学参数信息，有助于提升玉米精准管理水平。本研究基于3个种植密度和5份玉米材料的田间试验，获取玉米大喇叭口期的地面ASD高光谱数据与无人机高光谱影像，分析不同种植密度下不同遗传材料的叶面积指数（LAI）和单株地上部生物量，构建基于全波段、敏感波段和植被指数的LAI和单株地上部生物量高光谱估算模型，比较分析两类高光谱数据在玉米表型性状参数上的监测能力。结果表明，野生型玉米材料的冠层光谱反射率在近红外波段随着种植密度的增大而增大；同一种植密度下的野生型玉米材料的光谱反射率在可见光和近红外波段均为最低。在可见光波段550 nm的波峰处，4种转基因材料的光谱反射率比野生型玉米材料的光谱反射率提高4.52%~19.9%，在近红外波段870 nm的波峰处，4种转基因材料的光谱反射率比野生型玉米材料的光谱反射率提高23.64%~57.05%。基于21个高光谱植被指数构建的模型对LAI的估算效果最好，测试集决定系数R²为0.70，均方根误差RMSE为0.92，相对均方根误差rRMSE为15.94%。敏感波段反射率（839~893 nm和1336~1348 nm）对玉米单株地上部生物量估算效果最佳，测试集R²为0.71，RMSE为12.31 g，rRMSE为15.89%。综上，田间非成像高光谱和无人机成像高光谱在玉米LAI及生物量估算方面具有较好的一致性，能够快速有效地提取地块尺度玉米农学参数信息，本研究可为高光谱技术在小区尺度的精准农业管理应用提供参考。

关键词: 高光谱, 玉米, 叶面积指数, 地上部生物量, 偏最小二乘回归, 无人机遥感

Abstract:

In order to assess maize growth status accurately and quickly for improving maize precise management, field experiment was conducted in Gongzhuling research station, Jilin Academy of Agricultural Sciences, Jilin province. Experimental design included 3 planting densities and 5 maize materials. The near-ground hyperspectral data and the unmanned aerial vehicle (UAV) hyperspectral images were obtained when maize were during V11－V12 stage. The application abilities of the hyperspectral data obtained from the two phenotyping platforms were compared and analyzed in the estimation of maize leaf area index (LAI) and aboveground biomass. In this study, 21 commonly used spectral vegetation indices were constructed based on ground hyperspectral data, and then the estimation models of maize LAI and aboveground biomass were established based on ground hyperspectral full-bands, UAV hyperspectral full-bands and vegetation indices and partial least square regression method, respectively. According to the variance estimation of regression coefficients, the important bands of LAI and aboveground biomass were selected, and the partial least square method was also used to establish the estimation model of maize LAI and aboveground biomass based on important bands. The results showed that the canopy spectral reflectance of the same maize material increased with the increase of planting density in the near infrared bands. Among the 5 maize materials under the same planting density, the canopy spectral reflectance of wild type material was the lowest in the visible and near infrared bands. For LAI, the model constructed based on vegetation indices had the best estimation result, with R², RMSE and rRMSE values of 0.70, 0.92 and 15.94%. For aboveground biomass, the model constructed based on the sensitive spectral bands (839－893 nm and 1336－1348 nm) had the best estimation results, with R², RMSE and rRMSE values of 0.71, 12.31 g and 15.89%, which showed that there was information redundancy in hyperspectral bands in the estimation of aboveground biomass, and the estimation accuracy could be improved by reducing the number of spectral bands and selecting sensitive spectral bands. In summary, the UAV hyperspectral images have a good application ability in the estimation of maize LAI and aboveground biomass, and can quickly and effectively extract the parameters information of maize growth. For specific parameters, sensitive spectral bands selected can provide reliable basis for the development and practical application of multi-spectrum in the future. The study can provide a reference for the use of hyperspectral technology in the management of precision agriculture at the community scale.

Key words: hyper-spectrum, maize, leaf area index, aboveground biomass, partial least squares regression, UAV remote sensing

中图分类号:

S127

束美艳, 陈向阳, 王喜庆, 马韫韬. 基于高光谱数据的玉米叶面积指数和生物量评估[J]. 智慧农业(中英文), 2021, 3(1): 29-39.

SHU Meiyan, CHEN Xiangyang, WANG Xiqing, MA Yuntao. Estimation of Maize Leaf Area Index and Aboveground Biomass Based on Hyperspectral Data[J]. Smart Agriculture, 2021, 3(1): 29-39.

图/表 12

图1

表 1

表 2

图2

图 3

图4

图5

表 3

图6

图7

表4

图8

参考文献 40

1	束美艳, 顾晓鹤, 孙林, 等. 倒伏胁迫下的玉米冠层结构特征变化与光谱响应解析[J]. 光谱学与光谱分析, 2019, 39(11): 3553-3559.
	SHU M, GU X, SUN L, et al. Structural characteristics change and spectral response analysis of maize canopy under lodging stress[J]. Spectroscopy and Spectral Analysis, 2019, 39(11): 3553-3559.
2	周龙飞, 张云鹤, 成枢, 等. 不同生育期倒伏胁迫下玉米叶面积指数高光谱响应解析[J]. 遥感技术与应用, 2019, 34(4): 766-774.
	ZHOU L, ZHANG Y, CHENG S, et al. Analysis of hyperspectral response of maize leaf area index under lodging stress under different growth stages[J]. Remote Sensing Technology and Application, 2019, 34(4): 766-774.
3	潘海珠, 陈仲新. 无人机高光谱遥感数据在冬小麦叶面积指数反演中的应用[J]. 中国农业资源与区划, 2018, 9(3): 32-37
	PAN H, CHEN Z. Application of UVA hyperspectral remote sensing in winter wheat leaf area index inversion[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2018, 9(3): 32-37
4	束美艳, 顾晓鹤, 孙林, 等. 基于新型植被指数的冬小麦LAI高光谱反演[J]. 中国农业科学, 2018, 51(18): 3486-3496.
	SHU M, GU X, SUN L, et al. High spectral inversion of winter wheat LAI based on new vegetation index[J]. Scientia Agricultura Sinica, 2018, 51(18): 3486-3496.
5	BENDING J, KANG YU, AASEN H, et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley[J]. International Journal of Applied Earth Observations and Geoinformation, 2015, 39: 79-87.
6	李天驰, 冯海宽, 朱贝贝, 等. 基于无人机高光谱和数码影像数据的冬小麦生物量反演[J]. 现代农业科技, 2020(20): 1-5.
	LI T, FENG H, ZHU B, et al. Winter wheat biomass inversion based on UAV hyperspectral and digital image data[J]. Modern Agricultural Science and Technology, 2020(20): 1-5.
7	BODO M, URS S. Tractor-based quadrilateral spectral reflectance measurements to detect biomass and total aerial nitrogen in winter wheat[J]. Agronomy Journal, 2010, 102(2): 499-506.
8	刘杨, 冯海宽, 孙乾, 等. 基于无人机高光谱分数阶微分的马铃薯地上生物量估算[J]. 农业机械学报, 2020, 51(12): 202-211.
	LIU Y, FENG H, SUN Q, et al. Estimation of potato above-ground biomass based on fractional differential of UAV hyperspectral[J]. Transactions of the CSAM, 2020, 51(12): 202-211.
9	WU C, NIU Z, TANG Q, et al. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation[J]. Agricultural and Forest Meteorology, 2008, 148(8): 1230-1241.
10	CROFT H, J M, ZHANG C. The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures[J]. Ecological Complexity, 2014, 17: 119-130.
11	程雪, 贺炳彦, 黄耀欢, 等. 基于无人机高光谱数据的玉米叶面积指数估算[J]. 遥感技术与应用, 2019, 34(4): 775-784.
	CHENG X, HE B, HUANG Y, et al. Estimation of corn leaf area index based on UAV hyperspectral image[J]. Remote Sensing Technology and Application, 2019, 34(4): 775-784.
12	常潇月, 常庆瑞, 王晓凡, 等. 基于无人机高光谱影像玉米叶绿素含量估算[J]. 干旱地区农业研究, 2019, 37(1): 66-73.
	CHANG X, CHANG Q, WANG X, et al. Estimation of maize leaf chlorophyll contents based on UAV hyperspectral drone image[J]. Agricultural Research in the Arid Areas, 2019, 37(1): 66-73.
13	ZHOU Y, JIANG M. Comparison of inversion method of maize leaf area index based on UAV hyperspectral remote sensing[J]. Multimedia Tools and Applications, 2020(79): 16385-16401.
14	田明璐, 班松涛, 常庆瑞, 等. 基于低空无人机成像光谱仪影像估算棉花叶面积指数[J]. 农业工程学报, 2016, 32(21): 102-108.
	TIAN M, BAN S, CHANG Q, et al. Use of hyperspectral images from UAV-based imaging spectroradiometer to estimate cotton leaf area index[J]. Transactions of the CSAE, 2016, 32(21): 102-108.
15	田明璐, 班松涛, 常庆瑞, 等. 基于无人机成像光谱仪数据的棉花叶绿素含量反演[J]. 农业机械学报, 2016, 47(11): 285-293.
	TIAN M, BAN S, CHANG Q, et al. Estimation of SPAD value of cotton leaf using hyperspectral images from UAV-based imaging spectroradiometer[J]. Transactions of the CSAM, 2016, 47(11): 285-293.
16	陶惠林, 冯海宽, 杨贵军, 等. 基于无人机成像高光谱影像的冬小麦LAI估测[J]. 农业机械学报, 2020, 51(1): 176-187.
	TAO H, FENG H, YANG G, et al. Leaf area index estimation of winter wheat based on UAV imaging hyperspectral imagery[J]. Transactions of the CSAM, 2020, 51(1): 176-187.
17	陶惠林, 徐良骥, 冯海宽, 等. 基于无人机高光谱遥感的冬小麦株高和叶面积指数估算[J]. 农业机械学报, 2020, 51(12): 193-201.
	TAO H, XU L, FENG H, et al. Estimation of plant height and leaf area index of winter wheat based on UAV hyperspectral remote sensing[J]. Transactions of the CSAM, 2020, 51(12): 193-201.
18	陶惠林, 冯海宽, 徐良骥, 等. 基于无人机高光谱遥感数据的冬小麦生物量估算[J]. 江苏农业学报, 2020, 36(5): 1154-1162.
	TAO H, FENG H, XU L, et al. Winter wheat biomass estimation based on hyperspectral remote sensing data of unmanned aerial vehicle(UAV)[J]. Jiangsu Journal of Agricultural Sciences, 2020, 36(5): 1154-1162.
19	陶惠林, 徐良骥, 冯海宽, 等. 基于无人机高光谱长势指标的冬小麦长势监测[J]. 农业机械学报, 2020, 51(2): 180-191.
	TAO H, XU L, FENG H, et al. Monitoring of winter wheat growth based on UAV hyperspectral growth index[J]. Transactions of the CSAM, 2020, 51(2): 180-191.
20	陶惠林, 徐良骥, 冯海宽, 等. 基于无人机高光谱遥感数据的冬小麦产量估算[J]. 农业机械学报, 2020, 51(7): 146-155.
	TAO H, XU L, FENG H, et al. Winter wheat yield estimation based on UAV hyperspectral remote sensing data[J]. Transactions of the CSAM, 2020, 51(7): 146-155.
21	陶惠林, 冯海宽, 杨贵军, 等. 基于无人机数码影像和高光谱数据的冬小麦产量估算对比[J]. 农业工程学报, 2019, 35(23): 111-118.
	TAO H, FENG H, YANG G, et al. Comparison of winter wheat yields estimated with UAV digital image and hyperspectral data[J]. Transactions of the CASE, 2019, 35(23): 111-118.
22	LI Z, LI Z, FAIRBAIRN D, et al. Multi-LUTs method for canopy nitrogen density estimation in winter wheat by field and UAV hyperspectral[J]. Computers and Electronics in Agriculture, 2019, 162: 174-182.
23	秦占飞, 常庆瑞, 谢宝妮, 等. 基于无人机高光谱影像的引黄灌区水稻叶片全氮含量估测[J]. 农业工程学报, 2016, 32(23): 77-85.
	QIN Z, CHANG Q, XIE B, et al. Rice leaf nitrogen content estimation based on hyperspectral imagery of UAV in Yellow River diversion irrigation district[J]. Transactions of the CSAE, 2016, 32(23): 77-85.
24	DRISS H, JOHN R. MILLER N,et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture[J]. Remote Sensing of Environment, 2002, 81(2): 416-426.
25	ZARCO-TFJADA P, MILLER J, MORALES A, et al. Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops[J]. Remote Sensing of Environment, 2004, 90(4): 463-476.
26	LE G, MAIRE C, FRANCOIS E, et al. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements[J]. Remote Sensing of Environment, 2003, 89(1): 1-28.
27	ANATOLY A, GITRLSION Y, KAUFMAN M, et al. Use of a green channel in remote sensing of global vegetation from EOS-MODIS[J]. Remote Sensing of Environment, 1996, 58(3): 289-298.
28	CWU C, NIU Z, TANG Q, et al. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation[J]. Agricultural and Forest Meteorology, 2008, 148(8): 1230-1241.
29	SIMS D, GANMON J. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages[J]. Remote Sensing of Environment, 2002, 81(2): 337-354.
30	DASH J, CURRAN P. The MERIS terrestrial chlorophyll index[J]. International Journal of Remote Sensing, 2004, 25(23): 5403-5413.
31	HANSEN P, SCHJOERRING J. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression[J]. Remote Sensing of Environment, 2003, 86(4): 542-553.
32	CHU X, GUO Y, HE J, et al. Comparison of different hyperspectral vegetation indices for estimating canopy leaf nitrogen accumulation in rice[J]. Agronomy Journal, 2014, 106(5): 1911-1920.
33	FAVA F, COLOMBO R, BOCCHI S, et al. Identification of hyperspectral vegetation indices for Mediterranean pasture characterization[J]. International Journal of Applied Earth Observation and Geoinformation, 2009, 11(4): 233-243.
34	BISUN D. Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus leaves[J]. Remote Sensing of Environment, 1998, 66(2): 111-121.
35	STEDDOM K, HEIDEL G, JONES D, et al. Remote detection of rhizomania in sugar beets[J]. Phytopathology, 2003, 93(6): 720-726.
36	GITELSON A, MERZLYAK M N. Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves[J]. Journal of Photochemistry and Photobiology B Biology, 1994, 22(3): 247-252.
37	MAIRAJ D, JIN M, SADEED H, et al. Estimation of dynamic canopy variables using hyperspectral derived vegetation indices under varying N rates at diverse phenological stages of rice[J]. Frontiers in Plant Science, 2019, 9: ID 1883.
38	薛利红, 曹卫星, 罗卫红, 等.小麦叶片氮素状况与光谱特性的相关性研究[J]. 植物生态学报, 2004, 28(2): 172-177.
	XUE L, CAO W, LUO W, et al. Correlation between leaf nitrogen status and canopy spectral characteristics in wheat[J]. Acta Phytoecologica Sinica, 2004, 28(2): 172-177.
39	QUENOUILLE M. Approximate tests of correlation in time-series[J]. Journal of the Royal Statistical Society, Series B (Methodological), 1949, 11(1): 68-84.
40	MENG B, SKIDMORE A K, SCHLERF M, et al. Predicting foliar biochemistry of tea (Camellia sinensis) using reflectance spectra measured at powder, leaf and canopy levels[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 78: 148-156.

植被指数	计算公式	参考文献
DDI	DDI= (R₇₅₀-R₇₂₀) - (R₇₀₀-R₆₇₀) (3)	[26]
GNDVI	GNDVI = (R₇₅₀-R₅₅₀)/(R₇₅₀+R₅₅₀) (4)	[27]
MCARI	MCARI = [(R₇₅₀-R₇₀₅)-0.2×(R₇₅₀-R₅₅₀)]×(R₇₅₀/R₇₀₅) (5)	[28]
MND705	MND705 = (R₇₅₀-R₇₀₅)/(R₇₅₀+R₇₀₅-2×R₄₄₅) (6)	[29]
MSR	MSR = (R₇₅₀/R₇₀₅-1)/sqrt(R₇₅₀/R₇₀₅+1) (7)	[28]
MSR705	MSR705 = (R₇₅₀-R₄₄₅)/(R₇₀₅-R₄₄₅) (8)	[29]
MTCI	MTCI = (R₇₅₀-R₇₁₀)/(R₇₁₀-R₆₈₀) (9)	[30]
NDI [759,732]	NDI [759,732] = R₇₅₉-R₇₃₂ (10)	[31]
NDI [860,560]	NDI [860,560] = R₈₆₀-R₅₆₀ (11)	[32]
NDI [860,720]	NDI [860,720] = R₈₆₀-R₇₂₀ (12)	[33]
NDVI705	NDVI705 = (R₇₅₀-R₇₀₅)/(R₇₅₀+R₇₀₅) (13)	[29]
NDVI780	NDVI780 = (R₇₈₀-R₇₁₀)/(R₇₈₀-R₆₈₀) (14)	[34]
NDVI850	NDVI850 = (R₈₅₀-R₇₁₀)/(R₈₅₀-R₆₈₀) (15)	[34]
NDVI760	NDVI760 = (R₇₆₀-R₇₀₈)/(R₇₆₀+R₇₀₈) (16)	[35]
NDVI780	NDVI780 = (R₇₈₀-R₅₅₀)/(R₇₈₀+R₅₅₀) (17)	[27]
NDVI800	NDVI800 = (R₈₀₀-R₇₀₀)/(R₈₀₀+R₇₀₀) (18)	[36]
SRI [750,705]	SRI [750,705] = R₇₅₀/R₇₀₅ (19)	[36]
SRI [768,750]	SRI [768,750] = R₇₆₈/R₇₅₀ (20)	[37]
SRI [777,759]	SRI [777,759] = R₇₇₇/R₇₅₉ (21)	[38]
SRI [810,560]	SRI [810,560] = R₈₁₀/R₅₆₀ (22)	[38]
SRI [810,660]	SRI [810,660] = R₈₁₀/R₆₆₀ (23)	[32]

估算指标	样本数	最小值	最大值	平均值	变异系数
LAI	45	3.04	8.44	5.76	0.26
生物量/g	45	53.06	124.20	77.45	0.18

变量	R²±Std	RMSE±Std	rRMSE±Std/%
无人机高光谱	0.65±0.04	0.98±0.11	17.00±1.94
近地面高光谱	0.64±0.03	1.04±0.12	18.12±2.09
光谱植被指数	0.70±0.02	0.92±0.13	15.94±2.27
敏感光谱波段	0.44±0.04	1.16±0.12	20.17±2.09

变量	R²(test)±Std	RMSE±Std/g	rRMSE±Std/%
无人机高光谱	0.56±0.04	14.66±5.32	18.92±6.87
近地面高光谱	0.51±0.05	13.59±1.57	17.54±2.03
光谱植被指数	0.47±0.02	13.05±4.03	16.85±5.56
敏感光谱波段	0.71±0.02	12.31±3.98	15.89±5.14

[1]	李双伟, 朱俊奇, EVERS Jochem B., VAN DER WERF Wopke, 郭焱, 李保国, 马韫韬. 基于植物功能-结构模型的玉米-大豆条带间作光截获行间差异研究[J]. 智慧农业(中英文), 2022, 4(1): 97-109.
[2]	张莎, 刘木华, 陈金印, 赵进辉. 采用表面增强拉曼光谱技术快速检测脐橙果皮中抑霉唑残留[J]. 智慧农业(中英文), 2021, 3(4): 42-52.
[3]	郭志明, 王郡艺, 宋烨, 邹小波, 蔡健荣. 果蔬品质劣变传感检测与监测技术研究进展[J]. 智慧农业(中英文), 2021, 3(4): 14-28.
[4]	杨进, 明博, 杨飞, 许红根, 李璐璐, 高尚, 刘朝巍, 王克如, 李少昆. 利用无人机影像监测不同生育阶段玉米群体株高的精度差异分析[J]. 智慧农业(中英文), 2021, 3(3): 129-138.
[5]	张小青, 邵松, 郭新宇, 樊江川. 田间玉米苗期高通量动态监测方法[J]. 智慧农业（中英文）, 2021, 3(2): 88-99.
[6]	杨菲菲, 刘升平, 诸叶平, 李世娟. 基于高光谱遥感的冬小麦涝渍胁迫识别及程度判别分析[J]. 智慧农业（中英文）, 2021, 3(2): 35-44.
[7]	王蔚丹, 孙丽, 裴志远, 马尚杰, 陈媛媛, 孙娟英, 董沫. 东北三省地区生长季旱涝对春玉米产量的影响[J]. 智慧农业（中英文）, 2021, 3(2): 126-137.
[8]	戴声佩, 罗红霞, 郑倩, 胡盈盈, 李海亮, 李茂芬, 禹萱, 陈帮乾. 海南岛橡胶林叶面积指数遥感估算模型比较研究[J]. 智慧农业（中英文）, 2021, 3(2): 45-54.
[9]	朱超, 吴凡, 刘长斌, 赵健翔, 林丽丽, 田雪莹, 苗腾. 基于超体素聚类和局部特征的玉米植株点云雄穗分割[J]. 智慧农业（中英文）, 2021, 3(1): 75-85.
[10]	赵欢, 王璟璐, 廖生进, 张颖, 卢宪菊, 郭新宇, 赵春江. 基于Micro-CT的玉米籽粒显微表型特征研究[J]. 智慧农业(中英文), 2021, 3(1): 16-28.
[11]	FLORESPaulo, MATHEWJithin, JAHANNusrat, STENGERJohn. 利用图像和机器学习检测大豆作物幼苗期玉米杂苗[J]. 智慧农业（中英文）, 2020, 2(3): 61-74.
[12]	刘恬琳, 朱西存, 白雪源, 彭玉凤, 李美炫, 田中宇, 姜远茂, 杨贵军. 土壤有机质含量高光谱估测模型构建及精度对比[J]. 智慧农业（中英文）, 2020, 2(3): 129-138.
[13]	邵国敏, 王亚杰, 韩文霆. 基于无人机多光谱遥感的夏玉米叶面积指数估算方法[J]. 智慧农业（中英文）, 2020, 2(3): 118-128.
[14]	杨成海. 机载遥感系统在精准农业中的应用[J]. 智慧农业（中英文）, 2020, 2(1): 1-22.
[15]	吴刚, 彭要奇, 周广奇, 李晓龙, 郑永军, 严海军. 基于多光谱成像和卷积神经网络的玉米作物营养状况识别方法研究[J]. 智慧农业（中英文）, 2020, 2(1): 111-120.