Estimation of Maize Leaf Area Index and Aboveground Biomass Based on Hyperspectral Data

doi:10.12133/j.smartag.2021.3.1.202102-SA004

Abstract

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

CLC Number:

S127

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.

Figures/Tables 12

Fig. 1

Table 1

Table 2

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Table 3

Fig. 6

Fig. 7

Table 4

Fig. 8

References 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]	LI Shuangwei, ZHU Junqi, EVERS Jochem B., VAN DER WERF Wopke, GUO Yan, LI Baoguo, MA Yuntao. Estimating the Differences of Light Capture Between Rows Based on Functional-Structural Plant Model in Simultaneous Maize-Soybean Strip Intercropping [J]. Smart Agriculture, 2022, 4(1): 97-109.
[2]	ZHANG Sha, LIU Muhua, CHEN Jinyin, ZHAO Jinhui. Rapid Detection of Imazalil Residues in Navel Orange Peel Using Surface-Enhanced Raman Spectroscopy [J]. Smart Agriculture, 2021, 3(4): 42-52.
[3]	YANG Jin, MING Bo, YANG Fei, XU Honggen, LI Lulu, GAO Shang, LIU Chaowei, WANG Keru, LI Shaokun. The Accuracy Differences of Using Unmanned Aerial Vehicle Images Monitoring Maize Plant Height at Different Growth Stages [J]. Smart Agriculture, 2021, 3(3): 129-138.
[4]	ZHANG Xiaoqing, SHAO Song, GUO Xinyu, FAN Jiangchuan. High-Throughput Dynamic Monitoring Method of Field Maize Seedling [J]. Smart Agriculture, 2021, 3(2): 88-99.
[5]	WANG Weidan, SUN Li, PEI Zhiyuan, MA Shangjie, CHEN Yuanyuan, SUN Juanying, DONG Mo. Effect of Growing Season Drought and Flood on Yield of Spring Maize in Three Northeast Provinces of China [J]. Smart Agriculture, 2021, 3(2): 126-137.
[6]	DAI Shengpei, LUO Hongxia, ZHENG Qian, HU Yingying, LI Hailiang, LI Maofen, YU Xuan, CHEN Bangqian. Comparison of Remote Sensing Estimation Models for Leaf Area Index of Rubber Plantation in Hainan Island [J]. Smart Agriculture, 2021, 3(2): 45-54.
[7]	ZHU Chao, WU Fan, LIU Changbin, ZHAO Jianxiang, LIN Lili, TIAN Xueying, MIAO Teng. Tassel Segmentation of Maize Point Cloud Based on Super Voxels Clustering and Local Features [J]. Smart Agriculture, 2021, 3(1): 75-85.
[8]	ZHAO Huan, WANG Jinglu, LIAO Shengjin, ZHANG Ying, LU Xianju, GUO Xinyu, ZHAO Chunjiang. Study on the Micro-Phenotype of Different Types of Maize Kernels Based on Micro-CT [J]. Smart Agriculture, 2021, 3(1): 16-28.
[9]	SHAO Guomin, WANG Yajie, HAN Wenting. Estimation Method of Leaf Area Index for Summer Maize Using UAV-Based Multispectral Remote Sensing [J]. Smart Agriculture, 2020, 2(3): 118-128.
[10]	Lan Yubin, Deng Xiaoling, Zeng Guoliang. Advances in diagnosis of crop diseases, pests and weeds by UAV remote sensing [J]. Smart Agriculture, 2019, 1(2): 1-19.