基于无人机多光谱遥感的玉米LAI估算研究

doi:10.19788/j.issn.2096-6369.210403

Abstract

Abstract:

Remote sensing technology can be used to estimate the leaf area index (LAI) value of crops rapidly and harmlessly. The purpose of this study is to research the accuracy, reliability, and adaptability of the LAI using unmanned aerial vehicle (UAV) multispectral remote sensing. During a summer maize-fertilizer cross test, the LAI and multispectral images captured by a six-rotor UAV with a MicaSense RedEdge-M camera (which has five high-resolution channels: blue, green, red, red edge, and near infrared) were collected at the jointing, tasseling, and maturity stages of the maize. The normalized differential vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), enhanced vegetation index (EVI), and normalized differential red edge index (NDRE) were calculated at each stage. The correlation between these metrics and the LAI were analyzed and their values were established based on the multispectral images at different growth stages. Then, an LAI model for each growth stage was established. After the accuracy of these models was tested using independent data, a maize LAI estimation map was made by processing each pixel in the maize multispectral image using these models. The results indicate the following: 1) There is a high correlation between the LAI and the NDVI, OSAVI, EVI, and NDRE values at the jointing, tasseling, and maturity stages. 2) LAI estimation models were established based on OSAVI, NDRE, and NDRE for the jointing, tasseling, and maturity stages, respectively. They had decision coefficient values (R²) of 0.549, 0.753, and 0.733, respectively, and the R² of the verification models were 0.907, 0.932, and 0.926, respectively. The predicted and measured values at different growth stages had relative error values of 8.57, 8.37, and 9.24 and root-mean-squared error values of 0.104, 0.087, and 0.091, respectively. 3) The spatial distribution of the LAI at field scale was mapped by the LAI estimation models at each growth stage, yielding R² values of 0.883, 0.931, and 0.867 and relative error values of 9.17, 8.86, and 9.32, respectively. Therefore, the LAI map reflected the real-world spatial distribution pattern of the LAI in the maize fields well. The established agricultural UAV remote sensing monitoring system provides accuracy, reliability, and adaptability for precision agriculture applications as well as the corresponding retrieval models for studying the feasibility of estimating the LAI during different growth stages.

Key words: unmanned aerial vehicle (UAV), multi-spectral, remote sensing, maize, leaf area index (LAI)

CLC Number:

Jia He, Laigang Wang, Yan Guo, Yan Zhang, Xiuzhong Yang, Ting Liu, Hongli Zhang. Estimating the Leaf Area Index of Maize based on Unmanned Aerial Vehicle Multispectral Remote Sensing[J].Journal of Agricultural Big Data, 2021, 3(4): 20-28.

Figures/Tables 7

Table 1

Table 2

Table 3

Table 4

Fig.2

Fig.3

Fig. 4

References 36

1	Jefferies R A, Mackerron D K L. Responses of potato genotypes to drought. II. Leaf area index, growth and yield[J]. Annals of Applied Biology, 1993, 122(1): 105-112.
2	李俐,许连香,王鹏新,等.基于叶面积指数的河北中部平原夏玉米单产预测研究[J].农业机械学报,2020,51(06):198-208.
	Li L, Xu L X, Wang P X, et al. Summer maize yield forecasting based on leaf area index[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(10):230-239.
3	Fu Y, Yang G, Wang J, et al. Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements[J]. Computers and Electronics in Agriculture. 2014, 100: 51-59.
4	Verger A, Vigneau N, Cheron C, et al. Green area index from an unmanned aerial system over wheat and rapeseed crops [J]. Remote Sensing Environment, 2014, 152: 654-664.
5	Liang L, Di L, Zhang L, et al. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method [J]. Remote Sensing Environment, 2015, 165: 123-134.
6	Walthall C, Dulaney W, Anderson M, et al. A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM imagery[J]. Remote Sensing of Environment, 2004, 92(4): 465-474.
7	Propastin P A. Spatial non-stationarity and scale-dependency of prediction accuracy in the remote estimation of LAI over a tropical rainforest in Sulawesi, Indonesia[J]. Remote Sensing of Environment, 2009, 113(10):2234-2242.
8	Frampton W J, Dash J, Warmouth G, et al. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 82: 83-92.
9	苏伟, 侯宁, 李琪, 等. 基于Sentinel-2 遥感影像的玉米冠层叶面积指数反演[J]. 农业机械学报, 2018, 49(1): 151-156.
	Su W, Hou N, Li Q, et al. Retrieving leaf area index of corn canopy based on sentinel-2 remote sensing image[J]. Transaction of the Chinese Society for Agricultural Machinery, 2018, 49(1): 151-156.
10	易秋香. 基于Sentinel-2 多光谱数据的棉花叶面积指数估算[J]. 农业工程学报, 2019, 35(16):189-197.
	Yi Q X. Remote estimation of cotton LAI using Sentinel-2 multispectral data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(16): 189-197.
11	吾木提·艾山江, 买买提·沙吾提, 陈水森, 等. 基于GF-1/2卫星数据的冬小麦叶面积指数反演[J]. 作物学报, 2020, 46(5): 787-797.
	Umut H, Mamat S, Chen S, et al. Inversion of leaf area index of winter wheat based on GF-1/2 image[J]. Acta Agronomica Sinica, 2020, 46(5): 787-797.
12	Alonzo M, Bookhagen B, Mcfadden J P, et al. Mapping urban forest leaf area index with airborne LiDAR using penetration metrics and allometry[J]. Remote Sensing of Environment, 2015, 162(1):141-153.
13	Zhang L Y, Zhang H H, Niu Y X, et al. Mapping maize water stress based on UAV multispectral remote sensing[J]. Remote Sensing, 2019, 11(6): 605.
14	Yu N, Li L J, Schmitza N, et al. Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform[J]. Remote Sensing of Environment, 2016, 187(15): 91-101.
15	Bhardwaj A, Sam L, Akanksha A, et al. UAVs as Remote Sensing Platform in Glaciology: Present Applications and Future Prospects[J]. Remote Sensing of Environment, 2016, 175(15):196-204.
16	Niu Y X, Zhang L Y, Zhang H H, et al. Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery[J]. Remote Sensing, 2019, 11(11): 1261.
17	Guan K, Wu J, Kimball J S, et al. The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields[J]. Remote Sensing of Environment, 2017, 199: 333-349.
18	Roosjen P P J, Bbede B, Suomalainen J M, et al. Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data-potential of unmanned aerial vehicle imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 66: 14-26.
19	Berni J A J, ZARCO-TEJADA P J, SUAREZ L, et al. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(3): 722-738.
20	刘峰, 刘素红, 向阳. 园地植被覆盖度的无人机遥感监测研究[J]. 农业机械学报, 2014, 45(11): 250-257.
	Liu F, Liu S H, Xiang Y. Study on monitoring fractional vegetation cover of garden plots by unmanned aerial vehicles[J]. Transactions of the Chinese Society for Agriculural Machinery, 2014, 45(11): 250-257.
21	Corcoles J I, Ortega J F, Hernandez D, et al. Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle[J]. Biosystems Engineering, 2013, 115(1): 31-42.
22	Peter P J, Roosjen B B, Juha M, et al. Improved Estimation of Leaf Area Index and Leaf Chlorophyll Content of a Potato Crop using Multi-angle Spectral Data-Potential of Unmanned Aerial Vehicle Imagery[J]. International Journal of Applied Earth Observations and Geoinformation, 2017.
23	孙涛, 刘振波, 葛云健, 等. 基于数码相片Gamma 校正的水稻叶面积指数估算[J]. 生态学报, 2014, 34(13):3548-3557.
	Sun T, Liu Z B, Ge Y J, et al. Estimation of paddy rice leaf area index based on photo gamma correction[J]. Acta Ecologica Sinica, 2014, 34(13): 3548-3557
24	王瑛. 基于无人机遥感的小麦叶面积指数反演方法研究[D].杨凌: 西北农林科技大学, 2017: 28.
	Wang Y. Research on the inversion methods of wheat leaf area index based on unmanned aerial vehicle remote sensing[D]. Yangling: Northwest A＆F University, 2017: 28.
25	褚洪亮, 肖青, 柏军华, 等. 基于无人机遥感的叶面积指数反演[J]. 遥感技术与应用, 2017, 31(01):140-148.
	Chu H L, Xiao Q, Bai J H, et al. The retrieval of leaf area index based on remote sensing by unmanned aerial vehicle[J]. Remote Sensing Technology and Application, 2017, 31(1): 140-148.
26	牛庆林, 冯海宽, 杨贵军, 等. 基于无人机数码影像的玉米育种材料株高和LAI监测[J].农业工程学报, 2018, 34(05): 73-82.
	Niu Q L, Feng H K, Yang G J, et al. Monitoring plant height and leaf area index of maize breeding material based on UAV digital images[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(5): 73-82.
27	孙诗睿, 赵艳玲, 王亚娟, 等. 基于无人机多光谱遥感的冬小麦叶面积指数反演[J].中国农业大学学报, 2019, 24(11):51-58.
	Sun S R, Zhao Y L, Wang Y J, et al. Leaf area index inversion of winter wheat based on multispectral remote sensing of UAV. Journal of China Agricultural University. 2019, 24(11):51-58.
28	高林, 杨贵军, 李红军, 等. 基于无人机数码影像的冬小麦叶面积指数探测研究[J].中国生态农业学报, 2016, 24(09):1254-1264.
	Gao L, Yang G J, Li H J, et al. Winter wheat LAI estimation using unmanned aerial vehicle RGB-imaging[J]. Chinese Journal of Eco-Agriculture, 2016, 24(09): 1254-1264.
29	高林, 杨贵军, 王宝山, 等. 基于无人机遥感影像的大豆叶面积指数反演研究[J].中国生态农业学报,2015,23(07):868-876.
	Gao L, Yang G J, Wang B S, et al. Soybean leaf area index retrieval with UAV (unmanned aerial vehicle) remote sensing imagery[J]. Chinese Journal of Eco-Agriculture, 2015,23(07):868-876.
30	刘良云.叶面积指数遥感尺度效应与尺度纠正[J].遥感学报, 2014, 18(6):1158-1168.
	Liu L Y. Simulation and correction of spatial scaling effects for leaf area index. Journal of Remote Sensing, 2014, 18(6): 1158 -1168.
31	Rouse J W, Haas R H, Schell J A,et al. Monitoring vegetation systems in the Great Plains with ERTS[Z]. NASA Special Publication, 1974, 351: 309-313.
32	Rondeaux G, Steven M, Bare F. Op-timization of soil-adjusted vegetation indices[J]. Remote Sensing of Environment, 1996, 55: 95-107.
33	Huete A R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 1988, 25(3): 295-309.
34	Barnes E M, Clarke T R, Richards S E, et al. Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In: Robert P C. Rust RH LarsonWE, eds., Proceedings of the 5th International Conference on Precision Agriculture, Bloomington. MN. USA. 2000, 7, 16-19.
35	陈俊英, 陈硕博, 张智韬, 等. 无人机多光谱遥感反演花蕾期棉花光合参数研究[J].农业机械学报, 2018, 49(10):230-239.
	Chen J Y, Chen S B, Zhang Z T, et al. Investigation on Photosynthetic Parameters of Cotton during Budding Period by Multi-spectral Remote Sensing of Unmanned Aerial Vehicle [J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(10):230-239.
36	杨海波, 高兴, 黄绍福, 等. 基于卫星波段的马铃薯植株氮素含量估测[J].光谱学与光谱分析, 2019, 39(09):2686-2692.
	Yang H B, Gao X, Huang S F, et al. Staellite bands based estimation of nitrogen concentration in potato plants[J]. Spectroscopy and Spectral Analysis, 2019, 39(09):2686-2692.

设备 Equipment	参数 Parameter		参数值 Value
无人机遥感平台 UAV	有效载荷 Playload		0.8 kg
	航测速度Speed		2 m/s
	续航时间Flight time		20 min
	传感器 Camera		MicaSense RedEdge-M
	图像分辨率 Imager resolution		1280×960 pixels
多光谱传感器 MicaSense RedEdge-M	光谱波段 Spectral bands	中心波长 Central wavelength	波宽 Wavelength width	灰板反射率 Reference reflectance
	蓝 Blue	475 nm	20 nm	51.2 %
	绿 Green	560 nm	20 nm	51.2 %
	红 Red	668 nm	10 nm	51.2 %
	近红外 Near infrared	840 nm	40 nm	51.0 %
	红边 Rededge	717 nm	10 nm	51.1 %

植被指数 Vegetation index	计算公式 Formulas	来源 References
NDVI (Normalized difference vegetation index)	NDVI =ρ_nir-ρ_r/ρ_nir+ρ_r	[31]
OSAVI (Optimize Soil-adjusted vegetation index)	OSAVI =(ρ_nir-ρ_r)/( ρ_nir+ρ_r+X)	[32]
EVI (Enhanced Vegetation Index)	EVI=2.5×(ρ_nir -ρ_r)/(ρnir +6×ρ_r -7.5×ρ_b +1)	[33]
NDRE（Normalized difference red edge index）	NDRE=(ρ_nir-ρ_re)/( ρ_nir +ρ_re)	[34]

参数 Parameter	植被指数 Vegetation index
参数 Parameter	拔节期 Jointing	抽雄期 Tasseling	成熟期 Maturation
NDVI	0.721**	0.835**	0.813**
OSAVI	0.816**	0.817**	0.827**
EVI	0.713**	0.831**	0.794**
NDRE	0.741**	0.868**	0.856**

生育时期 Growth stages	植被指数 Vegetation index	估算模型			验证模型
生育时期 Growth stages	植被指数 Vegetation index	估算方程 Equation	决定系数 Determination coefficient R²	标准误差 Standard error SE	相对误差 Relative error RE	均方根误差 Root mean square error RMSE
拔节期 Jointing	NDVI	Y=2.243x-0.178	0.520	0.121	9.58	0.117
	OSAVI	Y=1.914x+0.493	0.666	0.026	8.57	0.104
	EVI	Y=1.627x+0.395	0.508	0.133	11.31	0.147
	NDRE	Y=2.395 x+0.164	0.549	0.089	10.46	0.134
抽雄期 Tasseling	NDVI	Y=1.523x+1.751	0.697	0.079	10.84	0.114
	OSAVI	Y=2.174x+0.953	0.667	0.121	9.79	0.093
	EVI	Y=2.336x+1.157	0.691	0.103	10.36	0.121
	NDRE	Y=5.241x+3.343	0.753	0.027	8.34	0.087
成熟期Maturation	NDVI	Y=1.954x+0.247	0.661	0.141	13.41	0.133
	OSAVI	Y=2.517x-0.314	0.684	0.106	10.53	0.141
	EVI	Y=1.983x+0.185	0.630	0.093	9.83	0.156
	NDRE	Y=5.711x-0.293	0.733	0.047	9.24	0.091

[1]	ZHANG Zhaoxu, XIAO Yue, GOU Wentao, CUI Jin. Study on Drought Monitoring and Spatiotemporal Change in Henan Province Based on Sun/solar-induced Chlorophyll Fluorescence Remote Sensing [J]. Journal of Agricultural Big Data, 2023, 5(1): 76-86.
[2]	ZHANG Zhaoxu, CUI Jin, GOU Wentao, XIAO Yue. Drought Monitoring and Spatiotemporal Changes Analysis in North China Plain Based on Temperature Vegetation Dryness Index [J]. Journal of Agricultural Big Data, 2023, 5(1): 95-107.
[3]	Xinglong Liu, Yu Wang, Xiaoxi Wang, Keqin Wang. A Dataset of Coccinellidae in Farmlands in Harbin, Heilongjiang Province from 2018 to 2021 [J]. Journal of Agricultural Big Data, 2022, 4(4): 39-44.
[4]	Ayitula Maimaitizunong, Shuai Yanju, Haodong Wei, Zhen He, Qinxi Xiao, Qiong Hu, Baodong Xu, Liangzhi You, Cougui Cao, Lin Ling. Evaluation of Green Development of Rice-Based Cropping Systems Using Remote Sensing Data and the DNDC Model: Case Study of Qianjiang City [J]. Journal of Agricultural Big Data, 2021, 3(3): 33-44.
[5]	Qiang Li, Maofang Gao, Ying Fang. Research on the Construction of the Agricultural Big Data Information Platform [J]. Journal of Agricultural Big Data, 2021, 3(2): 24-30.
[6]	Mingxu Zhang, Yuan Chen, Tuya Xilin, Ru Zhang, Yaqiong Bi, Chunhong Zhang, Taotao Wu, Minhui Li. Application and Prospects for Big Data of Traditional Chinese Medicine Resources [J]. Journal of Agricultural Big Data, 2021, 3(1): 14-24.
[7]	Kai Li, Qinghua Huang, Ruqing Zhong, Liang Chen, Weifeng Yuan, Hongfu Zhang. Analysis of the Contents and Correlations of Nutrient Components in Different Maize Cultivars [J]. Journal of Agricultural Big Data, 2020, 2(4): 70-77.
[8]	Yao Xie, Yan Tang, Xia Liu, Jing Meng, Lei Wang. Bibliometrics-based Analysis of Advances in Maize Adversity Stress Mechanism [J]. Journal of Agricultural Big Data, 2020, 2(2): 112-124.
[9]	Laigang Guo Yan Wang Lijun He Jia Yang Xiuzhong Zhang Hongli Zheng Guoqing Wang. Developing a Combined Map of the Spatial Distribution of Autumn Crops in Henan Province Using Multi-source Remote Sensing [J]. Journal of Agricultural Big Data, 2020, 2(1): 53-59.
[10]	Xiaoman Li,Yang Zhang,Qian Xu,Nengfu Xie. Bibliometrics-Based Analysis of Advances in Plant Phenomics Research [J]. Journal of Agricultural Big Data, 2019, 1(2): 64-75.
[11]	Li Xianjiang, Chen Youqi, Zou Jinqiu, Shi Shuqin, Guo Tao, Cai Weimin, Chen Hao. Application of Convolutional Neural Networks in High-Resolution Image Classification [J]. Journal of Agricultural Big Data, 2019, 1(1): 67-77.

Estimating the Leaf Area Index of Maize based on Unmanned Aerial Vehicle Multispectral Remote Sensing

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 36

Related Articles 11

Metrics

Comments

Recommended 0