Influence of unmanned aerial vehicle observation time on estimation of canopy chlorophyll density of maize

doi:10.3969/j.issn.1004-1524.20230284

Acta Agriculturae Zhejiangensis ›› 2024, Vol. 36 ›› Issue (1): 18-31.DOI: 10.3969/j.issn.1004-1524.20230284

• Crop Science • Previous Articles Next Articles

Influence of unmanned aerial vehicle observation time on estimation of canopy chlorophyll density of maize

ZHOU Lili¹^,²^,³(), FENG Haikuan⁴, NIE Chenwei²^,³, XU Xiaobin⁵, LIU Yuan¹^,²^,³, MENG Lin²^,³, XUE Beibei², MING Bo², LIANG Qiyun¹, SU Tao¹^,^*(), JIN Xiuliang²^,³^,^*()

1. School of Spatial Information and Geomatics Engineering, Anhui University of Science and Technology, Huainan 232001, Anhui, China
2. Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
3. National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, Hainan, China
4. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097,China
5. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Received:2023-03-06 Online:2024-01-25 Published:2024-02-18

Abstract

Abstract:

In order to explore the effect of multispectral data of unmanned aerial vehicles (UAV) acquired at different time on the estimation of canopy chlorophyll density (CCD) of maize, the UAV multispectral observation experiment was conducted at 10:00—10:59, 11:00—11:59, 13:00—13:59, 14:00—14:59 at the tasseling silking stage, blister stage, milk stage and dough stage, respectively. The PROSAIL model simulation results and the measured CCD data were combined to analyze the changes of the typical vegetation indexes at different time of the day and the difference between the CCD estimation results. The results showed that both the canopy reflectance and the value of vegetation indexes strongly correlated with the measured CCD changed with the time on the same day. The most obvious change of reflectance was found in the near-infrared band on the same day. The vegetation index value decreased as it approached 12:00 in the noon, whereas the value of vegetation indexes simulated by PROSAIL model at different time of the day showed little difference. The correlation between the same vegetation index obtained at different observation time and the measured CCD was different on the same day, and the difference between growth stages and indexes was not consistent; while the correlation between the same simulated vegetation index and CCD was not obvious at different time on the same day. CCD estimation models based on UAV data at different observation time could achieve good accuracy at different growth stages, but the estimation results at different observation time were different, with the lowest determination coefficient (R²) of 0.53 and the highest R² of 0.80. These results showed that the traditional spectral data acquisition time range (10:00—14:00) had an impact on CCD estimation of maize, and the closer to the noon (12:00), the higher the estimation accuracy. These results provided basic support for the accurate CCD estimation of crops.

Key words: canopy chlorophyll density, observation time, machine learning, PROSAIL model, maize

CLC Number:

S127
TP72

ZHOU Lili, FENG Haikuan, NIE Chenwei, XU Xiaobin, LIU Yuan, MENG Lin, XUE Beibei, MING Bo, LIANG Qiyun, SU Tao, JIN Xiuliang. Influence of unmanned aerial vehicle observation time on estimation of canopy chlorophyll density of maize[J]. Acta Agriculturae Zhejiangensis, 2024, 36(1): 18-31.

Figures/Tables 11

References 41

[1]	TIAN X L, LIU J J, LIU Q C, et al. The effects of soil properties, cropping systems and geographic location on soil prokaryotic communities in four maize production regions across China[J]. Journal of Integrative Agriculture, 2022, 21(7): 2145-2157.
[2]	SHAO G M, HAN W T, ZHANG H H, et al. Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods[J]. Agricultural Water Management, 2023, 276: 108064.
[3]	LÓPEZ A, JURADO J M, OGAYAR C J, et al. A framework for registering UAV-based imagery for crop-tracking in Precision Agriculture[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 97: 102274.
[4]	DENG L, MAO Z H, LI X J, et al. UAV-based multispectral remote sensing for precision agriculture: a comparison between different cameras[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 146: 124-136.
[5]	CHENG J P, YANG H, QI J B, et al. Estimating canopy-scale chlorophyll content in apple orchards using a 3D radiative transfer model and UAV multispectral imagery[J]. Computers and Electronics in Agriculture, 2022, 202: 107401.
[6]	JAY S, BARET F, DUTARTRE D, et al. Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops[J]. Remote Sensing of Environment, 2019, 231: 110898.
[7]	WANG N, SUOMALAINEN J, BARTHOLOMEUS H, et al. Diurnal variation of sun-induced chlorophyll fluorescence of agricultural crops observed from a point-based spectrometer on a UAV[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 96: 102276.
[8]	郭松, 常庆瑞, 郑智康, 等. 基于无人机高光谱影像的玉米叶绿素含量估测[J]. 江苏农业学报, 2022, 38(4): 976-984.
	GUO S, CHANG Q R, ZHENG Z K, et al. Estimation of maize chlorophyll content based on unmanned aerial vehicle (UAV) hyperspectral images[J]. Jiangsu Journal of Agricultural Sciences, 2022, 38(4): 976-984. (in Chinese with English abstract)
[9]	QIAO L, ZHANG Z Y, CHEN L S, et al. Detection of chlorophyll content in maize canopy from UAV imagery[J]. IFAC-PapersOnLine, 2019, 52(30): 330-335.
[10]	LI Z H, LI Z H, 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.
[11]	YANG G J, LI C C, WANG Y J, et al. The DOM generation and precise radiometric calibration of a UAV-mounted miniature snapshot hyperspectral imager[J]. Remote Sensing, 2017, 9(7): 642.
[12]	PENG M M, HAN W T, LI C Q, et al. Modeling the daytime net primary productivity of maize at the canopy scale based on UAV multispectral imagery and machine learning[J]. Journal of Cleaner Production, 2022, 367: 133041.
[13]	周珊羽. 基于无人机高光谱系统多角度观测的农作物叶绿素含量反演[D]. 济南: 山东大学, 2019.
	ZHOU S Y. Parameter retrieval of different crop types based on hyperspectral multi-angle data recorded from a UAV platform[D]. Jinan: Shandong University, 2019. (in Chinese with English abstract)
[14]	WANG L, LIAO Q H, XU X B, et al. Estimating the vertical distribution of chlorophyll in winter wheat based on multi-angle hyperspectral data[J]. Remote Sensing Letters, 2020, 11(11): 1032-1041.
[15]	ADELUYI O, HARRIS A, VERRELST J, et al. Estimating the phenological dynamics of irrigated rice leaf area index using the combination of PROSAIL and Gaussian Process Regression[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 102: 102454.
[16]	LI D, CHEN J M, ZHANG X, et al. Improved estimation of leaf chlorophyll content of row crops from canopy reflectance spectra through minimizing canopy structural effects and optimizing off-noon observation time[J]. Remote Sensing of Environment, 2020, 248: 111985.
[17]	舒方, 袁金国. 基于模型模拟的NDVI对叶片生化组分参数的敏感性及叶绿素估算[J]. 科技通报, 2019, 35(4): 120-128.
	SHU F, YUAN J G. Sensitivity of NDVI to leaf biochemical components parameters based on simulation and chlorophyll content estimation[J]. Bulletin of Science and Technology, 2019, 35(4): 120-128. (in Chinese with English abstract)
[18]	何宇航. 多角度遥感植物冠层叶绿素含量反演研究[D]. 杭州: 杭州电子科技大学, 2021.
	HE Y H. Research on plant canopy chlorophyll content retrieval with multi-angle remote sensing techniques[D]. Hangzhou: Hangzhou Dianzi University, 2021. (in Chinese with English abstract)
[19]	姚延娟, 范闻捷, 刘强, 等. 玉米全生长期叶面积指数收获测量法的改进[J]. 农业工程学报, 2010, 26(8): 189-194.
	YAO Y J, FAN W J, LIU Q, et al. Improved harvesting method for corn LAI measurement in corn whole growth stages[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(8): 189-194. (in Chinese with English abstract)
[20]	李得孝, 郭月霞, 员海燕, 等. 玉米叶绿素含量测定方法研究[J]. 中国农学通报, 2005, 21(6): 153-155.
	LI D X, GUO Y X, YAN H Y, et al. Determined methods of chlorophyll from maize[J]. Chinese Agricultural Science Bulletin, 2005, 21(6): 153-155. (in Chinese with English abstract)
[21]	TA N, CHANG Q R, ZHANG Y M. Estimation of apple tree leaf chlorophyll content based on machine learning methods[J]. Remote Sensing, 2021, 13(19): 3902.
[22]	HAN H S, WAN R R, LI B. Estimating forest aboveground biomass using Gaofen-1 images, Sentinel-1 images, and machine learning algorithms: a case study of the dabie mountain region, China[J]. Remote Sensing, 2021, 14(1): 176.
[23]	ZENG N, REN X L, HE H L, et al. Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm[J]. Ecological Indicators, 2019, 102: 479-487.
[24]	JACQUEMOUD S, VERHOEF W, BARET F, et al. PROSPECT+SAIL models: a review of use for vegetation characterization[J]. Remote Sensing of Environment, 2009, 113: S56-S66.
[25]	DARVISHZADEH R, SKIDMORE A, SCHLERF M, et al. Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland[J]. Remote Sensing of Environment, 2008, 112(5): 2592-2604.
[26]	ROUSE J W, HAAS R H, SCHELL J A. Monitoring the vernal advancements and retrogradation (green wave effect) of natural vegetation[EB/OL]. (1973-04-01) [2023-03-06]. https://ntrs.nasa.gov/api/citations/19730017588/downloads/19730017588.pdf.
[27]	GITELSON A A, MERZLYAK M N. Remote sensing of chlorophyll concentration in higher plant leaves[J]. Advances in Space Research, 1998, 22(5): 689-692.
[28]	GITELSON A A, VIÑA A, CIGANDA V, et al. Remote estimation of canopy chlorophyll content in crops[J]. Geophysical Research Letters, 2005, 32(8): L08403.
[29]	PEN-UELAS J, FILELLA I, LLORET P, et al. Reflectance assessment of mite effects on apple trees[J]. International Journal of Remote Sensing, 1995, 16(14): 2727-2733.
[30]	GITELSON A, MERZLYAK M N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. spectral features and relation to chlorophyll estimation[J]. Journal of Plant Physiology, 1994, 143(3): 286-292.
[31]	DASH J, CURRAN P J. The MERIS terrestrial chlorophyll index[J]. International Journal of Remote Sensing, 2004, 25(23): 5403-5413.
[32]	DAUGHTRY C S T, WALTHALL C L, KIM M S, et al. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[J]. Remote Sensing of Environment, 2000, 74(2): 229-239.
[33]	ZARCO-TEJADA P J, HABOUDANE D, MILLER J R, et al. Leaf chlorophyll a+b and canopy LAI estimation in crops using R-T models and hyperspectral reflectance imagery[EB/OL]. [2023-03-06]. https://quantalab.ias.csic.es/pdf/zarco_ESA_2002.pdf.
[34]	TUCKER C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing of Environment, 1979, 8(2): 127-150.
[35]	HUETE A, DIDAN K, MIURA T, et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices[J]. Remote Sensing of Environment, 2002, 83(1/2): 195-213.
[36]	RONDEAUX G, STEVEN M, BARET F. Optimization of soil-adjusted vegetation indices[J]. Remote Sensing of Environment, 1996, 55(2): 95-107.
[37]	METTERNICHT G. Vegetation indices derived from high-resolution airborne videography for precision crop management[J]. International Journal of Remote Sensing, 2003, 24(14): 2855-2877.
[38]	HABOUDANE D, MILLER J R, TREMBLAY 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(2/3): 416-426.
[39]	ALI A, IMRAN M, ALI A, et al. Evaluating Sentinel-2 red edge through hyperspectral profiles for monitoring LAI & chlorophyll content of Kinnow Mandarin orchards[J]. Remote Sensing Applications: Society and Environment, 2022, 26: 100719.
[40]	MILLER J R, HARE E W, WU J. Quantitative characterization of the vegetation red edge reflectance 1: an inverted-Gaussian reflectance model[J]. International Journal of Remote Sensing, 1990, 11(10): 1755-1773.
[41]	许晓斌. 基于辐射传输模型的玉米氮素高光谱遥感定量估算[D]. 青岛: 山东科技大学, 2019.
	XU X B. Nitrogen quantitative estimation of maize using hyperspectral remote sensing based on radiation transfer model[D]. Qingdao: Shandong University of Science and Technology, 2019. (in Chinese with English abstract)

处理 Treatment	不同种植时间的肥料施用量Application rate of fertilizer at different time
处理 Treatment	50 d	60 d	70 d	80 d	90 d	100 d
N_D1	0	0	0	72.92	72.92	72.92
N_D2	0	0	72.92	72.92	72.92	72.92
N_D3	0	0	145.83	72.92	72.92	72.92
N_D4	0	72.92	0	72.92	72.92	72.92
N_D5	0	72.92	72.92	72.92	72.92	72.92
N_D6	0	72.92	145.83	72.92	72.92	72.92
N_D7	0	145.83	0	72.92	72.92	72.92
N_D8	0	145.83	72.92	72.92	72.92	72.92
N_D9	0	145.83	145.83	72.92	72.92	72.92
N_D10	72.92	0	0	72.92	72.92	72.92
N_D11	72.92	0	72.92	72.92	72.92	72.92
N_D12	72.92	0	145.83	72.92	72.92	72.92
N_D13	72.92	72.92	0	72.92	72.92	72.92
N_D14	72.92	72.92	72.92	72.92	72.92	72.92
N_D15	72.92	72.92	145.83	72.92	72.92	72.92
N_D16	72.92	145.83	0	72.92	72.92	72.92
N_D17	72.92	145.83	72.92	72.92	72.92	72.92
N_D18	72.92	145.83	145.83	72.92	72.92	72.92
N_D19	145.83	0	0	72.92	72.92	72.92
N_D20	145.83	0	72.92	72.92	72.92	72.92
N_D21	145.83	0	145.83	72.92	72.92	72.92
N_D22	145.83	72.92	0	72.92	72.92	72.92
N_D23	145.83	72.92	72.92	72.92	72.92	72.92
N_D24	145.83	72.92	145.83	72.92	72.92	72.92
N_D25	145.83	145.83	0	72.92	72.92	72.92
N_D26	145.83	145.83	72.92	72.92	72.92	72.92
N_D27	145.83	145.83	145.83	72.92	72.92	72.92

处理 Treatment	不同种植时间的肥料施用量Application rate of fertilizer at different time
处理 Treatment	50 d	60 d	70 d	80 d	90 d	100 d
N_D1	0	0	0	72.92	72.92	72.92
N_D2	0	0	72.92	72.92	72.92	72.92
N_D3	0	0	145.83	72.92	72.92	72.92
N_D4	0	72.92	0	72.92	72.92	72.92
N_D5	0	72.92	72.92	72.92	72.92	72.92
N_D6	0	72.92	145.83	72.92	72.92	72.92
N_D7	0	145.83	0	72.92	72.92	72.92
N_D8	0	145.83	72.92	72.92	72.92	72.92
N_D9	0	145.83	145.83	72.92	72.92	72.92
N_D10	72.92	0	0	72.92	72.92	72.92
N_D11	72.92	0	72.92	72.92	72.92	72.92
N_D12	72.92	0	145.83	72.92	72.92	72.92
N_D13	72.92	72.92	0	72.92	72.92	72.92
N_D14	72.92	72.92	72.92	72.92	72.92	72.92
N_D15	72.92	72.92	145.83	72.92	72.92	72.92
N_D16	72.92	145.83	0	72.92	72.92	72.92
N_D17	72.92	145.83	72.92	72.92	72.92	72.92
N_D18	72.92	145.83	145.83	72.92	72.92	72.92
N_D19	145.83	0	0	72.92	72.92	72.92
N_D20	145.83	0	72.92	72.92	72.92	72.92
N_D21	145.83	0	145.83	72.92	72.92	72.92
N_D22	145.83	72.92	0	72.92	72.92	72.92
N_D23	145.83	72.92	72.92	72.92	72.92	72.92
N_D24	145.83	72.92	145.83	72.92	72.92	72.92
N_D25	145.83	145.83	0	72.92	72.92	72.92
N_D26	145.83	145.83	72.92	72.92	72.92	72.92
N_D27	145.83	145.83	145.83	72.92	72.92	72.92

生育时期 Growth stage	观测时间 Observation time	太阳天顶角 Solar zenith angle(SZA)/(°)	太阳方位角 Solar azimuth angle(SAA)/(°)
抽雄吐丝期Tasseling silking stage (VT/R1)	10:00—10:59(10AM)	35.59	125.46
	11:00—11:59(11AM)	28.16	148.67
	13:00—13:59(1PM)	28.18	211.31
	14:00—14:59(2PM)	35.62	234.50
籽粒建成期Blister stage (R2)	10:00—10:59(10AM)	38.17	128.70
	11:00—11:59(11AM)	31.16	151.15
	13:00—13:59(1PM)	31.18	208.83
	14:00—14:59(2PM)	38.21	231.25
乳熟期Milk stage (R3)	10:00—10:59(10AM)	40.76	131.52
	11:00—11:59(11AM)	34.11	153.18
	13:00—13:59(1PM)	34.14	206.80
	14:00—14:59(2PM)	40.80	228.43
蜡熟期Dough stage (R5)	10:00—10:59(10AM)	44.30	134.84
	11:00—11:59(11AM)	38.09	155.45
	13:00—13:59(1PM)	38.12	204.54
	14:00—14:59(2PM)	44.35	225.11

生育时期 Growth stage	观测时间 Observation time	太阳天顶角 Solar zenith angle(SZA)/(°)	太阳方位角 Solar azimuth angle(SAA)/(°)
抽雄吐丝期Tasseling silking stage (VT/R1)	10:00—10:59(10AM)	35.59	125.46
	11:00—11:59(11AM)	28.16	148.67
	13:00—13:59(1PM)	28.18	211.31
	14:00—14:59(2PM)	35.62	234.50
籽粒建成期Blister stage (R2)	10:00—10:59(10AM)	38.17	128.70
	11:00—11:59(11AM)	31.16	151.15
	13:00—13:59(1PM)	31.18	208.83
	14:00—14:59(2PM)	38.21	231.25
乳熟期Milk stage (R3)	10:00—10:59(10AM)	40.76	131.52
	11:00—11:59(11AM)	34.11	153.18
	13:00—13:59(1PM)	34.14	206.80
	14:00—14:59(2PM)	40.80	228.43
蜡熟期Dough stage (R5)	10:00—10:59(10AM)	44.30	134.84
	11:00—11:59(11AM)	38.09	155.45
	13:00—13:59(1PM)	38.12	204.54
	14:00—14:59(2PM)	44.35	225.11

植被指数 Vegetation index	各生育时期的相关系数Correlation coefficient at different growth stages
植被指数 Vegetation index	VT/R1	R2	R3	R5
MTCI	0.65^**	0.74^**	0.72^**	0.68^**
NDVI_rededge	0.70^**	0.76^**	0.75^**	0.75^**
CI_rededge	0.68^**	0.75^**	0.74^**	0.73^**
NDVI	0.36^**	0.61^**	0.64^**	0.66^**
GNDVI	0.72^**	0.67^**	0.77^**	0.76^**
TCARI/OSAVI	0.26^**	0.46^**	0.13^*	0.41^**
OSAVI	0.28^**	0.43^**	0.16^*	0.55^**
SIPI	0.07	0.02	0.09	0.34^**
EVI	0.23^**	0.31^**	0.09	0.47^**
DVI	0.24^**	0.31^**	0.09	0.44^**
TCARI	0.20^**	0.37^**	0.08	0.33^**
MCARI	0.20^**	0.37^**	0.08	0.33^**
PPR	0.11^*	0.31^**	0.13^*	0.37^**

Influence of unmanned aerial vehicle observation time on estimation of canopy chlorophyll density of maize

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[15]	WANG Jia, MU Ruirui, YANG Qiaoqiao, LIU Wei, ZHANG Yuehe, KANG Jianhong. Effects of potassium application rate on chlorophyll fluorescence characteristics and yield of spring maize in Ningxia under integrated drip irrigation [J]. Acta Agriculturae Zhejiangensis, 2021, 33(8): 1347-1357.