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Acta Agronomica Sinica ›› 2025, Vol. 51 ›› Issue (1): 189-206.doi: 10.3724/SP.J.1006.2025.43015

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

Estimation of canopy nitrogen concentration in maize based on UAV multi- spectral data and spatial nitrogen heterogeneity

HAO Qi(), CHEN Tian-Lu, WANG Fu-Gui, WANG Zhen, BAI Lan-Fang, WANG Yong-Qiang*(), WANG Zhi-Gang*()   

  1. College of Agronomy, Inner Mongolia Agricultural University, Hohhot 010010, Inner Mongolia, China
  • Received:2024-04-01 Accepted:2024-08-15 Online:2025-01-12 Published:2024-09-02
  • Contact: *E-mail: imauwzg@163.com; E-mail: wangyongqiang@nwafu.edu.cn
  • Supported by:
    Major Science and Technology Project of Inner Mongolia(2021ZD0003);National Natural Science Foundation of China(32160507);Key Project for Action ‘Development of Mongolia through Science and Technology’ of Inner Mongolia(NMKJXM202111)

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Abstract:

Remote sensing diagnosis of crop canopy nitrogen nutrition is crucial for guiding precise nitrogen application and improving crop nitrogen efficiency and yield. To address the issue of maize canopy depth significantly affecting the accuracy of UAV-based nitrogen concentration estimation, this study analyzed the spatial heterogeneity characteristics of maize canopy nitrogen concentration. This analysis was based on multi-spectral data and measured nitrogen concentration data from UAV across fields with different nitrogen fertilizer treatments in 2022 and 2023. Using the random forest algorithm, we identified the effective leaf layer for estimating canopy nitrogen concentration. We further constructed an estimation model for effective leaf nitrogen concentration by combining the random forest algorithm with multi-spectral vegetation indices, and then converted the effective leaf nitrogen concentration to the canopy scale to estimate the overall canopy nitrogen concentration. The results were as follows: (1) The nitrogen concentration of the maize canopy at the 9-leaf extension and large trumpet stages was highest in the upper leaves, followed by the middle and lower leaves. At the silk-spinning and milk-ripening stages, the nitrogen concentration was highest in the middle leaves, followed by the upper and lower leaves. (2) The effective leaf layers for estimating canopy nitrogen concentration at each growth stage were the lower layer, middle layer, middle layer, and middle layer, respectively. The random forest regression model demonstrated higher accuracy in estimating canopy nitrogen concentration compared to the support vector regression model. (3) Using the random forest algorithm, the average RMSE, NRMSE, and MAE for estimating canopy nitrogen concentration based on effective leaf nitrogen concentration were 0.10%, 4.41%, and 0.07%, respectively. In contrast, the average RMSE, NRMSE, and MAE for estimation based on direct vegetation indices were 0.19%, 9.00%, and 0.15%, respectively. In conclusion, the study identified the spatial differentiation of maize canopy nitrogen concentration. Considering effective leaf nitrogen concentration based on random forest and vegetation index estimation significantly improved the accuracy of canopy nitrogen concentration estimation. The canopy nitrogen concentration estimation framework, which accounts for the spatial heterogeneity of canopy nitrogen concentration, established in this study can provide theoretical support for real-time nitrogen nutrition diagnosis of maize.

Key words: maize, canopy nitrogen concentration, spatial nitrogen heterogeneity, multispectral data, machine learning

Table 1

Soil nutrient content in the 0-30 cm layer at the test site in 2022-2023"

年份
Year		 试验地点
Experimental location		 有机质
Organic
matter
(g kg-1)		 碱解氮
Alkali-hydrolyzale
nitrogen
(mg kg-1)		 速效磷
Rapidly available
phosphorus
(mg kg-1)		 速效钾
Rapidly available
potassium
(mg kg-1)		 pH
2022 TRB 23.7 53.6 4.5 114.9 7.5
TLB 23.9 33.1 14.7 148.0 7.5
2023 TRB 22.5 58.9 3.9 123.4 7.9

Fig. 1

Test field layout (A) and UAV RGB image (B), fertilization tanks layout for each treatment (C) N0, N270, and N360 indicate that the nitrogen application rates were 0, 270 kg hm-2, and 360 kg hm-2, respectively. F3 and F5 indicate that the nitrogen application times are three and five times, respectively."

Fig. 2

Meteorological conditions of the test site A: meteorological conditions from May to September in Tumd Right Banner in 2022; B: meteorological conditions from May to September in Tumd Left Banner in 2022; C: meteorological conditions from May to September in Tumd Right Banner in 2023."

Table 2

Fertilization of each treatment"

处理
Treatment		 施肥时期和施肥量 Fertilizer application time and rate (kg hm-2)
V4 V8 V12 R1 R3
N P2O5 K2O N N P2O5 K2O N N
N0 0 48 28.2 — 0 72 42.3 — 0
N270-F3 108.0 48 28.2 — 108.0 72 42.3 — 54
N360-F3 144.0 48 28.2 — 144.0 72 42.3 — 72
N270-F5 40.5 48 28.2 67.5 94.5 72 42.3 40.5 27
N360-F5 54.0 48 28.2 90.0 126.0 72 42.3 54.0 36

Table 3

Leaf stratification method at different growth stages of maize"

层位
Layer		 生育时期 Growth stage
V9 V12 R1 R3
上层叶片
Upper leaf		 上三叶
Upper three blades		 上四叶
Upper four blades		 穗三叶以上所有叶片
All leaves above three leaves of the corn ear		 穗三叶以上所有叶片
All leaves above three leaves of the corn ear
中层叶片
Middle leaf		 中三叶
Middle three blades		 中四叶
Middle four blades		 穗三叶
Three leaves of the corn ear		 穗三叶
Three leaves of the corn ear
下层叶片
Lower leaf		 下三叶
Lower three blades		 下四叶
Lower four blades		 穗三叶以下所有叶片
All leaves below three leaves of the corn ear		 穗三叶以下所有叶片
All leaves below three leaves of the corn ear

Fig. 3

UAV (A) equipped with multi-spectral sensor (B) image acquisition platform"

Table 4

Multi-spectral camera and gray-plate band information"

波段名称
Band name		 中心波长
Central wavelength (nm)		 带宽
Band width (nm)		 灰板反射率
Gray-plate reflectivity
蓝波段 Blue band 450 30 0.63
绿波段 Green band 555 27 0.62
红波段 Red band 660 22 0.61
红边波段 Red edge band 720 10 0.60
750 10 0.60
近红外波段 Nir band 840 30 0.58

Table 5

Vegetation index for estimation of nitrogen concentration"

序号
Number		 植被指数
Vegetation index		 公式
Formula		 来源
Resource
1 Chlorophyll index green (CI_Green) NIR/GREEN-1 Gitelson, et al.[13]
2 Green difference vegetation index (GDVI) NIR/GREEN Tucker, et al.[14]
3 Modified soil adjusted vegetation index (MSAVI) $\left[ 2\times \text{NIR}+1-\sqrt{{{\left( 2\times \text{NIR}+1 \right)}^{2}}}-8\times \left( \text{NIR}-\text{RED} \right) \right]/2$ Qi, et al.[15]
4 Modified simple ratio (MSR) [NIR/RED–1]/$\sqrt{\frac{\text{NIR}}{\text{RED}}+1}$ Chen[16]
5 Modified triangular vegetation index 1 (MTVI1) $1.2\times \left[ 1.2\times \left( \text{NIR}-\text{GREEN} \right)-2.5\times \left( \text{RED}-\text{GREEN} \right) \right]$ Haboudane, et al.[17]
6 Modified triangular vegetation index 2 (MTVI2) $\begin{align} & 1.8\times \left( \text{NIR}-\text{GREEN} \right)-3.75\times \left( \text{RED}-\text{GREEN} \right)/ \\ & \sqrt{{{\left( 2\times \text{NIR+1} \right)}^{2}}-6\times \left( \text{NIR}-5\times \sqrt{\text{RED}} \right)-0.5} \\ \end{align}$
7 Optimized soil adjusted vegetation index (OSAVI) 1.6×[(NIR-RED)/(NIR+RED+0.16)] Rondeaux, et al.[18]
8 Renormalized difference vegetation index (RDVI) $\left( \text{NIR}-\text{RED} \right)/\sqrt{\left( \text{NIR}+\text{RED} \right)}$ Roujean, et al.[19]
9 Ratio vegetation index (RVI) NIR/RED Jordan[20]
10 Soil adjusted vegetation index (SAVI) $\left( \text{NIR}-\text{RED} \right)\left( 1+0.5 \right)/\left( \text{NIR}+\text{RED}+0.5 \right)$ Huete, et al.[21]
11 Transformed chlorophyll absorption ratio index (TCARI) $3\times \left[ \left( \text{NIR}-\text{RED} \right)-0.2\times \left( \text{NIR}-\text{GREEN} \right)\left( \text{NIR}/\text{RED} \right) \right]$ Haboudane, et al.[22]
12 Triangular vegetation index (TVI) $\left[ 120\times \left( \text{NIR}-\text{GREEN} \right)-200\times \left( \text{RED}-\text{GREEN} \right) \right]/2$ Broge, Leblanc[23]

Fig. 4

Effects of different nitrogen application treatments on nitrogen concentration in maize canopy A: Tumd Right Banner in 2022; B: Tumd Left Banner in 2022; C: Tumd Right Banner in 2023. Abbreviations are the same as those given in Table 2. Treatments are the same as those given in Fig. 1."

Table 6

Multivariate variance analysis of maize canopy nitrogen concentration"

影响因素
Influence factor		 V9 V12 R1 R3
显著性
Sig.		 偏Eta平方
Partial Eta-square		 显著性
Sig.		 偏Eta平方
Partial Eta-square		 显著性
Sig.		 偏Eta平方
Partial Eta-square		 显著性
Sig.		 偏Eta平方
Partial Eta-square
E ns 0.13 ** 0.48 ** 0.87 ** 0.49
N ** 0.92 ** 0.93 ** 0.98 ** 0.93
F ** 0.68 ** 0.65 ** 0.94 ** 0.65
E×N ns 0 ns 0.15 ns 0.03 ns 0.17
E×F ns 0.01 ns 0.03 * 0.22 ns 0.02
N×F ns 0.10 ** 0.31 ** 0.31 * 0.18
E×N×F ns 0.17 ns 0 ** 0.39 ns 0.05

Fig. 5

Effects of different nitrogen application treatments on nitrogen concentration in leaves of different layers of maize canopy Abbreviations are the same as those given in Table 2. Treatments are the same as those given in Fig. 1. Nitrogen concentrations in leaves at different levels of corn canopy in Tumd Right Banner in 2022 (A-D), leaf nitrogen concentrations at different levels of corn canopy of V9, V12, R1, and R3 in Tumd Left Banner in 2022 (E-H) and at different levels of corn canopy of V9, V12, R1, and R3 in Tumd Right Banner in 2023 (I-L)."

Table 7

Multivariate variance analysis of leaf nitrogen concentration at different levels in maize canopy"

层位
Layer		 影响因素
Influence factor		 V9 V12 R1 R3
显著性
Sig.		 偏Eta平方
Partial Eta- squared		 显著性
Sig.		 偏Eta平方
Partial Eta- squared		 显著性
Sig.		 偏Eta平方
Partial Eta- squared		 显著性
Sig.		 偏Eta平方
Partial Eta- squared
上层叶片
Upper leaf		 E ** 0.98 ** 0.94 ** 0.89 ** 0.92
N ** 0.81 ** 0.64 ** 0.57 ** 0.64
F ** 0.62 ** 0.38 ns 0.13 ** 0.42
E×N ns 0.04 ns 0.08 ns 0.10 ns 0.13
E×F ns 0.04 ns 0 ns 0.01 ns 0.10
N×F * 0.24 ns 0.02 ns 0 ns 0.08
E×N×F ns 0.03 ns 0.02 ns 0.03 ns 0.06
中层叶片
Middle leaf		 E ** 0.94 ** 0.93 ** 0.90 ** 0.89
N ** 0.59 ** 0.75 ** 0.70 ** 0.66
F ** 0.35 ** 0.42 ** 0.45 ** 0.47
E×N ns 0.01 ns 0 * 0.19 ns 0.01
E×F ns 0 ns 0 ns 0.01 ns 0.01
N×F ns 0.01 ns 0.05 ns 0.05 ns 0
E×N×F ns 0.04 ns 0.02 ns 0.01 ns 0.01
下层叶片
Lower leaf		 E ** 0.86 ** 0.89 ** 0.85 ** 0.79
N ** 0.44 ** 0.51 ** 0.56 ** 0.75
F ns 0.15 ** 0.29 * 0.25 ** 0.30
E×N ns 0.01 ns 0.04 ns 0 * 0.22
E×F ns 0.00 ns 0 ns 0.01 ns 0.04
N×F ns 0.04 ns 0.03 ns 0 ns 0.02
E×N×F ns 0.02 ns 0.07 ns 0.04 ns 0

Table 8

Regression accuracy of leaf nitrogen concentration and canopy nitrogen concentration at different levels of maize"

数据集
Data Set		 评估指标
Evaluation indicator		 V9 V12 R1 R3
训练集
Training set		 R2 0.85 0.89 0.87 0.91
RMSE (%) 0.15 0.14 0.15 0.12
NRMSE (%) 6.27 5.98 7.67 6.84
MAE (%) 0.12 0.11 0.12 0.10
测试集
Test set		 R2 0.43 0.58 0.73 0.82
RMSE (%) 0.28 0.26 0.22 0.16
NRMSE (%) 11.64 11.79 11.32 9.44
MAE (%) 0.22 0.18 0.17 0.11

Fig. 6

The canopy nitrogen concentration of V9 (A), V12 (B), R1 (C), and R3 (D) effectively determined by the leaf layer based on the added value of node purity Increase in node purity represents the importance of leaf nitrogen concentration in different strata, and a larger value indicates a greater importance of leaf nitrogen concentration in the corresponding stratum. Abbreviations are the same as those given in Table 2."

Fig. 7

Correlation of V9 (A), V12 (B), R1 (C), and R3 (D) vegetation indices with canopy nitrogen concentration and effective leaf nitrogen concentration in maize NCmea, MLNC, and LLNC represent measured canopy nitrogen concentration, middle leaf nitrogen concentration, and lower leaf nitrogen concentration, respectively. * and ** indicate significant correlation at P < 0.05 and P < 0.01, respectively. Abbreviations are the same as those given in Table 2."

Fig. 8

Performance of Random Forest Regression and Support Vector Regression for estimating canopy nitrogen concentrations in corn V9 (A, E), V12 (B, F), R1 (C, G), and R3 (D, H) in training set NCpre_VI was used to predict canopy nitrogen concentration based directly on multispectral vegetation index, and NCpre_SNH was used to predict canopy nitrogen concentration considering nitrogen spatial heterogeneity. Abbreviations are the same as those given in Table 2."

Fig. 9

Performance of Random Forest Regression and Support Vector Regression for estimating canopy nitrogen concentrations in corn V9 (A, E), V12 (B, F), R1 (C, G), and R3 (D, H) in test set Abbreviations are the same as those given in Table 2 and Fig. 8."

Fig. 10

Spatial distribution of maize canopy nitrogen concentration based on UAV image and spatial heterogeneity of canopy nitrogen Field distribution of canopy N concentrations of maize V9 (A), V12 (B), R1 (C), and R3 (D) in Tumd Right Banner in 2022, field distribution of canopy N concentrations of maize V9 (E), V12 (F), R1 (G), and R3 (H) in Tumd Right Banner in 2023 and field distribution of canopy N concentrations of maize V9 (I), V12 (J), R1 (K), and R3 (L) in Tumd Left Banner in 2022. Treatments are the same as those given in Fig. 1. NC represents canopy nitrogen concentration."

[1] Guo B B, Qi S L, Heng Y R, Duan J Z, Zhang H Y, Wu Y P, Feng W, Xie Y X, Zhu Y J. Remotely assessing leaf N uptake in winter wheat based on canopy hyperspectral red-edge absorption. Eur J Agron, 2017, 82: 113-124.
[2] Wen P F, Wang R, Shi Z J, Ning F, Wang S L, Zhang Y J, Zhang Y H, Wang Q, Li J. Effects of N application rate on N remobilization and accumulation in maize (Zea mays L.) and estimating of vegetative N remobilization using hyperspectral measurements. Comput Electron Agric, 2018, 152: 166-181.
[3] Cai Y P, Guan K Y, Nafziger E, Chowdhary G, Peng B, Jin Z N, Wang S W, Wang S B. Detecting in-Season crop nitrogen stress of corn for field trials using UAV- and CubeSat-based multispectral sensing. IEEE J Sel Top Appl Earth Obs Remote Sens, 2019, 12: 5153-5166.
[4] Shu M Y, Zhu J Y, Yang X H, Gu X H, Li B G, Ma Y T. A spectral decomposition method for estimating the leaf nitrogen status of maize by UAV-based hyperspectral imaging. Comput Electron Agric, 2023, 212: 108100.
[5] Tavakoli H, Gebbers R. Assessing nitrogen and water status of winter wheat using a digital camera. Comput Electron Agric, 2019, 157: 558-567.
[6] Liu L, Peng Z G, Zhang B Z, Wei Z, Han N N, Lin S Z, Chen H, Cai J B. Canopy nitrogen concentration monitoring techniques of summer corn based on canopy spectral information. Sensors (Basel), 2019, 19: 4123.
[7] Wang X B, Miao Y X, Dong R, Zha H N, Xia T T, Chen Z C, Kusnierek K, Mi G H, Sun H, Li M Z. Machine learning-based in-season nitrogen status diagnosis and side-dress nitrogen recommendation for corn. Eur J Agron, 2021, 123: 126193.
[8] Li H L, Zhao C J, Huang W J, Yang G J. Non-uniform vertical nitrogen distribution within plant canopy and its estimation by remote sensing: a review. Field Crops Res, 2013, 142: 75-84.
[9] 魏夏永, 黄茜, 薄丽媛, 毛晓敏. 西北旱区不同覆膜和灌溉水平下的玉米冠层氮含量垂直分布及高光谱反演. 中国农业大学学报, 2022, 27(11): 13-21.
Wei X Y, Huang Q, Bo L Y, Mao X M. Vertical distribution of nitrogen content in spring maize leaves and its hyperspectral inversion under different film mulching and irrigation levels in northwest arid region. J China Agric Univ, 2022, 27(11): 13-21 (in Chinese with English abstract).
[10] Luo J H, Ma R H, Feng H H, Li X C. Estimating the total nitrogen concentration of reed canopy with hyperspectral measurements considering a non-uniform vertical nitrogen distribution. Remote Sens, 2016, 8: 789.
[11] Duan D D, Zhao C J, Li Z H, Yang G J, Yang W D. Estimating total leaf nitrogen concentration in winter wheat by canopy hyperspectral data and nitrogen vertical distribution. J Integr Agric, 2019, 18: 1562-1570.
[12] 刘蓉, 姚萌奇, 乔志刚, 李聪, 雒连春, 汪天胜, 张琪玮. 基于凯氏定氮法与杜马斯燃烧法测定肥料中氮含量的对比研究. 应用化工, 2023, 52: 2258-2260.
Liu R, Yao M Q, Qiao Z G, Li C, Luo L C, Wang T S, Zhang Q W. A comparative study based on Kjeldahl method and Dumas combustion methods for the determination of nitrogen content in fertilizers. Appl Chem Ind, 2023, 52: 2258-2260 (in Chinese with English abstract).
[13] Gitelson A A, Gritz Y, Merzlyak M N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol, 2003, 160: 271-282.
[14] Tucker C J, Elgin J H, McMurtrey J E, Fan C J. Monitoring corn and soybean crop development with hand-held radiometer spectral data. Remote Sens Environ, 1979, 8: 237-248.
[15] Qi J, Chehbouni A, Huete A R, Kerr Y H, Sorooshian S. A modified soil adjusted vegetation index. Remote Sens Environ, 1994, 48: 119-126.
[16] Chen J M. Evaluation of vegetation indices and a modified simple ratio for boreal applications, Can J Remote Sens, 1996, 22: 3, 229-242.
[17] Haboudane D, Miller J R, Pattey E, Zarco-Tejada P J, Strachan I B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens Environ, 2004, 90: 337-352.
[18] Rondeaux G, Steven M, Baret F. Optimization of soil-adjusted vegetation indices. Remote Sens Environ, 1996, 55: 95-107.
[19] Roujean J L, Breon F M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens Environ, 1995, 51: 375-384.
[20] Jordan C F. Derivation of leaf-area index from quality of light on the forest floor. Ecology, 1969, 50: 663-666.
[21] Huete A R. A soil-adjusted vegetation index (SAVI). Remote Sens Environ, 1988, 25: 295-309.
[22] Haboudane D, Miller J R, Tremblay N, Zarco-Tejada P J, Dextraze L. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens Environ, 2002, 81: 416-426.
[23] Broge N H, Leblanc E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens Environ, 2001, 76: 156-172.
[24] 岳继博, 杨贵军, 冯海宽. 基于随机森林算法的冬小麦生物量遥感估算模型对比. 农业工程学报, 2016, 32(18): 175-182.
Yue J B, Yang G J, Feng H K. Comparative of remote sensing estimation models of winter wheat biomass based on random forest algorithm. Trans CSAE, 2016, 32(18): 175-182 (in Chinese with English abstract).
[25] Zhang H F, Quost B, Masson M H. Cautious weighted random forests. Expert Syst Appl, 2023, 213: 118883.
[26] 蒋贵印. 基于无人机遥感的春玉米多光谱响应及氮素营养参数反演. 河南理工大学硕士学位论文, 河南焦作, 2020.
Jiang G Y. Multispectral Response of Spring Maize and Inversion of Nitrogen Nutrition Parameters Based on Remote Sensing of UAV. MS Thesis of Henan Polytechnic University, Jiaozuo, Henan, China, 2020 (in Chinese with English abstract).
[27] Ette J S, Ritter T, Vospernik S. Insights in forest structural diversity indicators with machine learning: what is indicated. Biodivers Conserv, 2023, 32: 1019-1046.
[28] Kebede M M, Le Cornet C, Fortner R T. In-depth evaluation of machine learning methods for semi-automating article screening in a systematic review of mechanistic literature. Res Synth Methods, 2023, 14: 156-172.
[29] 刘帅兵, 金秀良, 冯海宽, 聂臣巍, 白怡, 余汛. 基于无人机多源遥感的玉米LAI垂直分布估算. 农业机械学报, 2023, 54(5): 181-193.
Liu S B, Jin X L, Feng H K, Nie C W, Bai Y, Yu X. Vertical distribution estimation of maize LAI using UAV multi-source remote sensing. Trans CSAM, 2023, 54(5): 181-193 (in Chinese with English abstract).
[30] 张炜健, 高宇, 唐彧哲, 张贺景, 杨海波, 闫东, 李斐. 内蒙古中西部玉米临界氮浓度稀释模型的构建与验证. 植物营养与肥料学报, 2022, 28: 2020-2029.
Zhang W J, Gao Y, Tang Y Z, Zhang H J, Yang H B, Yan D, Li F. Construction and validation of critical nitrogen concentration dilution model for maize in central and western Inner Mongolia. J Plant Nutr Fert, 2022, 28: 2020-2029 (in Chinese with English abstract).
[31] 魏鹏飞, 徐新刚, 李中元, 杨贵军, 李振海, 冯海宽, 陈帼, 范玲玲, 王玉龙, 刘帅兵. 基于无人机多光谱影像的夏玉米叶片氮含量遥感估测. 农业工程学报, 2019, 35(8): 126-133.
Wei P F, Xu X G, Li Z Y, Yang G J, Li Z H, Feng H K, Chen G, Fan L L, Wang Y L, Liu S B. Remote sensing estimation of nitrogen content in summer maize leaves based on multispectral images of UAV. Trans CSAE, 2019, 35(8): 126-133 (in Chinese with English abstract).
[32] Richardson J T E. Eta squared and partial eta squared as measures of effect size in educational research. Educ Res Rev-Neth, 2011, 6: 135-147.
[33] 杨勤英. 冬小麦叶面积指数与氮素垂直分布的高光谱反演研究. 安徽大学硕士学位论文, 安徽合肥, 2014.
Yang Q Y. Inversion of Winter Wheat Leaf Area Index and Nitrogen Vertical Distribution with Hyperspectral Data. MS Thesis of Anhui University, Hefei, Anhui, China, 2014 (in Chinese with English abstract).
[34] 温鹏飞. 玉米单叶和冠层氮素营养参数垂直分布反演及遥感监测研究. 西北农林科技大学博士学位论文, 陕西杨凌, 2019.
Wen P F. Monitoring the Vertical Distribution of Nitrogen Status at Leaf and Canopy Scales with Remote Sensing Data in Maize. PhD Dissertation of Northwest A&F University, Yangling, Shaanxi, China, 2019 (in Chinese with English abstract).
[35] Winterhalter L, Mistele B, Schmidhalter U. Assessing the vertical footprint of reflectance measurements to characterize nitrogen uptake and biomass distribution in maize canopies. Field Crops Res, 2012, 129: 14-20.
[36] Li Y B, Song H, Zhou L, Xu Z Z, Zhou G S. Vertical distributions of chlorophyll and nitrogen and their associations with photosynthesis under drought and rewatering regimes in a maize field. Agric Forst Meteor, 2019, 272: 40-54.
[37] Chen B, Huang G M, Lu X J, Gu S H, Wen W L, Wang G T, Chang W S, Guo X Y, Zhao C J. Prediction of vertical distribution of SPAD values within maize canopy based on unmanned aerial vehicles multispectral imagery. Front Plant Sci, 2023, 14: 1253536.
[38] Meivel S, Maheswari S. Remote sensing analysis of agricultural drone. J Indian Soc Remote Sens, 2021, 49: 689-701.
[39] Yang B, Zhu W X, Rezaei E E, Li J, Sun Z G, Zhang J Q. The optimal phenological phase of maize for yield prediction with high- frequency UAV remote sensing. Remote Sens, 2022, 14: 1559.
[40] Osco L P, Junior J M, Ramos A P M, Furuya D E G, Santana D C, Teodoro L P R, Goncalves W N, Baio F H R, Pistori H, Junior C A S, Teodoro P E. Leaf nitrogen concentration and plant height prediction for maize using UAV-based multispectral imagery and machine learning techniques. Remote Sens, 2020, 12: 3237.
[41] Lee H, Wang J F, Leblon B. Using linear regression, random forests, and support vector machine with unmanned aerial vehicle multispectral images to predict canopy nitrogen weight in corn. Remote Sens, 2020, 12: 2071.
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