欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2025, Vol. 51 ›› Issue (1): 189-206.doi: 10.3724/SP.J.1006.2025.43015

• 耕作栽培·生理生化 • 上一篇    下一篇

基于无人机多光谱数据和氮素空间分异的玉米冠层氮浓度估算

郝琪(), 陈天陆, 王富贵, 王振, 白岚方, 王永强(), 王志刚()   

  1. 内蒙古农业大学农学院, 内蒙古呼和浩特 010010
  • 收稿日期:2024-04-01 接受日期:2024-08-15 出版日期:2025-01-12 网络出版日期:2024-09-02
  • 通讯作者: 王永强,王志刚
  • 作者简介:E-mail: haoqi199807@163.com
  • 基金资助:
    内蒙古自治区科技重大专项(2021ZD0003);国家自然科学基金项目(32160507);内蒙古自治区“科技兴蒙”行动重点专项(NMKJXM202111)

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 Published:2025-01-12 Published online:2024-09-02
  • Contact: WANG Yong-Qiang,WANG Zhi-Gang
  • 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)

RichHTML

1

PDF

103

摘要：

作物冠层氮素营养的遥感诊断对指导作物精准施氮, 提高作物氮效率和产量具有重要意义。本研究针对玉米冠层纵深大影响无人机估算氮浓度精度的问题, 基于2022年和2023年不同氮肥运筹处理下田间无人机多光谱数据和氮浓度实测数据, 分析玉米冠层氮浓度空间分布特征, 并利用随机森林算法确定估算冠层氮浓度的有效叶层。进一步结合随机森林算法和多光谱植被指数构建有效叶层氮浓度估算模型, 最终将有效叶层氮浓度转换到冠层尺度实现冠层氮浓度的估算。结果表明: (1) 九叶展期和大喇叭口期玉米冠层氮浓度表现为上层叶片>中层叶片>下层叶片, 吐丝期和乳熟期表现为中层叶片>上层叶片>下层叶片。(2) 各时期估算冠层氮浓度的有效叶层分别为下层、中层、中层和中层。与支持向量回归模型相比, 随机森林回归估算冠层氮浓度的精度较高。(3) 结合随机森林算法, 基于有效叶层氮浓度估算冠层氮浓度的平均RMSE、NRMSE和MAE分别为0.10%、4.41%和0.07%, 而直接基于植被指数估算冠层氮浓度的平均RMSE、NRMSE和MAE分别为0.19%、9.00%和0.15%。综上, 玉米冠层氮浓度存在空间分异特征, 估算冠层氮浓度时考虑基于随机森林和植被指数估算的有效叶层氮浓度能明显提高冠层氮浓度的估算精度。本研究确定的考虑空间分异的冠层氮浓度估算框架可为玉米氮素营养实时诊断提供理论支撑。

关键词: 玉米, 冠层氮浓度, 氮素空间分异, 多光谱数据, 机器学习

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

表1

2022-2023年试验点0~30 cm耕层土壤养分含量"

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

图1

田间试验布局(A)与无人机RGB影像(B), 各处理施肥罐照片(C) N0、N270、N360分别表示施氮量为0、270 kg hm-2、360 kg hm-2。F3、F5分别表示施氮次数为3次和5次。"

图2

试验地气象条件 A: 2022年土默特右旗5月至9月气象条件; B: 2022年土默特左旗5月至9月气象条件; C: 2023年土默特右旗5月至9月气象条件。"

表2

各处理施肥情况"

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

表3

玉米各生育时期叶片分层方法"

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

图3

无人机(A)搭载多光谱传感器(B)影像采集平台"

表4

多光谱相机与灰板波段信息"

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

表5

用于氮浓度估算的植被指数"

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]

图4

不同施氮处理对玉米冠层氮浓度的影响 A: 2022年土默特右旗; B: 2022年土默特左旗; C: 2023年土默特右旗。缩略词同表2。处理同图1。"

表6

玉米冠层氮浓度多因素方差分析"

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

图5

不同施氮处理对玉米冠层不同层次叶片氮浓度的影响 缩略词同表2。处理同图1。2022年土默特右旗V9、V12、R1和R3玉米冠层不同层次叶片氮浓度变化情况(A~D), 2022年土默特左旗V9、V12、R1和R3玉米冠层不同层次叶片氮浓度变化情况(E~H)和2023年土默特右旗V9、V12、R1和R3玉米冠层不同层次叶片氮浓度变化情况(I~L)。"

表7

玉米冠层不同层次叶片氮浓度多因素方差分析"

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

表8

玉米不同层位叶片氮浓度与冠层氮浓度的回归精度"

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

图6

基于节点纯度增加值的V9 (A)、V12 (B)、R1 (C)和R3 (D)冠层氮浓度有效叶层确定 Increase in node purity代表不同层位叶片氮浓度的重要性, 该值越大表示相应层位叶片氮浓度越重要。缩略词同表2。"

图7

V9 (A)、V12 (B)、R1 (C)和R3 (D)植被指数与玉米冠层氮浓度和有效叶层氮浓度相关性 NCmea、MLNC和LLNC分别代表实测冠层氮浓度、中层叶片氮浓度和下层叶片氮浓度; *和**分别表示在0.05和0.01概率水平显著相关。缩略词同表2。"

图8

随机森林回归和支持向量回归在玉米V9 (A, E)、V12 (B, F)、R1 (C, G)和R3 (D, H)在训练集估算冠层氮浓度的性能 NCpre_VI为直接基于多光谱植被指数的预测冠层氮浓度, NCpre_SNH为考虑氮素空间分异的预测冠层氮浓度。缩略词同表2。"

图9

随机森林回归和支持向量回归在玉米V9 (A, E)、V12 (B, F)、R1 (C, G)和R3 (D, H)在测试集估算冠层氮浓度的性能 缩略词同表2和图8。"

图10

基于无人机影像和冠层氮素空间分异的玉米冠层氮浓度空间分布 2022年土默特右旗玉米V9 (A)、V12 (B)、R1 (C)和R3 (D)冠层氮浓度田间分布, 2023年土默特右旗玉米V9 (E)、V12 (F)、R1 (G)和R3 (H)冠层氮浓度田间分布和2022年土默特左旗玉米V9 (I)、V12 (J)、R1 (K)和R3 (L)冠层氮浓度田间分布。处理同图1。NC表示冠层氮浓度。"

[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.
[1] 钱玉平, 宿兵兵, 高吉星, 阮粉花, 李亚伟, 茅林春. 玉米大豆间作对喀斯特区土壤理化性质及微生物碳代谢特征的影响[J]. 作物学报, 2025, 51(1): 273-284.
[2] 叶靓, 朱叶琳, 裴琳婧, 张思颖, 左雪倩, 李正真, 刘芳, 谭静. 联合全基因组关联和转录组分析筛选玉米拟轮枝镰孢穗腐病的抗性候选基因[J]. 作物学报, 2024, 50(9): 2279-2296.
[3] 孙照华, 任昊, 王洪章, 王子强, 姚海燕, 辛爱美, 赵斌, 张吉旺, 任佰朝, 刘鹏. 叶面喷施硅制剂对滨海盐碱地夏玉米叶片光合性能及籽粒产量的影响[J]. 作物学报, 2024, 50(9): 2383-2395.
[4] 曹晓晴, 祁显涛, 刘昌林, 谢传晓. 编辑ZmCCT10、ZmCCT9、ZmGhd7基因的串联DsRed荧光表达盒的CRISPR/Cas9系统的构建及验证[J]. 作物学报, 2024, 50(8): 1961-1970.
[5] 刘陈, 王昆昆, 廖世鹏, 杨佳群, 丛日环, 任涛, 李小坤, 鲁剑巍. 氮肥用量对玉米-油菜和水稻-油菜轮作模式下油菜产量及氮素吸收利用的影响[J]. 作物学报, 2024, 50(8): 2067-2077.
[6] 刘宸铭, 赵克勇, 悦曼芳, 赵延明, 吴忠义, 张春. 玉米转录因子ZmEREB180调控根系生长发育及耐逆的功能研究[J]. 作物学报, 2024, 50(8): 1920-1933.
[7] 刘爽, 李珅, 王东梅, 沙小茜, 何冠华, 张登峰, 李永祥, 刘旭洋, 王天宇, 黎裕, 李春辉. 基于大刍草渗入系的玉米抗旱优异等位基因挖掘[J]. 作物学报, 2024, 50(8): 1896-1906.
[8] 梁璐, 周宝元, 高卓晗, 王瑞, 王新兵, 赵明, 李从锋. 不同品种玉米根-冠生长对土壤紧实胁迫的差异性响应特征[J]. 作物学报, 2024, 50(8): 2053-2066.
[9] 郭思语, 赵克勇, 代正罡, 邹华文, 吴忠义, 张春. 玉米N-乙酰转移酶ZmNAT1基因响应非生物胁迫的功能分析[J]. 作物学报, 2024, 50(8): 2001-2013.
[10] 王蕊, 孙擘, 张云龙, 张茗起, 范亚明, 田红丽, 赵怡锟, 易红梅, 匡猛, 王凤格. 叶绿体标记在玉米种质资源快速分组中的应用分析[J]. 作物学报, 2024, 50(7): 1867-1876.
[11] 方宇辉, 齐学礼, 李艳, 张煜, 彭超军, 华夏, 陈艳艳, 郭瑞, 胡琳, 许为钢. 强光胁迫对转玉米C4型ZmPEPC+ZmPPDK基因小麦光合和生理特性的影响[J]. 作物学报, 2024, 50(7): 1647-1657.
[12] 王菲儿, 郭瑶, 李盼, 韦金贵, 樊志龙, 胡发龙, 范虹, 何蔚, 殷文, 陈桂平. 绿洲灌区增密对水氮减量玉米产量的补偿机制[J]. 作物学报, 2024, 50(6): 1616-1627.
[13] 折萌, 郑登俞, 柯照, 吴忠义, 邹华文, 张中保. 玉米ZmGRAS13基因的克隆及功能研究[J]. 作物学报, 2024, 50(6): 1420-1434.
[14] 郑雪晴, 王兴荣, 张彦军, 龚佃明, 邱法展. 玉米果穗相关性状QTL定位及重要候选基因分析[J]. 作物学报, 2024, 50(6): 1435-1450.
[15] 王永亮, 胥子航, 李申, 梁哲铭, 白炬, 杨治平. 不同覆盖措施对土壤水热状况及春玉米产量和水分利用效率的影响[J]. 作物学报, 2024, 50(5): 1312-1324.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备15008789号-2