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作物学报

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

所属专题： 玉米：耕作栽培·生理生化

• 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: *王志刚, E-mail: imauwzg@163.com; 王永强, E-mail: wangyongqiang@nwafu.edu.cn
  • 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: *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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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。"

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