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作物学报 ›› 2020, Vol. 46 ›› Issue (4): 557-570.doi: 10.3724/SP.J.1006.2020.94045

• 耕作栽培·生理生化 • 上一篇    下一篇

基于高光谱数据的滴灌甜菜叶片全氮含量估算

李宗飞1,苏继霞1,费聪1,李阳阳1,刘宁宁1,戴宇祥1,张开祥1,王开勇1,樊华1,*(),陈兵2,*()   

  1. 1 石河子大学农学院, 新疆石河子 832003
    2 新疆农垦科学院棉花研究所, 新疆石河子 832003
  • 收稿日期:2019-03-20 接受日期:2019-12-26 出版日期:2020-04-12 网络出版日期:2020-01-17
  • 通讯作者: 樊华,陈兵
  • 作者简介:E-mail: shzulizongfei@163.com
  • 基金资助:
    本研究由国家自然科学基金项目(31660360);本研究由国家自然科学基金项目(31771720);自治区研究生科研创新项目(XJGRI2016039);石河子大学国际科技合作推进计划资助(GJHZ201706)

Estimation of total nitrogen content in sugarbeet leaves under drip irrigation based on hyperspectral characteristic parameters and vegetation index

LI Zong-Fei1,SU Ji-Xia1,FEI Cong1,LI Yang-Yang1,LIU Ning-Ning1,DAI Yu-Xiang1,ZHANG Kai-Xiang1,WANG Kai-Yong1,FAN Hua1,*(),CHEN Bing2,*()   

  1. 1 Agronomy College, Shihezi University, Shihezi 832003, Xinjiang, China
    2 Cotton Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832003, Xinjiang, China
  • Received:2019-03-20 Accepted:2019-12-26 Published:2020-04-12 Published online:2020-01-17
  • Contact: Hua FAN,Bing CHEN
  • Supported by:
    This study was supported by the National Natural Science Foundation of China(31660360);This study was supported by the National Natural Science Foundation of China(31771720);the Research and Innovation Projects of Postgraduates in Autonomous Region(XJGRI2016039);the International Science and Technology Cooperation Promotion Plan of Shihezi University(GJHZ201706)

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摘要：

本文旨在明确甜菜叶片全氮含量与高光谱地面植被遥感的定量关系, 建立干旱区甜菜叶片全氮含量精确估测模型, 及时监测甜菜生长状况。本研究选取新疆滴灌甜菜(Beta356)为材料, 利用ASD野外高光谱仪在甜菜叶丛快速生长期、块根膨大期与糖分积累期采集各处理反射光谱, 并同时测定全氮含量, 分析原始光谱反射率及一阶微分光谱反射率与全氮含量的相关性, 并进一步建立光谱特征参数与敏感波段植被指数全氮含量估算模型。结果表明, 光谱特征参数Dr762幂函数下估算模型具有较好估算甜菜叶片全氮含量的能力, 其决定系数R 2 = 0.747, 验证相对误差RE(%)为21.635, 验证均方根误差RMSE为4.914; 通过植被指数与叶片全氮含量建立多种函数估测模型, 其中差值植被指数Dr762-Dr496下一元线性函数具有较好估算甜菜叶片全氮含量的能力, 其决定系数R 2 = 0.794, 验证相对误差RE(%)为23.008, 验证均方根误差为5.372。

关键词: 全氮, 高光谱, 特征参数, 植被指数, 估算模型

Abstract:

The purpose of this paper is to clarify the quantitative relationship between total nitrogen content of sugar beet and high-resolution vegetation remote sensing, to explore the establishment of an optimal estimation model for total nitrogen content of sugar beet, and to monitor the growth of sugar beet. Xinjiang drip-irrigated sugar beet (Beta356) was selected to collect the reflectance spectra of leaf cluster during the leaves rapid growth period, root expansion period and sugar accumulation period by the ASD field hyperspectral apparatus. The total nitrogen content was also measured and the relationship between original spectral reflectance and total nitrogen content was analyzed. According to the correlation between the first-order differential spectral reflectance and total nitrogen content, a total nitrogen content estimation model was established. The model with spectral characteristic parameter Dr762 power function had a good ability to estimate total nitrogen content in leaves of beet, with the determination coefficient, relative error, and root mean square error of 0.747, 21.635, and 4.914, respectively. Various function estimation models were established based on vegetation index and leaf total nitrogen content. The linear function under vegetation index Dr762-Dr496 had better ability to estimate leaf total nitrogen content. Its determinant coefficient, relative error, and root mean square error were 0.794, 23.008, and 5.372, respectively.

Key words: total nitrogen, hyperspectral, characteristic parameters, vegetation index, estimation model

表1

高光谱特征参数及植被指数定义"

光谱特征参数与植被指数
Spectral characteristic parameter and Spectral index		 定义
Definition
红边斜率
Red edge slope (Dr)		 680-760 nm内最大一阶微分光谱值
Maximum first order differential spectrum in 680-760 nm
红边位置
Red edge position (λr)		 680-760 nm内最大的一阶微分光谱值对应的波长
The wavelength corresponding to the largest first-order differential spectrum in 680-760 nm
红边面积
Red edge area (SDr)		 680-760 nm内一阶微分光谱值的总和
The sum of the first-order differential spectral values in 680-760 nm
黄边斜率
Yellow edge slope (Dy)		 560-640 nm内最大一阶微分光谱值
Maximum first order differential spectrum in 560-640 nm
黄边位置
Yellow edge position (Λy)		 560-640 nm内最大一阶微分光谱值对应的波长
The wavelength corresponding to the largest first-order differential spectrum in 560-640 nm
黄边面积
Yellow edge area (SDy)		 560-640 nm内一阶微分光谱值的总和
The sum of the first-order differential spectral values in 560-640 nm
蓝边斜率
Blue edge slope (Db)		 490-530 nm内最大一阶微分光谱值
Maximum first order differential spectrum in 490-530 nm
蓝边位置
Blue edge position (λb)		 490-530 nm内最大一阶微分光谱值对应的波长
The wavelength corresponding to the largest first-order differential spectrum in 490-530 nm
蓝边面积
Blue edge area (SDb)		 490-530 nm内一阶微分光谱值的总和
The sum of the first-order differential spectral values in 490-530 nm
比值植被指数
Ratio vegetation index (RVI)		 Rλ1/Rλ2
差值植被指数
Difference vegetation index (DVI)		 Rλ1- Rλ2
归一化比值植被指数
Normalized ratio vegetation index (NDVI)		 (RNIR-Rred)/(RNIR+Rred)
红边归一化差异指数
Red edge normalized difference index (NDI)		 (DRλ1-DRλ2)/(DRλ1+DRλ2)

图1

不同氮素水平下高光谱反射率与红边 N0: 施氮0 kg hm-2; N75: 施氮75 kg hm-2; N150: 施氮150 kg hm-2; N225: 施氮225 kg hm-2。"

图2

甜菜冠层原始光谱反射率与全氮含量相关性 F0.05: 显著相关(P < 0.05); F0.01: 极显著相关(P < 0.01)。"

图3

甜菜冠层一阶导数光谱与全氮含量相关性 F0.05: 显著相关(P < 0.05); F0.01: 极显著相关(P < 0.01)。"

表2

光谱特征参数及敏感波段植被指数与全氮含量相关性"

光谱特征参数
Spectral characteristic parameter		 相关关系
Correlation		 植被指数
Vegetation index		 相关性Correlation
Dr 0.590** 原始光谱比值植被指数Ratio vegetation index R1104/R767 0.358**
λr 0.497** 原始光谱差值植被指数Difference vegetation index R1104-R767 0.280**
SDr 0.601** 原始光谱归一化植被指数Normalized ratio vegetation index (R1104-R767)/(R1104+R767) 0.364**
Dy 0.074 原始光谱比值植被指数Ratio vegetation index R767/R604 0.652**
Λy 0.089 原始光谱差值植被指数Difference vegetation index R767-R604 0.433**
SDy -0.086 一阶微分比值植被指数Ratio vegetation index Dr1138/Dr762 -0.169
Db 0.072 一阶微分差值植被指数Difference vegetation index Dr1138-Dr762 -0.744**
λb 0.506** 一阶微分归一化植被指Normalized ratio vegetation index (Dr1138-Dr762)/(Dr1138+Dr762) 0.500**
SDb -0.216 一阶微分比值植被指数Ratio vegetation index Dr762/Dr496 0.586**
R604 -0.267** 一阶微分差值植被指数Difference vegetation index Dr762-Dr496 0.721**
R1104 0.680** 红边比值植被指数Red edge ratio vegetation index Dr747/Dr687 0.431**
Dr1138 -0.715** 红边差值植被指数Red edge difference vegetation index Dr747-Dr687 0.589**
Dr762 0.703** 红边归一化差异指数Red edge normalized difference index (Dr747-Dr687)/( Dr747+Dr687) 0.545**

表3

已知高光谱植被指数与全氮含量的相关性"

植被指数
Vegetation index		 计算公式
Calculation formula		 相关关系Correlation 文献来源
Source of literature
RSI R990/R720 0.528** [23]
NDSI (R860-R720)/(R860+R720) 0.582** [23]
NDVI (R790-R670)/(R790+R670) 0.332** [24]
FD-NDVI (R730-R525)/(R730+R525) 0.451** [25]
RENDVI (R750-R705/( R750+ R705) 0.367** [26]
mND705 (R750-R705)/(R750+2R445) 0.360** [27]
GNDVI (R790-R550)/(R790+R550) 0.464** [28]
SAVI 1.5(R870-R680)/(R870+R680+0.5) 0.692** [29]
OSAVI (1+0.16)(R810-R680)/(R810-R680+0.16) 0.633** [30]
MSAVI 0.5{2R800+1-[(2R800+1)2-8(R800-R670)]0.5} 0.623** [31]
DCNI (R720-R700/(R700-R670)/(R720-R670+0.03) 0.460** [32]
CIgreen [(R840-R870)/R550]-1 -0.493** [33]
NINI [lg(1/R1510)-lg(1/R1680)]/[lg(1/R1510)+lg(1/R1680)] 0.553** [34]
TVI 0.5[120(R750-R550)-200(R670-R550)] 0.538** [35]
DSI R800-R680 0.510** [36]

表4

不同光谱特征参数与全氮含量回归关系模型及验证"

高光谱特征参数
Characteristic parameter		 拟合模型 Fitting model 验证模型 Performance model
拟合方程 Equation R2 RE(%) RMSE
R1104 y = 63.88x-8.167 0.516 21.429 4.762
y = -30.27x2+96.39-16.64 0.519 22.299 4.829
y = 58.24x1.296 0.513 20.898 4.766
y = 7.555e2.277x 0.493 22.904 4.898
Dr1138 y = 64.73x-1.144 0.643 19.893 4.874
y = -47.08x2+104.7x-9.162 0.648 20.625 4.945
y = 63.68x1.031 0.643 19.909 4.881
y = 9.629e2.313x 0.615 23.265 5.010
Dr762 y = 157.8x-147 0.770 20.877 5.038
y = -192.3x2+578.8x-377.1 0.772 21.518 5.137
y = 13.96x6.461 0.747 21.635 4.914
y = 0.04302e5.813x 0.740 22.103 4.928

图4

甜菜光谱特征参数Dr762 (一阶微分最大正相关)下全氮含量估测模型验证"

表5

不同植被指数与全氮含量回归关系模型及验证"

高光谱特征参数
Characteristic parameter		 拟合模型 Fitting model 验证模型 Performance model
拟合方程 Equation R2 RE(%) RMSE
SAVI y = 114.7x-105.7 0.541 24.492 5.092
y = 104.5x2-122.1x+28.18 0.544 23.153 4.926
y = 12.39x5.208 0.539 21.878 4.830
y = 0.1489e4.474x 0.535 21.514 4.829
Dr1138-Dr762 y = 49.36x+0.4913 0.732 19.708 4.709
y = -1.864x2+51.27x+0.04145 0.732 19.777 4.721
y = 49.73x0.9814 0.732 19.746 4.715
y = 9.737e1.842x 0.714 20.934 4.656
Dr762-Dr496 y = 12.85x+5.673 0.794 23.008 5.372
y = -2.828x2+21.68x-0.3212 0.804 22.996 5.608
y = 18.64x0.755 0.799 23.025 5.447
y = 11.91e0.4736x 0.759 24.623 5.288

图5

甜菜植被指数Dr1138-Dr762下全氮含量估测模型验证"

图6

甜菜植被指数Dr762-Dr496下全氮含量估测模型验证"

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