欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2020, Vol. 46 ›› Issue (12): 1979-1990.doi: 10.3724/SP.J.1006.2020.04023

• 耕作栽培·生理生化 • 上一篇    下一篇

不同光谱指数反演马铃薯叶片氮累积量的研究

韩康(), 于静, 石晓华, 崔石新, 樊明寿*()   

  1. 内蒙古农业大学农学院, 内蒙古呼和浩特 010019
  • 收稿日期:2020-02-05 接受日期:2020-08-19 出版日期:2020-09-02 网络出版日期:2020-09-02
  • 通讯作者: 樊明寿
  • 基金资助:
    国家自然科学基金项目(31960637);内蒙古自治区自然科学基金项目(2019BS03021)

Inversion of nitrogen accumulation in potato leaf with different spectral indices

HAN Kang(), YU Jing, SHI Xiao-Hua, CUI Shi-Xin, FAN Ming-Shou*()   

  1. College of Agronomy, Inner Mongolia Agricultural University, Hohhot 010019, Inner Mongolia, China
  • Received:2020-02-05 Accepted:2020-08-19 Published:2020-09-02 Published online:2020-09-02
  • Contact: FAN Ming-Shou
  • Supported by:
    National Natural Science Foundation of China(31960637);Natural Science Foundation of Inner Mongolia(2019BS03021)

RichHTML

6

PDF

373

摘要：

光谱指数作为光谱衍生参数, 可用于反映作物叶片氮累积量状况, 但其因环境与作物而有所不同。本研究在内蒙古察右中旗和杭锦旗, 以马铃薯克新1号与夏波蒂品种为研究对象, 于2016—2018年进行了田间试验, 并在马铃薯生育期间, 用手持式光谱仪(SVC HR-1024i)获取了马铃薯冠层地面观测光谱信息。在前人光谱指数算法的基础上, 通过相关分析对比了22种光谱指数与马铃薯叶片氮累积量(LNA)之间的相关关系, 并利用线性与非线性回归分析建立了马铃薯关键生育时期的氮素营养诊断模型。结果表明, (1)红边区域是反演马铃薯叶片氮素累积量的主要波段, 以715、720、726、734、747 nm构成的Vogelmann红边指数2 (VOG2)、Vogelmann红边指数3 (VOG3)为内蒙古地区马铃薯LNA的敏感光谱指数。(2)苗期、块茎形成期与全生育时期VOG3与LNA的关系符合二次项模型(R 2>0.75), 以此可以较好地估算不同施氮水平下的马铃薯LNA状况。(3)上述3个模型的均方根误差(RMSE)范围分别为4.04~6.69、9.45~10.89、9.17~13.45 kg hm -2, 生育时期对马铃薯叶片氮累积量监测模型的准确性影响较大, 生育后期模型的预测性能变差, 但全生育时期监测模型准确度较高, 因此生育前期分阶段建模与生育后期统一建模可以准确估算马铃薯氮素营养状况, 为光谱指数在马铃薯氮素营养诊断应用提供了理论依据与方法。

关键词: 马铃薯, 叶片氮累积量, 光谱指数, 监测模型

Abstract:

As an important derivative parameter of optical spectrum, spectral index could reflect the leaf nitrogen accumulation of crops. However, the sensitive spectral index varies with different environments and crops. In order to obtain the sensitive spectral index for potato in Inner Mongolia, field experiments were conducted in Chayouzhongqi and Hangjinqi of Inner Mongolia from 2016 to 2018, and during the potato growth period, the canopy spectrum information of potato cultivars Kexin 1 and Shepody was obtained using a handheld spectrometer (SVC HR-1024i). Based on the previous spectral indices algorithm, the correlation coefficients between the leaves nitrogen accumulation of potato (LNA) and each of the 22 spectral indices were compared, and the nitrogen nutrition diagnosis models of potato at critical growth stages were established using linear and nonlinear regression analysis. The results were as follows: (1) the red edge area was the main spectral band for inverting the LNA of potato, and Vogelmann red edge index 2 (VOG2), Vogelmann red edge index 3 (VOG3) were the sensitive spectral indices for potato LNA in Inner Mongolia, which composed of 715, 720, 726, 734, and 747 nm of spectral bands. (2) At the seedling stage, tuber formation stage or whole growth stage, the quadratic regression models (R 2 > 0.75) between VOG3 and LNA could estimate better the LNA of potato under different nitrogen levels using VOG3. (3) The root mean square error (RMSE) of the models was 4.04-6.69, 9.45-10.89, 9.17-13.45 kg hm -2, indicating the accuracy of using the models to predict potato LNA varies with potato growth stage, and it was lower at late growth stages, while it is higher for whole growth duration. In summary, the staged modeling for potato early growth period and the unified modeling for potato later growth period could accurately estimate the potato LNA, which provides a theoretical basis and method for the application of spectral indices in the nitrogen nutrition diagnosis of potato.

Key words: potato, leaf nitrogen accumulation, spectral index, monitoring models

表1

氮肥施用方案"

处理
Treatment		 基肥
Base fertilizer		 追肥Top dressing
出苗后10 d
10 days after emergence		 出苗后25 d
25 days after emergence
N0 0 0 0
N1 75 15 60
N2 75 45 180
N3 75 118.5 256.5
N4 75 150 375

表2

田间试验概况"

试验
Experiment		 年份
Year		 地点
Location		 品种
Variety		 播种日期
Sowing date
(month/day)		 收获日期
Harvest date
(month/day)		 出苗日期
Emergence date
(month/day)		 数据采集日期
Data collection date
(month/day)
Exp.1 2016 察右中旗
Chayouzhongqi		 克新1号
Kexin 1		 5/27 9/24 6/18 7/8, 7/23, 8/7, 8/22
Exp.2 2016 察右中旗
Chayouzhongqi		 夏波蒂
Shepody		 5/27 9/24 6/18 7/8, 7/23, 8/7, 8/22
Exp.3 2016 杭锦旗
Hangjinqi		 克新1号
Kexin 1		 5/27 9/24 6/20 7/10, 7/25, 8/9, 8/24
Exp.4 2017 察右中旗
Chayouzhongqi		 克新1号
Kexin 1		 5/11 9/20 6/13 7/3, 7/18, 8/2, 8/17
Exp.5 2018 察右中旗
Chayouzhongqi		 克新1号
Kexin 1		 5/3 9/28 6/10 6/30, 7/15, 7/30, 8/14

表3

SVC HR-1024i的光谱分辨率与采样间隔"

波长范围
Wavelength range (nm)		 光谱分辨率
Spectral resolution (nm)		 光谱采样间隔
Spectral sampling interval (nm)
350-1000 ≤3.3 ≤1.5
1000-1890 ≤9.5 ≤3.8
1890-2500 ≤6.5 ≤2.5

表4

光谱指数公式"

光谱指数
Spectral index		 计算公式
Calculation formula		 参考文献
References
1 Simple Ratio Index (SRI) R800 ? R680 Sim et al. [13]
2 Normalized Difference Vegetation (NDVI) (R800 - R680) ? (R800 + R680) Rouse et al. [14]
3 Difference Vegetation Index (DVI) R800 - R680 Richardson et al. [15]
4 Structure Insensitive Pigment Index (SIPI) (R800 - R445) ? (R800 + R680) Penuelas et al. [16]
5 Modified Red Edge Normalized Difference
Vegetation (mND705)		 (R750 - R705) ? (R750 + R705 - 2 × R445) Sims et al. [13]
6 Modified Simple Ratio Index (mSR705) (R750 - R445) ? (R705 - R445) Sims et al. [13]
7 Photochemical Reflectance Index (PRI) (R531 - R570) ? (R570 + R531) Gamon et al. [17]
8 Plant Senescence Reflectance Index (PSRI) (R680 - R500) ? R750 Merzlyak et al. [18]
9 The MERIS terrestrial Chlorophyll Index (MTCI) (R750 - R710) ? (R710 - R680) Dash et al. [19]
10 Modified Chlorophyll Absorption in Refectance
Index (MCARI)		 [(R700 - R670) - 0.2 × (R700 - R550)] × (R700 ? R670) Dash et al. [19]
11 Optimized Soil-adjusted Vegetation Index (OSAVI) (1 + 0.16) × (R800 - R670) ? (R800 + R670 + 0.16) Rondeaux et al. [20]
12 Transformed Chlorophyll Absorption In
Reflectance Index (TCARI)		 3 × [(R700 - R670) - 0.2 × (R700 - R550)] × (R700 ? R670) Haboudane et al. [21]
13 Enhanced Vegetation Index (EVI) 2.5 × (R800 - R680) ? (R800 + 6 × R680 - 7.5 × R450 + 1) Huete et al. [22]
14 Atmospherically Resistant Vegetation Index (ARVI) (R800 - 2 × R680 + R450) ? (R800 + 2 × R680 - R450) Kaufman et al. [23]
15 705nm Normalized Difference Vegetation (NDVI705) (R750 - R705) ? (R750 + R705) Gitelson et al. [24]
16 Vogelmann Red Edge Index 1 (VOG1) R740 ? R720 Zarco-Tejada et al. [25]
17 Vogelmann Red Edge Index 2 (VOG2) (R734 - R747) ? (R715 + R726) Zarco-Tejada et al. [25]
18 Vogelmann Red Edge Index 3 (VOG3) (R734 - R747) ? (R715 + R720) Zarco-Tejada et al. [25]
19 Carotenoid Reflectance Index 1 (CRI1) 1 ? R510 - 1 ? R550 Gitelson et al. [26]
20 Carotenoid Reflectance Index 2 (CRI2) 1 ? R510 - 1 ? R700 Gitelson et al. [26]
21 Anthocyanin Reflectance Index 1 (ARI1) 1 ? R550 - 1 ? R700 Gitelson et al. [27]
22 Anthocyanin Reflectance Index 2 (ARI2) R800 × (1 ? R550 - 1 ? R700) Gitelson et al. [28]

表5

建模集与验证集的样本数量"

生育时期
Growth stage		 样本数量Number of samples
建模集Modeling set 验证集Validation set
苗期Seedling stage 45 30
块茎形成期Tuber formation stage 45 30
块茎膨大期Tuber expansion stage 45 30
淀粉积累期Starch accumulation stage 45 30
全生育时期Total growth stages 180 120

图2

不同施氮水平对叶片氮累积量的影响(Exp.1) LNA为叶片氮累积量。处理同表1。不同小写字母表示在0.05水平上显著差异。"

图3

不同施氮水平对冠层反射率的影响 a: 全波段; b: 可见光波段; c: 红边波段。处理同表1。"

表6

光谱指数与叶片氮累积量的回归模型"

生育时期
Growth stage		 监测模型方程式
Monitoring model equation		 R2
苗期Seedling stage LNA = - 269.271 × VOG3 - 16.5 0.882**
LNA = 1976.733 × VOG32 + 211.143 × VOG3 + 11.236 0.913**
LNA = 1.712 × e-17.519 × VOG3 0.908**
块茎形成期Tuber formation stage LNA = - 323.22 × VOG3 - 21.257 0.771**
LNA = 1393.778 × VOG32 + 162.368×VOG3 + 17.484 0.790**
LNA = 4.841 × e-10.411 × VOG3 0.740**
块茎膨大期Tuber expansion stage LNA = - 403.678 × VOG3 - 34.233 0.723**
LNA = 1020.908 × VOG32 - 20.441 × VOG3 - 0.711 0.741**
LNA = 4.249 × e-11.184 × VOG3 0.741**
淀粉积累期Starch accumulation stage LNA = - 279.991 × VOG2 - 8.249 0.550**
LNA = - 654.51 × VOG22 - 464.961 × VOG2 - 20.102 0.555**
LNA = 4.894 × e-11.837 × VOG2 0.570**
全生育时期Total growth stages LNA = - 328.711 × VOG3 - 21.683 0.765**
LNA = 832.343 × VOG32 - 46.576 × VOG3 - 0.242 0.778**
LNA = 3.867 × e-11.534 × VOG3 0.773**

图5

光谱指数与叶片氮累积量的关系 LNA为叶片氮累积量。a: 苗期; b: 块茎形成期; c: 全生育时期。**表示在0.01水平上显著差异。"

表7

马铃薯叶片氮累积量监测模型的检验"

生育时期
Growth stage		 均方根误差RMSE (kg hm-2)
Exp.2 Exp.3
苗期Seedling stage 6.69 4.04
块茎形成期Tuber formation stage 9.45 10.89
块茎膨大期Tuber expansion stage 13.38 9.12
淀粉积累期Starch accumulation stage 16.18 14.13
全生育时期Total growth stages 13.45 9.17

图6

叶片氮累积量估测值与观察值之间的关系 a: 苗期; b: 块茎形成期; c: 块茎膨大期; d: 淀粉积累期; e: 全生育时期。"

图4

光谱指数与叶片氮累积量的相关性 a: 苗期; b; 块茎形成期; c块茎膨大期; d: 淀粉积累期; e: 全生育时期。缩写同表4。"

图1

试验地位置及试验小区分布 N0~N4分别代表0、150、300、450、600 kg hm-2 的氮肥水平; a、b、c为3次重复。"

[1] 秦永林, 于静, 陈杨, 贾立国 , 苏亚拉其其格, 樊明寿. 内蒙古灌溉马铃薯施肥现状及肥料利用效率. 中国蔬菜, 2019, ( 11):75-79.
Qin Y L, Yu J, Chen Y, Jia L G , Suyala Q Q G, Fan M S. Situation of fertilization and fertilizer use efficiency on irrigated potato in Inner Mongolia. China Veget, 2019, ( 11):75-79 (in Chinese with English abstract).
[2] 于静, 李斐, 秦永林, 樊明寿 . 应用主动作物冠层传感器对马铃薯氮素营养诊断. 光谱学与光谱分析, 2013,33:3092-3097.
Yu J, Li F, Qin Y L, Fan M S , Active crop canopy sensor-based nitrogen diagnosis for potato. Spectr Spectr Anal, 2013,33:3092-3097 (in Chinese with English abstract).
[3] Li R, Chen J H, Qin Y L, Fan M S . Possibility of using a SPAD chlorophyll meter to establish a normalized threshold index of nitrogen status in different potato cultivars. J Plant Nutr, 2019,42:834-841.
doi: 10.1080/01904167.2019.1584215
[4] 姚霞, 朱艳, 冯伟, 田永超, 曹卫星 . 监测小麦叶片氮积累量的新高光谱特征波段及比值植被指数. 光谱学与光谱分析, 2009,29:2191-2195.
Yao X, Zhu Y, Feng W, Tian Y C, Cao W X . Exploring novel hyperspectral band and key Index for leaf nitrogen accumulation in wheat. Spectr Spectr Anal, 2009,29:2191-2195 (in Chinese with English abstract).
[5] Zhu Y, Yao X, Tian Y C, Liu X J, Cao W X . Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice. Int J Appl Earth Observ Geoinf, 2008,10:1-10.
doi: 10.1016/j.jag.2007.02.006
[6] Mistele B, Schmidhalter U . Estimating the nitrogen nutrition index using spectral canopy reflectance measurements. Eur J Agron, 2008,29:184-190.
doi: 10.1016/j.eja.2008.05.007
[7] 薛利红, 曹卫星, 罗卫红, 张宪 . 小麦叶片氮素状况与光谱特性的相关性研究. 植物生态学报, 2004,28:172-177.
Xue L H, Cao W X, Luo W H, Zhang X . Correlation between leaf nitrogen status and canopy spectral characteristics in wheat. Chin J Plant Ecol, 2004,28:172-177 (in Chinese with English abstract).
[8] 薛利红, 曹卫星, 罗卫红, 姜东, 孟亚利, 朱艳 . 基于冠层反射光谱的水稻群体叶片氮素状况监测. 中国农业科学, 2003,36:807-812.
Xue L H, Cao W X, Luo W H, Jiang D, Meng Y L, Zhu Y . Diagnosis of nitrogen status in rice leaves with the canopy spectral reflectance. Sci Agric Sin, 2003,36:807-812 (in Chinese with English abstract).
[9] Xue L H, Cao W X, Luo W H, Dai T B, Zhu Y . Monitoring leaf nitrogen status in rice with canopy spectal reflectance. Agron J, 2004,96:135-142.
doi: 10.2134/agronj2004.0135
[10] 周冬琴, 朱艳, 田永超, 姚霞, 曹卫星 . 以冠层反射光谱监测水稻叶片氮积累量的研究. 作物学报, 2006,32:1316-1322.
Zhou D Q, Zhu Y, Tian Y C, Yao X, Cao W X . Monitoring leaf nitrogen accumulation with canopy spectral reflectance in rice. Acta Agron Sin, 2006,32:1316-1322 (in Chinese with English abstract).
[11] 冯伟, 朱艳, 姚霞, 田永超, 庄森, 曹卫星 . 小麦氮素积累动态的高光谱监测. 中国农业科学, 2008,41:1937-1946.
Feng W, Zhu Y, Yao X, Tian Y C, Zhuang S, Cao W X . Monitoring plant nitrogen accumulation dynamics with hyperspectral remote sensing in wheat. Sci Agric Sin, 2008,41:1937-1946 (in Chinese with English abstract).
[12] 姚霞, 刘小军, 王薇, 田永超, 曹卫星, 朱艳 . 基于减量精细采样法估算小麦叶片氮积累量的最佳归一化光谱指数. 应用生态学报, 2010,21:3175-3182.
Yao X, Liu X J, Wang W, Tian Y C, Cao W X, Zhu Y . Estimation of optimum normalized difference spectral index for nitrogen accumulation in wheat leaf based on reduced precise sampling method. Chin J Appl Ecol, 2010,21:3175-3182 (in Chinese with English abstract).
[13] Sim D A, Gamon J A . Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ, 2002,81:331-354.
[14] Rouse J W, Haas R H, Schel J A, Deering D W . Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA/GSFC Final Report, NASA, 1974. pp 371-375.
[15] Richardson A J, Wiegand C L . Distinguishing vegetation from soil background information. Photogramm Eng Remote Sens, 1997,43:1541-1552.
[16] Penuelas J, Baret F, Filella I . Semi-empirical indices to assess carotenoid/chlorophyll a ratio from leaf spectral reflectance. Photosynthet, 1995,31:221-230.
[17] Gamon J A, Penuelas J, Field G B . A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens Environ, 1992,41:35-44.
doi: 10.1016/0034-4257(92)90059-S
[18] Merzlyak M N, Gitelson A A, Chivkunova O B, Rakitin V Y . Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol Plant, 1999,106:135-141.
[19] Dash J, Curran P J . The MERIS terrestrial chlorophyll index. Int J Remote Sens, 2004,25:5403-5413.
doi: 10.1080/0143116042000274015
[20] Rondeaux G, Steven M, Baret F . Optimization of soil-adjusted vegetation indices. Remote Sens Environ, 1996,55:95-107.
doi: 10.1016/0034-4257(95)00186-7
[21] 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.
doi: 10.1016/S0034-4257(02)00018-4
[22] Huete A R, Liu H, Batchily K, Leeuwen W . A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens Environ, 1997,59:440-451.
doi: 10.1016/S0034-4257(96)00112-5
[23] Kaufman Y J, Tanre D . Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: from AVHRR to EOS-MODIS. Remote Sens Environ, 1996,55:65-79.
doi: 10.1016/0034-4257(95)00193-X
[24] 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 Plant Physiol, 1994,143:286-292.
doi: 10.1016/S0176-1617(11)81633-0
[25] Zarco-Tejada P J, Miller J R, Noland T L, Mohammed G H . Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Trans Geosci Remote Sens, 2001,39:1491-1507.
doi: 10.1109/36.934080
[26] Gitelson A A, Zur Y, Chivkunova O B, Merzlyak M N . Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem Photobiol, 2002,75:272-281.
doi: 10.1562/0031-8655(2002)075<0272:accipl>2.0.co;2 pmid: 11950093
[27] Gitelson A A, Merzlyak M N, Chivkunova O B . Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochem Photobiol, 2001,71:38-45.
[28] Gitelson A A, Merzlyak M N . Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. J Plant Physiol, 1996,148:494-500.
doi: 10.1016/S0176-1617(96)80284-7
[29] 张苏江, 陈庆波 . 数据统计分析软件SPSS的应用(五)——相关分析与回归分析. 畜牧与兽医, 2003,35(9):16-18.
Zhang S J, Chen Q B . Application of data statistical analysis software SPSS (5)—correlation analysis and regression analysis. Animal Husb Vet Med, 2003,35(9):16-18 (in Chinese with English abstract).
[30] Wang S Q, Li W D, Li J, Liu X S . Prediction of soil texture using FT-NIR spectroscopy and PXRF spectrometry with data fusion. Soil Sci, 2013,178:626-638.
doi: 10.1097/SS.0000000000000026
[31] 强生才, 张富仓, 向友珍, 张燕, 闫世程, 邢英英 . 关中平原不同降雨年型夏玉米临界氮稀释曲线模拟及验证. 农业工程学报, 2015,31(17):168-175.
Qiang S C, Zhang F C, Xiang Y Z, Zhang Y, Yan S C, Xing Y Y . Simulation and validation of critical nitrogen dilution curve for summer maize in Guanzhong plain during different rainfall years. Trans CSAE, 2015,31(17):168-175 (in Chinese with English abstract).
[32] 肖艳芳, 周德民, 宫辉力, 赵文吉 . 冠层反射光谱对植被理化参数的全局敏感性分析. 遥感学报, 2015,19:368-374.
Xiao Y F, Zhou D M, Gong H L, Zhao W J . Sensitivity of canopy reflectance to biochemical and biophysical variables. J Remote Sens, 2015,19:368-374 (in Chinese with English abstract).
[33] Filella I, Serrano L, Serra J, Penuelas J . Evaluating wheat nitrogen status with canopy reflectance indices and discriminate analysis. Crop Sci, 1995,35:1400-1405.
doi: 10.2135/cropsci1995.0011183X003500050023x
[34] 陈鹏飞, Nicolas T, 王纪华, Philippe V, 黄文江, 李保国 . 估测作物冠层生物量的新植被指数的研究. 光谱学与光谱分析, 2010,30:512-517.
Chen P F, Nicolas T, Wang J H, Philippe V, Huang W J, Li B G . New index for crop canopy fresh biomass estimation. Spectr Spectr Anal, 2010,30:512-517 (in Chinese with English abstract).
[35] 付元元, 王纪华, 杨贵军, 宋晓宇, 徐新刚, 冯海宽 . 应用波段深度分析和偏最小二乘回归的冬小麦生物量高光谱估算. 光谱学与光谱分析, 2013,33:1315-1319.
Fu Y Y, Wang J H, Yang G J, Song X Y, Xu X G, Feng H K . Band depth analysis and partial least square regression based winter wheat biomass estimation using hyperspectral measurements. Spectr Spectr Anal, 2013,33:1315-1319 (in Chinese with English abstract).
[36] 刘冰峰, 李军, 贺佳, 师祖娇 . 基于高光谱植被指数的夏玉米地上干物质量估算模型研究. 农业机械学报, 2016,47(3):254-262.
Liu B F, Li J, He J, Shi Z J . Estimation models of above-ground dry matter accumulation of summer maize based on vegetation indexes of hyperspectral remote sensing. Trans CSAM, 2016,47(3):254-262 (in Chinese with English abstract).
[37] Tian Y C, Yao X, Yang J, Cao W X, Hammaway D B, Zhu Y . Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance. Field Crops Res, 2011,120:299-310.
doi: 10.1016/j.fcr.2010.11.002
[38] Wang W, Yao X, Yao X F, Tian Y C, Liu X J, Ni J, Cao W X, Zhu Y . Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat. Field Crops Res, 2012,129:90-98.
doi: 10.1016/j.fcr.2012.01.014
[39] Shiratsuchi L, Ferguson R, Shanahan J, Adamchuk V, Rundquist D, Marx D, Slater G . Water and nitrogen effects on active canopy sensor vegetation indices. Agron J, 2011,103:1815-1826.
doi: 10.2134/agronj2011.0199
[40] 贾方方 . 不同种植密度烟草叶面积指数的高光谱估测模型. 中国烟草科学, 2017,38(4):37-43.
Jia F F . Estimating model for leaf area index of tobacco via hyperspectral reflectance at different planting densities. Chin Tob Sci, 2017,38(4):37-43 (in Chinese with English abstract).
[41] 王宏博, 赵梓淇, 林毅, 冯锐, 李丽光, 赵先丽, 温日红, 魏楠, 姚欣, 张玉书 . 基于线性回归算法的春玉米叶面积指数的冠层高光谱反演研究. 光谱学与光谱分析, 2017,37:1489-1496.
Wang H B, Zhao Z Q, Lin Y, Feng R, Li L G, Zhao X L, Wen R H, Wei N, Yao X, Zhang Y S . Leaf area index estimation of spring maize with canopy hyperspectral data based on linear regression algorithm. Spectr Spectr Anal, 2017,37:1489-1496 (in Chinese with English abstract).
[42] 吕晓, 殷红, 蒋春姬, 张兵兵, 战莘晔, 辛明月, 张美玲 . 基于高光谱遥感的不同品种花生冠层叶面积指数的通用估算模型. 中国农业气象, 2016,37:720-727.
Lyu X, Yin H, Jiang C J, Zhang B B, Zhan S Y, Xin M Y, Zhang M L . General estimation model of peanut canopy LAI based on hyperspectral remote sensing. Chin J Agrometeorol, 2016,37:720-727 (in Chinese with English abstract).
[43] 李云梅, 倪绍祥, 黄敬峰 . 高光谱数据探讨水稻叶片叶绿素含量对叶片及冠层光谱反射特性的影响. 遥感技术与应用, 2003,18(1):1-5.
Li Y M, Ni S X, Huang J F . Discussing effects of different chlorophyll concentration to leaf and canopy reflectance by hyperspectral data. Remote Sens Technol Appl, 2003,18(1):1-5 (in Chinese with English abstract).
[44] 李恒凯, 欧彬, 刘雨婷 . 基于高光谱参数的竹叶叶绿素质量分数估算模型. 东北林业大学学报, 2017,45(5):44-48.
Li H K, Ou B, Liu Y T . Estimation models of chlorophyll content of bamboo leaves based on spectral parameter. J Northeast For Univ, 2017,45(5):44-48 (in Chinese with English abstract).
[45] 焦红波, 金继业, 刘振民, 查勇, 李四海, 李云梅, 黄家柱 . 湖泊水体叶绿素a含量估算的波段宽度变化影响分析——以太湖为例. 地球信息科学, 2008,10:6787-6791.
Jiao H B, Jin J Y, Liu Z M, Zha Y, Li S H, Li Y M, Huang J Z . The influence of bandwidth’s variety on estimating chlorophyll-a concentration of lake waters: Taking Taihu Lake as an example. Geoinf Sci, 2008,10:6787-6791 (in Chinese with English abstract).
[46] 孙永华, 张冬冬, 田杰, 黄锦 . 基于高光谱的湿地植被冠层叶绿素反演研究. 河北师范大学学报(自然科学版), 2018,42(2):157-164.
Sun Y H, Zhang D D, Tian J, Huang J . Inversion of vegetation canopy chlorophyll in wet land based on hyperspectal data. J Hebei Norm Univ (Nat Sci Edn), 2018,42(2):157-164 (in Chinese with English abstract).
[47] Cho M A, Skidmore A K . A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sens Environ, 2006,101:181-193.
doi: 10.1016/j.rse.2005.12.011
[48] Josep P, Gamon J A, Griffin K L, Griffin K L, Field C B . Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sens Environ, 1993,46:110-118.
doi: 10.1016/0034-4257(93)90088-F
[49] 李国强, 吴士文, 郑国清, 张学治, 冯晓, 张杰, 胡峰 . 基于冠层反射光谱的夏玉米叶片氮积累量估测. 中国农学通报, 2014,30(3):85-90.
Li G Q, Wu S W, Zheng G Q, Zhang X Z, Feng X, Zhang J, Hu F . Monitoring leaf nitrogen accumulation in summer maize with Canopy reflectance spectra. Chin Agric Sci Bull, 2014,30(3):85-90 (in Chinese with English abstract).
[50] 秦永林, 樊明寿 . 马铃薯氮素管理策略. 中国蔬菜, 2011, ( 18):1-5.
Qin Y L, Fan M S . Strategy for potato nitrogen management. China Veget, 2011, ( 18):1-5 (in Chinese with English abstract).
[1] 王海波, 应静文, 何礼, 叶文宣, 涂卫, 蔡兴奎, 宋波涛, 柳俊. rDNA和端粒重复序列鉴定马铃薯和茄子体细胞杂种染色体丢失和融合[J]. 作物学报, 2022, 48(5): 1273-1278.
[2] 石艳艳, 马志花, 吴春花, 周永瑾, 李荣. 垄作沟覆地膜对旱地马铃薯光合特性及产量形成的影响[J]. 作物学报, 2022, 48(5): 1288-1297.
[3] 冯亚, 朱熙, 罗红玉, 李世贵, 张宁, 司怀军. 马铃薯StMAPK4响应低温胁迫的功能解析[J]. 作物学报, 2022, 48(4): 896-907.
[4] 张霞, 于卓, 金兴红, 于肖夏, 李景伟, 李佳奇. 马铃薯SSR引物的开发、特征分析及在彩色马铃薯材料中的扩增研究[J]. 作物学报, 2022, 48(4): 920-929.
[5] 谭雪莲, 郭天文, 胡新元, 张平良, 曾骏, 刘晓伟. 黄土高原旱作区马铃薯连作根际土壤微生物群落变化特征[J]. 作物学报, 2022, 48(3): 682-694.
[6] 余慧芳, 张卫娜, 康益晨, 范艳玲, 杨昕宇, 石铭福, 张茹艳, 张俊莲, 秦舒浩. 马铃薯CrRLK1Ls基因家族的鉴定及响应晚疫病菌信号的表达分析[J]. 作物学报, 2022, 48(1): 249-258.
[7] 荐红举, 尚丽娜, 金中辉, 丁艺, 李燕, 王季春, 胡柏耿, Vadim Khassanov, 吕典秋. 马铃薯PIF家族成员鉴定及其对高温胁迫的响应分析[J]. 作物学报, 2022, 48(1): 86-98.
[8] 许德蓉, 孙超, 毕真真, 秦天元, 王一好, 李成举, 范又方, 刘寅笃, 张俊莲, 白江平. 马铃薯StDRO1基因的多态性鉴定及其与根系性状的关联分析[J]. 作物学报, 2022, 48(1): 76-85.
[9] 张骁, 闫岩, 王文辉, 郑恒彪, 姚霞, 朱艳, 程涛. 基于小波分析的水稻籽粒直链淀粉含量高光谱预测[J]. 作物学报, 2021, 47(8): 1563-1580.
[10] 唐锐敏, 贾小云, 朱文娇, 印敬明, 杨清. 马铃薯热激转录因子HsfA3基因的克隆及其耐热性功能分析[J]. 作物学报, 2021, 47(4): 672-683.
[11] 李鹏程, 毕真真, 孙超, 秦天元, 梁文君, 王一好, 许德蓉, 刘玉汇, 张俊莲, 白江平. DNA甲基化参与调控马铃薯响应干旱胁迫的关键基因挖掘[J]. 作物学报, 2021, 47(4): 599-612.
[12] 秦天元, 刘玉汇, 孙超, 毕真真, 李安一, 许德蓉, 王一好, 张俊莲, 白江平. 马铃薯StIgt基因家族的鉴定及其对干旱胁迫的响应分析[J]. 作物学报, 2021, 47(4): 780-786.
[13] 蒋伟, 潘哲超, 包丽仙, 周福仙, 李燕山, 隋启君, 李先平. 马铃薯资源晚疫病抗性的全基因组关联分析[J]. 作物学报, 2021, 47(2): 245-261.
[14] 柳燕兰, 郭贤仕, 张绪成, 马明生, 王宏康. 密度和施肥对旱地马铃薯干物质积累、产量和水肥利用的影响[J]. 作物学报, 2021, 47(2): 320-331.
[15] 牛娜, 刘震, 黄鹏翔, 朱金勇, 李志涛, 马文婧, 张俊莲, 白江平, 刘玉汇. 马铃薯GAUT基因家族的全基因组鉴定及表达分析[J]. 作物学报, 2021, 47(12): 2348-2361.
Viewed
Full text


Abstract

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