Welcome to Acta Agronomica Sinica,

Acta Agronomica Sinica ›› 2020, Vol. 46 ›› Issue (12): 1979-1990.doi: 10.3724/SP.J.1006.2020.04023

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

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 Online:2020-09-02 Published:2020-09-02
  • Contact: FAN Ming-Shou E-mail:hankanghhht@163.com;fmswh@126.com
  • Supported by:
    National Natural Science Foundation of China(31960637);Natural Science Foundation of Inner Mongolia(2019BS03021)

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

Table 1

Nitrogen application plan (kg N hm-2)"

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

Table 2

General information for the experiment"

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

Table 3

SVC HR-1024i spectral resolution and sampling interval"

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

Table 4

Spectral index formula"

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]

Table 5

Number of samples in modeling set and validation set"

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

Fig. 2

Effects of different nitrogen application rates on leaf nitrogen accumulation of potato (Exp.1) LNA is the leaf nitrogen accumulation. Treatments are the same as those given in Table 1. Different lowercase letters indicates significant difference at the 0.05 probability level."

Fig. 3

Effects of different nitrogen application rates on canopy reflectance a: full-wave band; b: visible light band; c: red edge band. Treatments are the same as those given in Table 1."

Table 6

Regression models of potato spectral indices and leaf nitrogen accumulation"

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**

Fig. 5

Relationship between spectral index and leaf nitrogen accumulation LNA is the leaf nitrogen accumulation. a: seedling stage; b: tuber formation stage; c: total growth stages. ** indicates significant difference at the 0.01 probability level."

Table 7

Examination of monitoring model for nitrogen accumulation in potato leaves"

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

Fig. 6

Relationship between estimated values and observed values of leaf nitrogen accumulation a: seedling stage; b: tuber formation stage; c: tuber bulking stage; d: starch accumulation stage; e: total growth stages."

Fig. 4

Correlation between spectral indices and leaf nitrogen accumulation a: seedling stage; b: tuber formation stage; c: tuber bulking stage; d: starch accumulation stage; e: total growth stages. Abbreviations are the same as in Table 4."

Fig. 1

Location and distribution of experimental plots N0-N4 represent 0, 150, 300, 450, 600 kg hm-2 nitrogen fertilizer levels, respectively; a, b, and c are three replications."

[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] WANG Hai-Bo, YING Jing-Wen, HE Li, YE Wen-Xuan, TU Wei, CAI Xing-Kui, SONG Bo-Tao, LIU Jun. Identification of chromosome loss and rearrangement in potato and eggplant somatic hybrids by rDNA and telomere repeats [J]. Acta Agronomica Sinica, 2022, 48(5): 1273-1278.
[2] SHI Yan-Yan, MA Zhi-Hua, WU Chun-Hua, ZHOU Yong-Jin, LI Rong. Effects of ridge tillage with film mulching in furrow on photosynthetic characteristics of potato and yield formation in dryland farming [J]. Acta Agronomica Sinica, 2022, 48(5): 1288-1297.
[3] FENG Ya, ZHU Xi, LUO Hong-Yu, LI Shi-Gui, ZHANG Ning, SI Huai-Jun. Functional analysis of StMAPK4 in response to low temperature stress in potato [J]. Acta Agronomica Sinica, 2022, 48(4): 896-907.
[4] ZHANG Xia, YU Zhuo, JIN Xing-Hong, YU Xiao-Xia, LI Jing-Wei, LI Jia-Qi. Development and characterization analysis of potato SSR primers and the amplification research in colored potato materials [J]. Acta Agronomica Sinica, 2022, 48(4): 920-929.
[5] JIN Rong, JIANG Wei, LIU Ming, ZHAO Peng, ZHANG Qiang-Qiang, LI Tie-Xin, WANG Dan-Feng, FAN Wen-Jing, ZHANG Ai-Jun, TANG Zhong-Hou. Genome-wide characterization and expression analysis of Dof family genes in sweetpotato [J]. Acta Agronomica Sinica, 2022, 48(3): 608-623.
[6] TAN Xue-Lian, GUO Tian-Wen, HU Xin-Yuan, ZHANG Ping-Liang, ZENG Jun, LIU Xiao-Wei. Characteristics of microbial community in the rhizosphere soil of continuous potato cropping in arid regions of the Loess Plateau [J]. Acta Agronomica Sinica, 2022, 48(3): 682-694.
[7] ZHANG Hai-Yan, XIE Bei-Tao, JIANG Chang-Song, FENG Xiang-Yang, ZHANG Qiao, DONG Shun-Xu, WANG Bao-Qing, ZHANG Li-Ming, QIN Zhen, DUAN Wen-Xue. Screening of leaf physiological characteristics and drought-tolerant indexes of sweetpotato cultivars with drought resistance [J]. Acta Agronomica Sinica, 2022, 48(2): 518-528.
[8] JIAN Hong-Ju, SHANG Li-Na, JIN Zhong-Hui, DING Yi, LI Yan, WANG Ji-Chun, HU Bai-Geng, Vadim Khassanov, LYU Dian-Qiu. Genome-wide identification and characterization of PIF genes and their response to high temperature stress in potato [J]. Acta Agronomica Sinica, 2022, 48(1): 86-98.
[9] XU De-Rong, SUN Chao, BI Zhen-Zhen, QIN Tian-Yuan, WANG Yi-Hao, LI Cheng-Ju, FAN You-Fang, LIU Yin-Du, ZHANG Jun-Lian, BAI Jiang-Ping. Identification of StDRO1 gene polymorphism and association analysis with root traits in potato [J]. Acta Agronomica Sinica, 2022, 48(1): 76-85.
[10] ZHANG Si-Meng, NI Wen-Rong, LYU Zun-Fu, LIN Yan, LIN Li-Zhuo, ZHONG Zi-Yu, CUI Peng, LU Guo-Quan. Identification and index screening of soft rot resistance at harvest stage in sweetpotato [J]. Acta Agronomica Sinica, 2021, 47(8): 1450-1459.
[11] ZHANG Xiao, YAN Yan, WANG Wen-Hui, ZHENG Heng-Biao, YAO Xia, ZHU Yan, CHENG Tao. Application of continuous wavelet analysis to laboratory reflectance spectra for the prediction of grain amylose content in rice [J]. Acta Agronomica Sinica, 2021, 47(8): 1563-1580.
[12] SONG Tian-Xiao, LIU Yi, RAO Li-Ping, Soviguidi Deka Reine Judesse, ZHU Guo-Peng, YANG Xin-Sun. Identification and expression analysis of cell wall invertase IbCWIN gene family members in sweet potato [J]. Acta Agronomica Sinica, 2021, 47(7): 1297-1308.
[13] TANG Rui-Min, JIA Xiao-Yun, ZHU Wen-Jiao, YIN Jing-Ming, YANG Qing. Cloning of potato heat shock transcription factor StHsfA3 gene and its functional analysis in heat tolerance [J]. Acta Agronomica Sinica, 2021, 47(4): 672-683.
[14] LI Peng-Cheng, BI Zhen-Zhen, SUN Chao, QIN Tian-Yuan, LIANG Wen-Jun, WANG Yi-Hao, XU De-Rong, LIU Yu-Hui, ZHANG Jun-Lian, BAI Jiang-Ping. Key genes mining of DNA methylation involved in regulating drought stress response in potato [J]. Acta Agronomica Sinica, 2021, 47(4): 599-612.
[15] QIN Tian-Yuan, LIU Yu-Hui, SUN Chao, BI Zhen-Zhen, LI An-Yi, XU De-Rong, WANG Yi-Hao, ZHANG Jun-Lian, BAI Jiang-Ping. Identification of StIgt gene family and expression profile analysis of response to drought stress in potato [J]. Acta Agronomica Sinica, 2021, 47(4): 780-786.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!