不同光谱指数反演马铃薯叶片氮累积量的研究

doi:10.3724/SP.J.1006.2020.04023

Abstract

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

HAN Kang, YU Jing, SHI Xiao-Hua, CUI Shi-Xin, FAN Ming-Shou. Inversion of nitrogen accumulation in potato leaf with different spectral indices[J].Acta Agronomica Sinica, 2020, 46(12): 1979-1990.

Figures/Tables 13

Table 1

Table 2

Table 3

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 4

Table 5

Fig. 2

Fig. 3

Table 6

Fig. 5

Table 7

Fig. 6

Fig. 4

Fig. 1

References 50

[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).

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

处理 Treatment	基肥 Base fertilizer	追肥Top dressing
处理 Treatment	基肥 Base fertilizer	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

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

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

生育时期 Growth stage	样本数量Number of samples
生育时期 Growth stage	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

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^**
苗期Seedling stage	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^**
块茎形成期Tuber formation stage	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^**
块茎膨大期Tuber expansion stage	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^**
淀粉积累期Starch accumulation stage	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^**
全生育时期Total growth stages	LNA = 832.343 × VOG3² - 46.576 × VOG3 - 0.242	0.778^**
	LNA = 3.867 × e^{-11.534 × VOG3}	0.773^**

Inversion of nitrogen accumulation in potato leaf with different spectral indices

RichHTML375

PDF

Abstract

Cite this article

share this article

Figures/Tables 13

References 50

Related Articles 15

Metrics

Comments

Recommended 0

[1]	Chen Guo-Huan, Zhang Rui, Li Yan-Di, Zhao Jia-Qi, Ren Yong-Tao, Zhang Tian-Ci, Guo Hua-Chun, Li Jun, Yang Fang. Effects of foliar application of exogenous selenium on anthocyanin biosynthesis in tubers of light purple-fleshed potatoes [J]. Acta Agronomica Sinica, 2026, 52(6): 1876-1890.
[2]	Wang Yi-Han, Li Fu-Chang, Liu Yi, Zhu Guo-Peng. Cloning of the IbOPR2 gene promoter and identification of regulatory factors in sweetpotato [J]. Acta Agronomica Sinica, 2026, 52(4): 1268-1276.
[3]	Tian Li-Tao, Ding Ning, Wang Shu-Lin, Qi En-Fang, Zhang Rong, Wang Rui-Rui, Ma Li-Wen, Li Jian-Wu, Yang Jiang-Wei. Genome-wide identification of the Argonaute gene family and its induction by late blight in potato (Solanum tuberosum L.) [J]. Acta Agronomica Sinica, 2026, 52(4): 1116-1126.
[4]	Jiang Jia-Hui, Jiang Bing-Zhi, Liu Guan-Ming, Wang Zhang-Ying, Tang Chao-Chen. Establishment and optimization of near-infrared spectroscopy models for quality traits of purple-fleshed sweet potato [J]. Acta Agronomica Sinica, 2026, 52(4): 1088-1102.
[5]	Zhang Yu, Liu Fang, Cai Cheng-Cheng, Yang Xiao-Hua, Jia MO-Shi-Zha, Yang Yuan-Jun, Wang Xi-Yao. Preliminary investigation on the mechanism of potato tuber dormancy release induced by combined treatment of bromoethane and gibberellin [J]. Acta Agronomica Sinica, 2026, 52(3): 825-838.
[6]	Yu Yong-Chao, Liu Ming, Jin Rong, Zhao Peng, Zhang Qiang-Qiang, Wang Jing, Zhu Xiao-Ya, Tang Zhong-Hou. Physiological mechanism and transcriptome analysis of sweet potato overgrowth under high-nitrogen conditions [J]. Acta Agronomica Sinica, 2026, 52(3): 813-824.
[7]	Wang Ling, Hu Hao, Song Jia-Feng, Cheng Jie-Lan, Chen Ying, Zheng Ting-Ting, Lyu Zhao-Yan, Zhu Xiao-Biao, Hou Hua-Lan. Cloning and functional validation of UDP-glycosyltransferase gene StUGT52 in potato [J]. Acta Agronomica Sinica, 2026, 52(3): 665-676.
[8]	Xu Qiang, Xie Kui-Zhong, Hu Xin-Yuan, Yue Yun, Dong Bo, Luo Ai-Hua. Effects of continuous cropping on the structure and function of soil nematode communities in potato [J]. Acta Agronomica Sinica, 2026, 52(2): 527-538.
[9]	Yang Xuan, Li Jian-Kang, He Wan-Jie, Li You-Jun, Cheng Xiang-Han, Hou Wen-Bang. Effects of selenium fertilization on fresh-cut browning in sweet potatoes and its mechanism analysis [J]. Acta Agronomica Sinica, 2026, 52(2): 578-589.
[10]	Yang Biao, Du Shuai-Kang, Zhang Ji-Wang, Shi Ying, Zhang Li-Li. Genome-wide identification of class III POD gene family in potato and its expression profile analysis [J]. Acta Agronomica Sinica, 2026, 52(2): 405-420.
[11]	Wang Ya-Zhi, Yang Biao, Ji Xiang-Lin, Shi Ying, Zhang Li-Li. Identification of drought-resistant resources and preliminary screening of drought resistant genes in diploid potatoes [J]. Acta Agronomica Sinica, 2026, 52(1): 72-84.
[12]	Tian Jia-Chun, Ge Xia, Li Shou-Qiang, Li Mei, Tian Shi-Long, Zhang Ya-Qian, Cheng Jian-Xin, Li Yu-Mei. Mechanism of low O₂and high CO₂ storage environment delaying aging of potato tuber [J]. Acta Agronomica Sinica, 2026, 52(1): 262-278.
[13]	Ji Xuan-Tong, Bian Chun-Song, Jin Li-Ping, Li Sen, Qin Jun-Hong, Li Guang-Cun. Responses of root-associated microorganisms of different drought-tolerant potato varieties to drought conditions [J]. Acta Agronomica Sinica, 2026, 52(1): 165-177.
[14]	ZHUO Feng-Qi, TANG Zhen-San, LEI Yu-Jun, CHENG Li-Xiang, ZHAO Tian-Tian, LYU Tai, YANG Chen, ZHANG Feng. Screening of low glycemic potato varieties (lines) based on cooking methods and regeneration temperature [J]. Acta Agronomica Sinica, 2025, 51(9): 2538-2546.
[15]	ZHU Jin-Cheng, YANG Qiu-Hua, CHENG Li-Xiang, LI Wen-Li, SHI Ming-Ming, LI Hui-Xia, ZHANG Feng. Screening of potato germplasm for resistance to Meloidogyne incognita and analysis of related physiological responses [J]. Acta Agronomica Sinica, 2025, 51(9): 2307-2317.