Scientia Agricultura Sinica ›› 2021, Vol. 54 ›› Issue (21): 4525-4538.doi: 10.3864/j.issn.0578-1752.2021.21.004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Determination of Suitable Band Width for Estimating Rice Nitrogen Nutrition Index Based on Leaf Reflectance Spectra

XU HaoCong1(),YAO Bo1,WANG Quan1,CHEN TingTing1,ZHU TieZhong1,HE HaiBing1,KE Jian1,YOU CuiCui1,WU XiaoWen2,GUO ShuangShuang3,WU LiQuan1,4,*()   

  1. 1College of Agronomy, Anhui Agricultural University, Hefei 230036
    2Lujiang County Agricultural Technology Extension Center, Lujiang 231500, Anhui
    3Zoomlion Intelligent Agriculture Co. Ltd., Wuhu 241000, Anhui
    4Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing 210095
  • Received:2020-11-23 Accepted:2021-02-01 Online:2021-11-01 Published:2021-11-09
  • Contact: LiQuan WU E-mail:861389737@qq.com;wlq-001@163.com

RichHTML

38

PDF

658

Abstract:

【Objective】The research aimed to analyze the relationship between rice (Oryza sativa L.) nitrogen nutrition index (NNI) and leaf spectral reflectance characteristics of leaf on different positions, so as to provide an effective method for nondestructive and timely evaluation of NNI in rice.【Method】Field experiments were conducted with different N application rates and rice cultivars across two growing seasons during 2018-2019, and the leaf hyperspectral reflectance of 350-2 500 nm of leaf on different positions and the plant NNI were measured during key fertility growth stages to construct a variety of spectral index model for rice NNI monitoring.【Result】The results indicated that green band (540 nm) at leaf level was the sensitive band for estimating NNI, and narrow band ratio index SR (R900, R540) composed of near infrared band and green band could be used to retrieve NNI of rice. However, the prediction accuracy of narrow band ratio index and rice NNI of leaf on different positions were different. In terms of prediction accuracy, the best single leaf position was the third leaf (L3) from the top (R2=0.731, RMSE=0.130, RE=11.6%), the second leaf (L2) from the top followed (R 2=0.707, RMSE=0.136, RE=12.2%), and the top one (L1) was the worst (R 2=0.443, RMSE=0.187, RE=14.7%). The averaged spectra of L2 and L3 (L23) was the optimum leaf spectra combination, which contributed to improving the predictability to NNI(R 2=0.740, RMSE=0.128, RE=11.5%). The samples were resampled at 50 nm and 10 nm in the near infrared region (900 nm) and green region (540 nm) respectively, and the accuracy of the wide band ratio index SR (AR(900±50), AR(540±10)) was not significantly lower than that of SR (R900, R540). The model accuracy and prediction accuracy of the two models were basically the same at L23. When the NNI of rice was less than 1, there was a significant positive linear correlation with the yield, and then it tended to be stable. 【Conclusion】The results showed that the reflectance spectra of L2 and L3 leaves were sensitive for monitoring NNI for rice, and L23 could improve the prediction accuracy of the model. Multiple band ratio indices SR (R900, R540) and SR (AR(900±50), AR(540±10)) based on leaf reflectance spectra could be used to rapidly estimate rice NNI, which provided a theoretical basis for monitoring rice NNI with various sensors.

Key words: leaf, rice(Oryza sativa L.), nitrogen nutrition index, ratio index, model, band width

Table 1

Soil information about experiments"

年份
Year		 有机质
Organic matter (g·kg-1)		 全氮
Total N (g·kg-1)		 有效磷
Available P (mg·kg-1)		 速效钾
Available K (mg·kg-1)		 pH
2018 32.36 2.03 24.80 211.42 5.11
2019 30.89 1.77 25.97 245.52 5.54

Fig. 1

Relationship between nitrogen nutrition index and yield in rice (2018-2019) ** indicates significance at 0.01 level"

Table 2

List of hyperspectral indices used in this study"

光谱指数
Spectral index		 计算公式
Algorithm		 参考文献
Reference
比值光谱指数 Simple ratio spectral index (SR) R1 / R2 本研究 This study
归一化差值光谱指数 Normalized difference spectral index (ND) (R1 - R2)/(R1 + R2) 本研究This study
修正红边归一化指数
Modified red edge normalized difference vegetation index (MSR 705)		 (R750-R705)/(R750+2×R445) [12]
红边归一化指数 Red edge normalized difference vegetation index (ND 705) (R750- R705)/(R750+ R705) [13]
比值指数-ldB Ratio index-1dB (RI-ldB) R735 /R720 [14]
Vogelmann 红边指数 Vogelman red edge index (VOG) R740 / R720 [15]
双峰冠层氮指数 Double-peak canopy nitrogen index (DCNI) (R720-R700)/(R700-R670)/(R720-R670+0.03) [16]
线性内插法红边位置 Red edge position: linear interpolation method (REPLI) 700+40×[(R670+R780)/2-R700] /(R740-R700) [17]
比值植被指数II Ratio Vegetation Index Ⅱ(RVI Ⅱ) R810 / R560 [18]

Table 3

Analysis of variance of NNI among varieties, nitrogen treatments, and growth stage, respectively"

分组因子
Grouping factor		 变异来源
Variation source		 平方和
Sum of squares		 自由度
df		 均方
Mean square		 F值
F-value		 P值
P-value
氮肥处理
Nitrogen rate		 组间 Between groups 2.643 4 0.661 31.745 0
组内 Within groups 1.561 75 0.021
总计 Total 4.203 79
品种
Variety		 组间 Between groups 0.231 3 0.077 1.475 0.228
组内 Within groups 3.972 76 0.052
总计 Total 4.203 79
生育期
Growth stage		 组间 Between groups 0.094 1 0.094 1.782 0.186
组内 Within groups 4.109 78 0.053
总计 Total 4.203 79

Fig. 2

Dynamic changes of NNI under different nitrogen application rates of rice plants"

Fig. 3

Leaf spectral reflectance under different nitrogen application rates"

Fig. 4

Correlation between NNI and leaf spectral reflectance of different position leaves of rice plants L1: The top leaf; L2: The second leaf; L3: The third leaf; L12: The averaged spectra of L1 and L2; L23: The averaged spectra of L2 and L3; L13: The averaged spectra of L1 and L3; L123: The averaged spectra of L1, L2 and L3"

Table 4

Correlation coefficient between typical spectral index and rice NNI at different growth stages of rice plants"

时期 Stage 叶位 Leaf position MSR705 ND705 RI-1dB VOG DCNI REPLI RVI Ⅱ
分蘖期
Tillering stage		 顶1叶（L1） 0.483** 0.534** 0.561** 0.564** 0.607** 0.478** 0.553**
顶2叶（L2） 0.555** 0.479** 0.444** 0.443** 0.367** 0.436** 0.563**
顶3叶（L3） 0.311** 0.394** 0.406** 0.409** 0.718** 0.488** 0.398**
顶1、2叶平均（L23） 0.441** 0.456** 0.446** 0.448** 0.555** 0.488** 0.499**
拔节期
Jointing stage		 顶1叶（L1） 0.568** 0.541** 0.538** 0.536** 0.253** 0.465** 0.657**
顶2叶（L2） 0.717** 0.665** 0.648** 0.649** 0.770** 0.538** 0.792**
顶3叶（L3） 0.765** 0.742** 0.718** 0.717** 0.652** 0.669** 0.810**
顶2、3叶平均（L23） 0.791** 0.748** 0.721** 0.721** 0.721** 0.655** 0.835**
孕穗期
Booting stage		 顶1叶（L1） 0.620** 0.635** 0.711** 0.712** 0.278** 0.629** 0.686**
顶2叶（L2） 0.781** 0.812** 0.833** 0.835** 0.476** 0.772** 0.859**
顶3叶（L3） 0.730** 0.755** 0.758** 0.758** 0.485** 0.714** 0.783**
顶2、3叶平均（L23） 0.763** 0.790** 0.807** 0.808** 0.490** 0.749** 0.837**
抽穗期
Heading stage		 顶1叶（L1） 0.462** 0.511** 0.510** 0.516** 0.519** 0.557** 0.521**
顶2叶（L2） 0.586** 0.652** 0.684** 0.690** 0.714** 0.592** 0.730**
顶3叶（L3） 0.546** 0.589** 0.635** 0.643** 0.600** 0.432** 0.706**
顶2、3叶平均（L23） 0.593** 0.649** 0.696** 0.703** 0.646** 0.538** 0.734**

Table 5

Correlation between existing spectral index and NNI in rice"

叶位 Leaf position MSR705 ND705 RI-1dB VOG DCNI REPLI RVI Ⅱ
顶1叶（L1） 0.571** 0.576** 0.570** 0.572** 0.477** 0.584** 0.621**
顶2叶（L2） 0.708** 0.726** 0.741** 0.745** 0.575** 0.710** 0.798**
顶3叶（L3） 0.629** 0.660** 0.698** 0.703** 0.577** 0.543** 0.739**
顶2、3叶平均（L23） 0.676** 0.702** 0.733** 0.738** 0.587** 0.642** 0.781**
样本量 160 个 Sample volume is 160

Fig. 5

Contour maps of determination coefficients (R2) between NNI and normalized difference (ND) and ratio (SR) spectral indices based on two wavebands-combination in rice"

Fig. 6

Contour maps of RMSE and RE values using simple ratio index or predicting NNI in rice (n=90)"

Table 6

Quantitative relationships between NNI (y) and different spectral index (x) in rice (n=249)"

叶位
Leaf position		 光谱指数
Spectral index		 回归方程
Regression equation		 模型精度
Model precision (R2)		 预测精度
Prediction precision (R2)		 均方根误
RMSE		 相对误差
RE (%)
顶一叶（L1） SR（R900,R540） y=0.2488x+0.1267 0.390 0.443 0.187 14.7
SR[AR（900±50）,AR（540±10）] y=0.2454x+0.1307 0.388 0.441 0.187 14.7
顶二叶（L2） SR（R900,R540） y=0.3943x-0.3199 0.657 0.707 0.136 12.2
SR[AR（900±50）,AR（540±10）] y=0.3896x-0.3154 0.654 0.706 0.136 12.2
顶三叶（L2） SR（R900,R540） y=0.3019x-0.0364 0.557 0.731 0.130 11.6
SR[AR（900±50）,AR（540±10）] y=0.2988x-0.0344 0.556 0.729 0.130 11.6
顶二、顶三光谱平均（L23） SR（R900,R540） y=0.3556x-0.1997 0.625 0.740 0.128 11.5
SR[AR（900±50）,AR（540±10）] y=0.3517x-0.1967 0.624 0.740 0.128 11.5
顶一、顶二、顶三光谱平均（L123） SR（R900,R540） y=0.3645x-0.2208 0.618 0.720 0.133 11.2
SR[AR（900±50）,AR（540±10）] y=0.3602x-0.217 0.616 0.718 0.133 11.3

Fig. 7

Comparison between predicted and observed values of NNI of rice plants (L23; n=249)"

[1] ZHAO D, REDDY K R, KAKANI V G, REDDY V R. Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum. European Journal of Agronomy, 2005, 22(4):391-403.
doi: 10.1016/j.eja.2004.06.005
[2] GASTAL F, BLANGER G. The effects of nitrogen fertilization and the growing season on photosynthesis of field-grown tall fescue (Festuca arundinacea Schreb.) canopies. Annals of Botany, 1993, 72(5):401-408.
doi: 10.1006/anbo.1993.1125
[3] PENG S B, BURESH R J, HUANG J L, YANG J C, ZOU Y B, ZHONG X H, WANG G H, ZHANG F S. Strategies for overcoming low agronomic nitrogen use efficiency in irrigated rice systems in China. Field Crops Research, 2006, 96(1):37-47.
doi: 10.1016/j.fcr.2005.05.004
[4] BENTJE B, BJERG P L, SONG X F, JAKOBSEN R. Field scale interaction and nutrient exchange between surface water and shallow groundwater in the Baiyang Lake region, North China Plain. Journal of Environmental Sciences, 2016, 45(7):60-75.
doi: 10.1016/j.jes.2015.11.021
[5] ZHAO B, ATA-UL-KARIM S T, LIU Z D, NING D F, XIAO J F, LIU Z G, QIN A Z, NAN J Q, DUAN A W. Development of a critical nitrogen dilution curve based on leaf dry matter for summer maize. Field Crops Research, 2017, 208:60-68.
doi: 10.1016/j.fcr.2017.03.010
[6] 贺志远, 朱艳, 李艳大, 刘小军, 曹强, 曹卫星, 汤亮. 中国南方双季稻氮营养指数及产量估算模型研究. 南京农业大学学报, 2017, 40(1):11-19.
HE Z Y, ZHU Y, LI Y D, LIU X J, CAO Q, CAO W X, TANG L. Study on estimation model for nitrogen nutrition index and yield on double cropping rice in southern China. Journal of Nanjing Agricultural University, 2017, 40(1):11-19. (in Chinese)
[7] 杜宇笑, 李鑫格, 张羽, 程涛, 刘小军, 田永超, 朱艳, 曹卫星, 曹强. 不同产量水平下稻茬小麦的氮素营养指标特征. 植物营养与肥料学报, 2020, 26(8):1420-1429.
DU Y X, LI X G, ZHANG Y, CHENG T, LIU X J, TIAN Y C, ZHU Y, CAO W X, CAO Q. Variation in nitrogen status indicators with grain yield level for winter wheat after rice. Journal of Plant Nutrition and Fertilizers, 2020, 26(8):1420-1429. (in Chinese)
[8] 贾彪, 付江鹏. 基于叶面积指数构建滴灌玉米营养生长期临界氮稀释曲线. 农业工程学报, 2020, 36(6):66-73.
JIA B, FU J P. Critical nitrogen dilution curve of drip-irrigated maize at vegetative growth stage based on leaf area index. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(6):66-73. (in Chinese)
[9] 马露露, 吕新, 张泽, 马革新, 海兴岩. 基于临界氮浓度的滴灌棉花氮素营养诊断模型研究. 农业机械学报, 2018, 49(2):277-283.
MA L L, LÜ X, ZHANG Z, MA G X, HAI X Y. Establishment of nitrogen nutrition diagnosis model for drip-irrigation cotton based on critical nitrogen concentration. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(2):277-283. (in Chinese)
[10] ZHAO B, LIU Z D, ATA-UL-KARIM S T, XIAO J F, LIU Z G, QIN A Z, NING D F, NAN J Q, DUAN A W. Rapid and nondestructive estimation of the nitrogen nutrition index in winter barley using chlorophy Ⅱ measurements. Field Crops Research, 2016, 185:59-68.
doi: 10.1016/j.fcr.2015.10.021
[11] AKRAM M. Effects of nitrogen application on chlorophyll content, water relations, and yield of maize hybrids under saline conditions. Communications in Soil Science and Plant Analysis, 2014, 45(10):1336-1356.
doi: 10.1080/00103624.2013.875199
[12] SIMS 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 Sensing of Environment, 2002, 81(2/3):337-354.
doi: 10.1016/S0034-4257(02)00010-X
[13] 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. Journal of Plant Physiology, 1994, 143(3):286-292.
doi: 10.1016/S0176-1617(11)81633-0
[14] GUPTA R K, VIJAYAN D, PRASAD T S. Comparative analysis of red-edge hyperspectral indices. Advances in Space Research, 2003, 32(11):2217-2222.
doi: 10.1016/S0273-1177(03)90545-X
[15] VOGELMANN J E, ROCK B N, MOSS D M. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 1993, 14(8):1563-1575.
doi: 10.1080/01431169308953986
[16] CHEN P F, HABOUDANE D, TREMBLAY N, WANG J H, VIGNEAULT P, LI B G. New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sensing of Environment, 2010, 114(9):1987-1997.
doi: 10.1016/j.rse.2010.04.006
[17] CHO M A, SKIDMORE A K. A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sensing of Environment, 2006, 101(2):181-193.
doi: 10.1016/j.rse.2005.12.011
[18] XUE L H, CAO W X, LUO W H, DAI T B, ZHU Y. Monitoring leaf nitrogen status in rice with canopy spectral reflectance. Agronomy Journal, 2004, 96(1):135-142.
doi: 10.2134/agronj2004.0135
[19] 曾水生, 李艳大, 舒时富, 陈立才, 叶春, 贺志远, 张晨, 曾一凡. 光谱技术在水稻生长监测中的应用研究进展. 农学学报, 2015, 5(2):100-104.
ZENG S S, LI Y D, SHU S F, CHEN L C, YE C, HE Z Y, ZHANG C, ZENG Y F. Application of spectrum technique in monitoring rice growth. Journal of Agriculture, 2015, 5(2):100-104. (in Chinese)
[20] 梁惠平, 刘湘南. 玉米氮营养指数的高光谱计算模型. 农业工程学报, 2010, 26(1):264-269.
LIANG H P, LIU X N. Model for calculating corn nitrogen nutrition index using hyper-spectral data. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(1):264-269. (in Chinese)
[21] 王仁红, 宋晓宇, 李振海, 杨贵军, 郭文善, 谭昌伟, 陈立平. 基于高光谱的冬小麦氮素营养指数估测. 农业工程学报, 2014, 30(19):191-198.
WANG R H, SONG X Y, LI Z H, YANG G J, GUO W S, TAN C W, CHEN L P. Estimation of winter wheat nitrogen nutrition index using hyperspectral remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(19):191-198. (in Chinese)
[22] 姚霞. 小麦冠层和单叶氮素营养指标的高光谱监测研究[D]. 南京:南京农业大学, 2009.
YAO X. Monitoring nitrogen stayus at canopy and leaf scales with hyperspectral sensing in wheat[D]. Nanjing: Nanjing Agricultuarl University, 2009. (in Chinese)
[23] 田永超, 杨杰, 姚霞, 曹卫星, 朱艳. 利用叶片高光谱指数预测水稻群体叶层全氮含量. 作物学报, 2010, 36(9):1529-1537.
TIAN Y C, YANG J, YAO X, CAO W X, ZHU Y. Monitoring canopy leaf nitrogen concentration based on leaf hyperspectral indices in rice. Acta Agronomica Sinica, 2010, 36(9):1529-1537. (in Chinese)
[24] 李刚华, 薛利红, 尤娟, 王绍华, 丁艳锋, 吴昊, 杨文祥. 水稻氮素和叶绿素SPAD叶位分布特点及氮素诊断的叶位选择. 中国农业科学, 2007, 40(6):1127-1134.
LI G H, XUE L H, YOU J, WANG S H, DING Y F, WU H, YANG W X. Spatial distribution of leaf N content and SPAD value and determination of the suitable leaf for N diagnosis in rice. Scientia Agricultura Sinica, 2007, 40(6):1127-1134. (in Chinese)
[25] 赵犇, 姚霞, 田永超, 刘小军, 曹卫星, 朱艳. 基于上部叶片SPAD值估算小麦氮营养指数. 生态学报, 2013, 33(3):916-924.
doi: 10.5846/stxb
ZHAO B, YAO X, TIAN Y C, LIU X J, CAO W X, ZHU Y. Estimation of nitrogen nutrient index on SPAD value of top leaves in wheat. Acta Ecologica Sinica, 2013, 33(3):916-924. (in Chinese)
doi: 10.5846/stxb
[26] INOUE Y, SAKAIYA E, ZHU Y, TAKAHASHI W. Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sensing of Environment, 2012, 126:210-221.
doi: 10.1016/j.rse.2012.08.026
[27] TIAN Y C, GU K J, CHU X, YAO X, CAO W X, ZHU Y. Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice. Plant and Soil, 2014, 376(1/2):193-209.
doi: 10.1007/s11104-013-1937-0
[28] 宋红燕, 胡克林, 彭希. 基于高光谱技术的覆膜旱作水稻植株氮含量及籽粒产量估算. 中国农业大学学报, 2016, 21(8):27-34.
SONG H Y, HU K L, PENG X. Crop nitrogen content diagnosis and yield estimation in ground cover rice production system based on hyperspectral data. Journal of China Agricultural University, 2016, 21(8):27-34. (in Chinese)
[29] LEMAIRE G, GASTAL F, SALETTE J. N Uptake and Distribution in Plant Canopies. Springer: Berlin, 1997, 25:3-45.
[30] 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(8):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. Spectroscopy and Spectral Analysis, 2009, 29(8):2191-2195. (in Chinese)
[31] STROPPIANA D, MBOSCHETTI M, BRIVIO P A, BOCCHI S. Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Research, 2009, 111(1/2):119-129.
doi: 10.1016/j.fcr.2008.11.004
[32] 杜宇笑, 李鑫格, 王雪, 刘小军, 田永超, 朱艳, 曹卫星, 曹强. 不同产量水平稻茬小麦氮素需求特征研究. 作物学报, 2020, 46(11):1780-1789.
doi: 10.3724/SP.J.1006.2020.01027
DU Y X, LI X G, WANG X, LIU X J, TIAN Y C, ZHU Y, CAO W X, CAO Q. Nitrogen demand characteristics with different grain yield levels for wheat after rice. Acta Agronomica Sinica, 2020, 46(11):1780-1789. (in Chinese)
doi: 10.3724/SP.J.1006.2020.01027
[33] ATA-UL-KARIM S T, LIU X J, LU Z Z, ZHENG H B, CAO W X, ZHU Y. Estimation of nitrogen fertilizer requirement for rice crop using critical nitrogen dilution curve. Field Crops Research, 2017, 201:32-40.
doi: 10.1016/j.fcr.2016.10.009
[34] 陈志强, 王磊, 白由路, 杨俐苹, 卢艳丽, 王贺, 王志勇. 玉米叶片的光谱响应及其氮素含量预测研究. 光谱学与光谱分析, 2013, 33(4):1066-1070.
CHEN Z Q, WANG L, BAI Y L, YANG L P, LU Y L, WANG H, WANG Z Y. Spectral response of maize leaves and prediction of their nitrogen content. Spectroscopy and Spectral Analysis, 2013, 33(4):1066-1070. (in Chinese)
[35] 张银杰, 王磊, 白由路, 杨俐苹, 卢艳丽, 张静静, 李格. 基于高光谱分析的玉米叶片氮含量分层诊断研究. 光谱学与光谱分析, 2019, 39(9):2829-2835.
ZHANG Y J, WANG L, BAI Y L, YANG L P, LU Y L, ZHANG J J, LI G. Nitrogen nutrition diagnostic based on hyperspectral analysis about different layers leaves in maize. Spectroscopy and Spectral Analysis, 2019, 39(9):2829-2835. (in Chinese)
[36] 刘忠, 万炜, 黄晋宇, 韩已文, 王佳莹. 基于无人机遥感的农作物长势关键参数反演研究进展. 农业工程学报, 2018, 34(24):60-71.
LIU Z, WAN W, HUANG J Y, HAN Y W, WANG J Y. Progress on key parameters inversion of crop growth based on unmanned aerial vehicle remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(24):60-71. (in Chinese)
[37] 李振. 基于高光谱玉米氮素营养与生长指标的监测[D]. 泰安: 山东农业大学, 2012.
LI Z. Monitoring nitrogen status and growth characters with hyper-spectral in maize[D]. Taian:Shandong Agricutural University, 2012. (in Chinese)
[38] ZHAO B, DUAN A W, ATA-UL-KARIM S T, LIU Z D, CHEN Z F, GONG Z H, ZHANG J Y, XIAO J F, LIU Z G, QIN A Z, NING D F. Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize. European Journal of Agronomy, 2018, 93:113-125.
doi: 10.1016/j.eja.2017.12.006
[1] WU YuanYuan, LÜ ShuWen, ZHANG ZiJun, WANG Tao, ZHANG YiMing, BU LingChao, ZOU QingDao, JIANG Jing. Mixed Major Gene+Polygene Genetic Analysis of Blossom-End Scar Size in Tomato Fruit [J]. Scientia Agricultura Sinica, 2026, 59(5): 1060-1069.
[2] YUE LiXin, WANG QingHua, WANG ZhenBao, NIMAQIONGJI, LIU ZeZhou, KONG SuPing, ZHANG LiFeng, GAO LiMin. Widely Targeted Metabolomics-Based Analysis of the Differences in Tibetan Bunching Onion and Chive on Nutritional Quality and Flavonoid Metabolites [J]. Scientia Agricultura Sinica, 2026, 59(5): 1070-1086.
[3] DONG JinLong, ZHAO Ying, YU HaiBing, LÜ JianYe, QIN JiaQi, LIANG Chen, MING Bo, LI ShaoKun. Multi-Model Elucidating of Nutritional Quality Contributions to Maize Kernel Test Weight and Regional Heterogeneity [J]. Scientia Agricultura Sinica, 2026, 59(5): 985-995.
[4] LI SaiYa, XU YaPing, ZHENG JiaXing, ZHENG Yang, YU JiaHui, LI YongQiang, PU ShouCheng, YANG Li, GUO WeiDong. Bioactive Polyphenolic Composition and Antioxidant Activities in Leaves of 34 Vaccinium Species [J]. Scientia Agricultura Sinica, 2026, 59(4): 874-886.
[5] LUO Wei, YU Hong, YUAN LiXin, WANG LingLing, ZHAO JinPeng, YIN Wei, WANG MingTian, WANG RuLin. Change of Geographic Distributions of Ratoon Rice in Sichuan- Chongqing Under Global Climate Change [J]. Scientia Agricultura Sinica, 2026, 59(4): 765-780.
[6] ZHOU Qi, ZHANG ShiHao, ZHANG Liang, PAN Yu, ZHANG LiJuan, TU Zhi, PAN HongMei, LONG Xi. Prediction of the Potential Habitat Suitability of Luopanshan Pigs in Chongqing Based on the Optimized MaxEnt Model [J]. Scientia Agricultura Sinica, 2026, 59(1): 205-219.
[7] TANG HuaJun, WU WenBin, YU QiangYi, SHI Yun, DUAN YuLin, LI WenJuan, QIAN JianPing, SONG Qian, XIA Lang, LI HuiBin, SU BaoFeng, FAN BeiLei, HU Qiong, YE JianQiu, ZHANG Shuai. Developing a Lightweight Multimodal Model for Cropland Remote Sensing Monitoring [J]. Scientia Agricultura Sinica, 2026, 59(1): 78-89.
[8] WANG AiDong, LI RuiJie, FENG XiangQian, HONG WeiYuan, LI ZiQiu, ZHANG XiaoGuo, WANG DanYing, CHEN Song. Multi-Angle Imaging and Machine Learning Approaches for Accurate Rice Leaf Area Estimation [J]. Scientia Agricultura Sinica, 2025, 58(9): 1719-1734.
[9] WANG BingJie, QIN ShiHan, LI DeCheng, HU WenYou, JIANG Jun, CHI FengQin, ZHANG Chao, ZHANG JiuMing, XU YingDe, WANG JingKuan. Spatial Distribution Pattern and Transfer Function Construction of Soil Bulk Density in Nenjiang City, Heilongjiang Province [J]. Scientia Agricultura Sinica, 2025, 58(9): 1791-1803.
[10] YANG GuoChang, ZHENG Yue, BAO XiangNan, DAI YingChun, WANG JinGang, BAI XueFeng, SUN Wei, LI XiHe, ZHANG ShuJun. Research Progress on the Application of Mid-Infrared Spectroscopy Analysis Technology in Predicting Methane Emissions from Cows [J]. Scientia Agricultura Sinica, 2025, 58(9): 1856-1866.
[11] ZHANG HaoXin, YU ShengYue, LEI QiuLiang, DU XinZhong, ZHANG Jizong, AN MiaoYing, FAN BingQian, LUO JiaFa, LIU HongBin. Simulating Soil Organic Carbon Dynamic Changes in Dryland and Paddy Field of Northeast China Using RothC Model [J]. Scientia Agricultura Sinica, 2025, 58(8): 1564-1578.
[12] LI FangLiang, KONG QingBo, ZHANG Qing. Research on the Estimation Model of Calcium Content in Guanxi Honey Pomelo Leaves Based on Spectral Index [J]. Scientia Agricultura Sinica, 2025, 58(7): 1321-1332.
[13] YIN Bo, YU AiZhong, WANG PengFei, YANG XueHui, WANG YuLong, SHANG YongPan, ZHANG DongLing, LIU YaLong, LI Yue, WANG Feng. Effects of Green Manure Returning Combined with Nitrogen Fertilizer Reduction on Hydrothermal Characteristics of Wheat Field and Grain Yield in Oasis Irrigation Area [J]. Scientia Agricultura Sinica, 2025, 58(7): 1366-1380.
[14] LI XinYu, HOU MingYu, CUI ShunLi, LIU YingRu, LI XiuKun, LIU LiFeng. Construction of Near Infrared Spectrometry Model for Flavonoids Content of Peanut with Red and Black Testa [J]. Scientia Agricultura Sinica, 2025, 58(7): 1284-1295.
[15] WU XinJia, XUE Wei, YAN YiDan, NIE YingYing, YE LiMing, XU LiJun. Temporal and Spatial Variation Characteristics of Soil Organic Carbon in Hulunbuir and Its Influencing Factors [J]. Scientia Agricultura Sinica, 2025, 58(6): 1145-1158.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备09089781号-30
Copyright 2018 © Editorial office of Scientia Agricultura Sinica
Tel: 86-010-82106279    E-mail: zgnykx@mail.caas.net.cn
Supported by Beijing Magtech Co.Ltd