欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2021, Vol. 47 ›› Issue (8): 1563-1580.doi: 10.3724/SP.J.1006.2021.02063

• 耕作栽培·生理生化 • 上一篇    下一篇

基于小波分析的水稻籽粒直链淀粉含量高光谱预测

张骁1(), 闫岩1, 王文辉1, 郑恒彪1, 姚霞1,2, 朱艳1, 程涛1,2,*()   

  1. 1南京农业大学国家信息农业工程技术中心/江苏省信息农业重点实验室/农业农村部农作物系统分析与决策重点实验室/智慧农业教育部工程研究中心, 江苏南京 210095
    2江苏省现代作物生产协同创新中心, 江苏南京 210095
  • 收稿日期:2020-09-01 接受日期:2021-01-13 出版日期:2021-08-12 网络出版日期:2021-02-25
  • 通讯作者: 程涛
  • 作者简介:E-mail: 13675139398@163.com
  • 基金资助:
    国家重点研发计划项目(2016YFD0300601);国家自然科学基金项目(41871259)

Application of continuous wavelet analysis to laboratory reflectance spectra for the prediction of grain amylose content in rice

ZHANG Xiao1(), YAN Yan1, WANG Wen-Hui1, ZHENG Heng-Biao1, YAO Xia1,2, ZHU Yan1, CHENG Tao1,2,*()   

  1. 1National Engineering and Technology Center for Information Agriculture (NETCIA), Nanjing Agricultural University/Jiangsu Key Laboratory for Information Agriculture/Key Laboratory of Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs/Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing 210095, Jiangsu, China
    2Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing 210095, Jiangsu, China
  • Received:2020-09-01 Accepted:2021-01-13 Published:2021-08-12 Published online:2021-02-25
  • Contact: CHENG Tao
  • Supported by:
    National Key Research and Development Program of China(2016YFD0300601);National Natural Science Foundation of China(41871259)

RichHTML

19

PDF

391

摘要：

水稻籽粒直链淀粉含量影响稻米的蒸煮食味品质。利用遥感技术及时、准确地获取籽粒直链淀粉含量可以指导相应栽培措施的制定与实施, 以提高稻米的食味品质。小波分析作为光谱敏感特征提取的有效方法, 广泛应用于作物生理生化参数的估算, 然而基于小波分析的作物品质参数估算, 在米粉、稻穗水平上的应用还未见报道。本文以室内获取的水稻米粉与干穗反射光谱为基础数据源, 通过连续小波光谱变换、敏感小波特征提取、共性特征分析和预测模型构建等4个步骤, 明确不同光谱参数预测水稻籽粒直链淀粉含量的性能, 最终实现在器官水平的直链淀粉含量高光谱预测。结果表明: (1) 相较归一化光谱指数, 敏感小波特征可有效地提高直链淀粉含量预测精度, 预测模型更具普适性和鲁棒性; (2) 从米粉光谱提取的敏感小波特征WF2037,6, 与籽粒直链淀粉含量相关性较高(R2= 0.59), 对独立年份的样本预测效果较好(RMSE = 1.51%, Bias = 0.44%, RRMSE = 23.50%), 并可直接应用于干穗光谱(R2= 0.62, RMSE = 1.49%, Bias = -0.17%, RRMSE = 25.76%)。本文利用连续小波光谱分析, 提取了米粉和稻穗水平的直链淀粉敏感小波特征WF2037,6, 建立了高精度预测模型, 拓宽了连续小波光谱分析的应用范围, 为冠层水平水稻籽粒直链淀粉含量的高光谱估算奠定基础。

关键词: 米粉, 稻穗, 反射光谱, 水稻籽粒直链淀粉含量, 光谱指数, 连续小波光谱分析

Abstract:

Grain amylose content (GAC) is a critical factor affecting the cooking and eating quality of rice. Remote sensing technology can be used to obtain amylose content timely and accurately, which are useful for the establishment and implementation of corresponding cultivation to improve the quality of rice taste. As an effective method for spectral feature extraction, continuous wavelet analysis (CWA) has been widely used to estimate crop physiological and biochemical parameters. However, none of previous studies have used CWA for crop quality estimation and investigated the application of CWA to dried grain powder reflectance spectra acquired in the laboratory for absorption feature extraction. The method was conducted in four steps as below: continuous wavelet transforms, extraction of sensitive wavelet features, analysis of common features, and construction of predictive models. Finally, we estimated GAC on grain scale and compared the performance of different spectral features. The results were as follows: (1) The performance of sensitive wavelet features was better vegetation indices and the independent validation confirmed this superiority; (2) The GAC could be estimated from WF2037,6 with a high R2= 0.59 and the accuracy assessed with validation data from an independent year was RMSE = 1.51%, Bias = 0.44%, RRMSE = 23.50%. The results derived from dried grain powder could be applied to dried panicles (R2= 0.62, RMSE = 1.49%, Bias = -0.17%, RRMSE = 25.76%). This study determined the optimal amylose-sensitive wavelet feature WF2037,6. It can provide new insight into GAC estimation with hyperspectral remote sensing and this method would advance the understanding of rice quality estimation from reflectance spectra at grain and canopy levels.

Key words: rice powder, rice panicle, reflectance spectra, grain amylose content, spectral index, continuous wavelet analysis

表1

米粉及干穗光谱获取数量"

数据集
Data set		 年份
Year		 样本数量
Number of samples		 目的
Objective
米粉光谱Dried grain powder spectra 2018 48 建模集Modeling set
米粉光谱Dried grain powder spectra 2017 16 验证集Validation set
干穗光谱Dried panicle spectra 2018 29 建模集Modeling set
干穗光谱Dried panicle spectra 2018 19 验证集Validation set

图1

米粉光谱测试照片 A: 测试环境; B: 光纤视野场景。"

图2

稻穗光谱测试照片 A: 测试环境; B: 光纤视野场景。"

图3

米粉反射光谱(A)、不同尺度下的小波系数(B)及水稻籽粒直链淀粉敏感小波特征分布图(C)"

图4

水稻叶(A)、茎(B)及穗(C)氮含量随生育期的变化趋势 N0: 施氮量为0 kg hm-2; N1: 施氮量为210 kg hm-2; N2: 施氮量为300 kg hm-2; N3: 施氮量为390 kg hm-2。"

表2

水稻籽粒直链淀粉含量统计特征"

年份
Year		 样本数
No. of samples		 最小值
Min. (%)		 最大值
Max. (%)		 平均值
Mean (%)		 标准差
SD (%)
2017 48 3.13 11.21 6.27 2.19
2018 48 2.10 9.81 5.65 2.21

图5

不同施氮量(A)、播栽方式(B)及品种(C)对水稻籽粒直链淀粉含量的影响 N0: 施氮量为0 kg hm-2; N1: 施氮量为210 kg hm-2; N2: 施氮量为300 kg hm-2; N3: 施氮量为390 kg hm-2。T1: 毯苗移栽; T2: 钵苗移栽。V1: 南粳9108; V2: 甬优2640。"

图6

成熟期水稻器官反射光谱 黑色箭头代表干物质成分的主要吸收波段位置。"

表3

短波红外波段重要化学物质的吸收特征[28]"

波长
Wavelength (nm)		 干物质
Dry matter constituent		 波长
Wavelength (nm)		 干物质
Dry matter constituent
1690 木质素、蛋白质 Lignin, protein 2060 蛋白质、氮 Protein, nitrogen
1820 纤维素 Cellulose 2130 蛋白质 Protein
1900 淀粉 Starch 2180 蛋白质、氮 Protein, nitrogen
1980 蛋白质 Protein 2240 蛋白质 Protein
2000 淀粉 Starch 2300 蛋白质、氮Protein, nitrogen

表4

水稻抽穗后各生育时期米粉或干穗光谱指数与籽粒直链淀粉含量的相关性"

生育时期
Growth stage		 米粉Dried grain powder 干穗Dried panicle
光谱参数
Spectral feature		 决定系数
R2		 光谱参数
Spectral feature		 决定系数
R2
乳熟期Milk stage — — NDAI1760,1585 0.59**
蜡熟期Dough ripening stage NDAI1505,1455 0.61** NDAI1835,1810 0.60**
黄熟期Yellow ripening stage NDAI1345,1210 0.39** NDAI2265,2135 0.70**
完熟期Full ripening stage NDAI1600,1430 0.49** NDAI1290,1175 0.64**

图7

基于NDAI与籽粒直链淀粉含量线性关系的决定系数(R2)等值线图 A: 米粉; B: 干穗。"

图8

水稻籽粒直链淀粉含量与(A)蜡熟期米粉NDAI1505,1455及(B)黄熟期干穗NDAI2265,2135的回归关系图"

图9

米粉水平直链淀粉敏感小波特征分布图 A: 蜡熟期; B: 黄熟期; C: 完熟期; D: 相应波长的米粉反射光谱。蓝色方框代表不同时期存在的相同敏感小波特征。蓝色虚线代表对应波长。黑色箭头代表淀粉吸收波段。"

表5

水稻灌浆后各生育时期米粉小波系数与籽粒直链淀粉含量的相关性"

生育时期
Growth stage		 小波系数
Wavelet feature		 决定系数
R2
蜡熟期 WF1380,3 0.62**
Dough ripening stage WF2037,6 0.59**
黄熟期 WF1812,3 0.43**
Yellow ripening stage WF1550,3 0.36**
完熟期 WF1600,3 0.45**
Full ripening stage WF1375,3 0.30*

图10

水稻籽粒直链淀粉含量与米粉光谱(A) WF1380,3及(B) WF2037,6的回归关系图"

表6

水稻抽穗后各生育期干穗小波系数与籽粒直链淀粉含量的相关性"

生育期Growth stage 小波系数Wavelet feature 决定系数R2
乳熟期 WF1155,6 0.49**
Milk stage WF1895,4 0.49**
蜡熟期 WF1835,3 0.74**
Dough ripening stage WF2350,3 0.64**
黄熟期 WF1692,4 0.67**
Yellow ripening stage WF2030,5 0.65**
完熟期 WF2250,6 0.60**
Full ripening stage WF2050,6 0.55**

图11

干穗水平直链淀粉敏感小波特征分布图 A: 乳熟期; B: 蜡熟期; C: 黄熟期; D: 完熟期; E: 相应波长的米粉反射光谱。蓝色方框代表不同时期存在的相同敏感小波特征。黄色方框代表米粉光谱直链淀粉敏感小波系数。蓝色虚线代表对应波长。黑色箭头代表淀粉吸收波段。"

图12

水稻籽粒直链淀粉含量与干穗光谱(A) WF1835,3及(B) WF2350,3的回归关系图"

图13

米粉、干穗反射光谱和小波系谱对水稻籽粒直链淀粉含量变化的响应 A, B: 不同直链淀粉含量米粉、干穗样品反射光谱; C, D, E, F: 不同直链淀粉含量米粉、干穗样品小波系谱。蓝色箭头代表直链淀粉敏感小波特征对应的波长位置。"

图14

直链淀粉敏感小波特征分布图 A: 米粉; B: 干穗。"

表7

水稻籽粒直链淀粉含量光谱预测精度对照表"

测试器官
Organ of
measurements		 最佳时期
Optimal growth stage		 光谱参数
Spectral feature		 均方根误差
RMSE (%)		 偏移值
Bias (%)		 相对均方根误差
RRMSE (%)
米粉
Dried grain
powder		 蜡熟期
Dough ripening stage		 NDAI1505,1455 3.96 3.30 61.63
WF1380,3 7.73 7.32 120.31
WF2037,6 1.51 0.44 23.50
WF2037,5 3.40 2.14 52.92
干穗
Dried panicle		 NDAI2265,2135 2.17 0.61 37.52
WF1835,3 1.91 -0.84 56.17
WF2037,6 1.49 -0.17 25.76
WF2037,5 2.77 -1.01 81.74

图15

水稻籽粒直链淀粉含量与干穗光谱WF2037,6的回归关系图"

图16

基于光谱指数和小波系数的水稻籽粒直链淀粉含量模型预测值与实测值的1:1关系图 A, B: 米粉NDAI1505,1455, WF2037,6-GAC; C, D: 干穗NDAI2265,2135, WF2037,6-GAC。GAC: 籽粒直链淀粉含量。"

图17

不同生育期米粉及干穗的直链淀粉敏感小波特征分布图及器官反射光谱 米粉A~C: 蜡熟期、黄熟期、完熟期; 干穗D~G: 乳熟期、蜡熟期、黄熟期、完熟期。蓝色方框代表不同时期存在的相同敏感小波系数。蓝色虚线代表对应波长。黑色箭头代表淀粉吸收波段。"

[1] Thomas R, Wan-Nadiah W A, Bhat R. Physiochemical properties, proximate composition, and cooking qualities of locally grown and imported rice varieties marketed in Penang, Malaysia. Int Food Res, 2013,20:1679-1685.
[2] Umemoto T, Nakamura Y, Ishikura N. Activity of starch synthase and the amylose content in rice endosperm. Phytochemistry, 1995,40:1613-1616.
doi: 10.1016/0031-9422(95)00380-P
[3] Zhou Z, Robards K, Helliwell S, Blanchard C. Ageing of stored rice: changes in chemical and physical attributes. J Cereal Sci, 2002,35:65-78.
doi: 10.1006/jcrs.2001.0418
[4] Ata-UI-Karim S T, Zhu Y, Cao Q, Rehmani M I A, Cao W X, Tang L. In-season assessment of grain protein and amylose content in rice using critical nitrogen dilution curve. Eur J Agron, 2017,90:139-151.
doi: 10.1016/j.eja.2017.08.001
[5] 韩天富, 马常宝, 黄晶, 柳开楼, 薛彦东, 李冬初, 刘立生, 张璐, 刘淑军, 张会民. 基于Meta分析中国水稻产量对施肥的响应特征. 中国农业科学, 2019,52:1918-1929.
Han T F, Ma C B, Huang J, Liu K L, Xue Y D, Li D C, Liu L S, Zhang L, Liu S J, Zhang H M. Variation in rice yield response to fertilization in China: Meta-analysis. Sci Agric Sin, 2019,52:1918-1929 (in Chinese with English abstract).
[6] Duan S H, Fang R S, Zhu R S, Xian T. Remote estimation of rice yield with unmanned aerial vehicle (UAV) data and spectral mixture analysis. Front Plant Sci, 2019,10:169-178.
doi: 10.3389/fpls.2019.00169
[7] Raquel A O, Roope N, Oiva N, Laura N, Katja A, Jere K, Lauri J, Niko V, Somayeh N, Lauri M, Teemu H, Eija H. Machine learning estimators for the quality and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry. Remote Sens Environ, 2020,246:111830.
doi: 10.1016/j.rse.2020.111830
[8] Berger K, Verrelst J, Fret J, Wang Z, Hank T. Crop nitrogen monitoring: recent progress and principal developments in the context of imaging spectroscopy missions. Remote Sens Environ, 2020,242:111758.
doi: 10.1016/j.rse.2020.111758
[9] Basnet B B, Apan A A, Kelly R M, Jensen T A, Strong W M, Butler D G. Relating satellite imagery with grain protein content. J Spat Sci, 2003,80:22-27.
[10] Mutanga O, Skidmore A K. Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa. Remote Sens Environ, 2004,90:104-115.
doi: 10.1016/j.rse.2003.12.004
[11] Zhao C, Liu L, Wang J, Huang W, Song X, Li C. Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress. Int J Appl Earth Observ Geoinf, 2005,7:1-9.
doi: 10.1016/j.jag.2004.10.002
[12] 宋晓宇, 黄文江, 王纪华, 刘良云, 李存军. ASTER卫星遥感影像在冬小麦品质监测方面的初步应用. 农业工程学报, 2006,22(9):148-153.
Song X Y, Huang W J, Wang J H, Liu L Y, Li C J. Preliminary application of ASTER images in winter wheat quality monitoring. Trans CSAE, 2006,22(9):148-153 (in Chinese with English abstract).
[13] 谭昌伟, 王纪华, 黄文江, 王君婵, 朱新开, 郭文善. 基于TM和PLS的冬小麦籽粒蛋白质含量预测. 农业工程学报, 2011,27(3):388-392.
Tan C W, Wang J H, Huang W J, Wang J C, Zhu X K, Guo W S. Predicting grain protein content in winter wheat based on TM images and partial least squares regression. Trans CSAE, 2011, 27(3):388-392 (in Chinese with English abstract).
[14] 王利民, 刘佳, 杨福刚, 杨玲波, 姚保民, 王小龙. 基于GF-1卫星遥感数据识别京津冀冬小麦面积. 作物学报, 2018,44:762-773.
Wang L M, Liu J, Yang F G, Yang L B, Yao B M, Wang X L. Acguisition of winter wheat area in the Beijing-Tianjin-Hebei region with GF-1 satellite data. Acta Agron Sin, 2018,44:762-773 (in Chinese with English abstract).
[15] 李卫国, 王纪华, 赵春江, 刘良云, 宋晓宇, 童庆禧. 基于NDVI和氮素积累的冬小麦籽粒蛋白质含量预测模型. 遥感学报, 2008,3:506-514.
Li W G, Wang G H, Zhao C J, Liu L Y, Song X Y, Tong Q X. A model for predicting content in winter wheat grain based on Land-Sat TM image and nitrogen accumulation. J Remote Sens, 2008,3:506-514 (in Chinese with English abstract).
[16] 王大成, 张东彦, 李宇飞, 秦其明, 王纪华, 范闻捷, 陈诗琳. 结合HJ1A/B卫星数据和生态因子的籽粒品质监测. 红外与激光工程, 2013,42:780-786.
Wang D C, Zhang D Y, Li Y M, Qin Q M, Wang J H, Fan W J, Chen S L. Monitoring wheat quality based on HJ1A/B remote sensing data and ecological factors. Infrar Laser Eng, 2013,42:780-786 (in Chinese with English abstract).
[17] 田永超, 朱艳, 曹卫星, 范雪梅, 刘小军. 利用冠层反射光谱和叶片SPAD值预测小麦籽粒蛋白质和淀粉的积累. 中国农业科学, 2004,37:808-813.
Tian Y C, Zhu Y, Cao W X, Fan X M, Liu X J. Monitoring protein and starch accumulation in wheat grains with Leaf SPAD and canopy spectral reflectance. Sci Agric Sin, 37:808-813 (in Chinese with English abstract).
[18] 薛利红, 朱艳, 张宪, 曹卫星. 利用冠层反射光谱预测小麦籽粒品质指标的研究. 作物学报, 2004,30:1036-1041.
Xue L H, Zhu Y, Zhang X, Cao W X. Predicting wheat grain quality with canopy reflectance spectra. Acta Agron Sin, 2004,30:1036-1041 (in Chinese with English abstract).
[19] Pettersson C G, Eckersten H. Prediction of grain protein in spring malting barley grown in northern Europe. Eur J Agron, 2007,27:205-214.
doi: 10.1016/j.eja.2007.04.002
[20] Wang Z J, Wang J H, Liu L Y, Huang W J, Zhao C J, Wang C Z. Prediction of grain protein content in winter wheat (Triticum aestivum L.) using plant pigment ratio (PPR). Field Crops Res, 2004,90:311-321.
doi: 10.1016/j.fcr.2004.04.004
[21] 宋晓宇, 王纪华, 杨贵军, 崔贝, 常红. 基于叶片及冠层叶绿素参数的冬小麦籽粒蛋白质含量预测研究. 光谱学与光谱分析, 2014,34:1917-1921.
Song X Y, Wang J H, Yang G J, Cui B, Chang H. Winter wheat GPC estimation based on leaf and canopy chlorophyll parameters. Spectr Spect Anal, 2014,34:1917-1921 (in Chinese with English abstract).
[22] 谢晓金, 李秉柏, 朱红霞. 利用高光谱数据估测不同温度胁迫下的水稻籽粒中粗蛋白和直链淀粉含量. 农业现代化研究, 2012,33:481-484.
Xie X J, Li B B, Zhu H X. Estimating contents of crude protein and amylose content in rice grain by hyper-spectral under different high temperature stress. Res Agric Modern, 2012,33:481-484 (in Chinese with English abstract).
[23] 刘冰峰. 夏玉米不同生育时期生理生态参数的高光谱遥感监测模型. 西北农林科技大学博士学位论文, 陕西杨凌, 2016.
Liu B F. Monitoring Models of Physiological and Ecological Parameters of Summer Maize Based on Hyperspectral Remote Sensing at Different Growth Stages. PhD Dissertation of Northwest A&F University, Yangling, Shaanxi, China, 2016 (in Chinese with English abstract).
[24] 田容才, 高志强, 卢俊玮. 基于冠层光谱的早籼稻籽粒粗蛋白含量估测. 作物杂志, 2020, (4):188-194.
Tian R C, Gao Z Q, Lu J Q. Estimation of crude protein content in grain of early indica rice based on canopy spectrum. Crops, 2020, (4):188-194 (in Chinese with English abstract).
[25] 谢莉莉, 王福民, 张垚, 黄敬峰, 胡景辉, 王飞龙, 姚晓萍. 基于多生育期光谱变量的水稻直链淀粉含量监测. 农业工程学报, 2020,36(8):173-181.
Xie L L, Wang F M, Zhang Y, Huang J F, Hu J H, Wang F L, Yao X P. Monitoring of amylose content in rice based on spectral variables at the multiple growth stages. Trans CSAE, 2020,36(8):173-181 (in Chinese with English abstract).
[26] 黄文江, 王纪华, 刘良云, 赵春江, 宋晓宇, 马智宏. 冬小麦品质的影响因素及高光谱遥感监测方法. 遥感技术与应用, 2004,10:143-148.
Huang W J, Wang J H, Liu L Y, Zhao C J, Song X Y, Ma Z H. Study on grain quality effecting factors and monitoring methods by using hyperspectral data in winter wheat. Remote Sens Technol Appl, 2004,10:143-148 (in Chinese with English abstract).
[27] 王纪华, 黄文江, 赵春江, 杨敏华, 王之杰. 利用光谱反射率估算叶片生化组分和籽粒品质指标研究. 遥感学报, 2003,7:277-284.
Wang J H, Huang W J, Zhao C J, Yang M H, Wang Z J. The inversion of leaf biochemical components and grain quality indicators of winter wheat with spectral reflectance. J Remote Sens, 2003,7:277-284 (in Chinese with English abstract).
[28] Curran P. Remote sensing of foliar chemistry. Remote Sens Environ, 1990,30:271-278.
doi: 10.1016/0034-4257(89)90069-2
[29] 何理. 水稻叶片氮素含量及产量、相关品质高光谱预测模型的初步研究. 扬州大学硕士学位论文, 江苏扬州, 2014.
He L. Preliminary Study on Hyperspectral Prediction Model of Leaf Nitrogen Content, Yield and Related Quality in Rice. MS Thesis of Yangzhou University, Yangzhou, Jiangsu, China, 2014 (in Chinese with English abstract).
[30] Curran P J, Dungan J L, Peterson D L. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies. Remote Sens Environ, 2001,76:349-359.
doi: 10.1016/S0034-4257(01)00182-1
[31] Grossman Y L, Ustin S L, Jacquemoud S, Sanderson E W, Schmuck G, Verdebout J. Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data. Remote Sens Environ, 1996,56:182-193.
doi: 10.1016/0034-4257(95)00235-9
[32] Kokaly R F, Clark R N. Spectroscopic determination of leaf biochemistry using Band-Depth analysis of absorption features and stepwise multiple linear regression. Remote Sens Environ, 1999,67:267-287.
doi: 10.1016/S0034-4257(98)00084-4
[33] 唐延林, 黄敬峰, 王人潮. 利用高光谱法估测稻穗稻谷的粗蛋白质和粗淀粉含量. 中国农业科学, 2004,37:1282-1287.
Tang Y L, Huang J F, Wang R C. Study on estimating the contents of crude protein and crude starch in rice panicle and paddy by hyperspectra. Sci Agric Sin, 2004,37:1282-1287 (in Chinese with English abstract).
[34] 刘芸, 唐延林, 黄敬峰, 蔡绍洪, 楼佳. 利用高光谱数据估测水稻米粉中粗蛋白粗淀粉和直链淀粉含量. 中国农业科学, 2008,41:62-68.
Liu L, Tang Y L, Huang J F, Cai S H, Lou J. Contents of crude protein, crude starch and amylose in rice flour by hyperspectral data. Sci Agric Sin, 2008,41:62-68 (in Chinese with English abstract).
[35] 颜士博. 基于高分数据的水稻品质监测方法的研究. 杭州师范大学硕士学位论文, 浙江杭州, 2017.
Yan S B. Study on Rice Quality Monitoring Method Based on High Resolution Data. MS Thesis of Hangzhou Normal University, Hangzhou, Zhejiang, China, 2017 (in Chinese with English abstract).
[36] Gitelson A A, Merzlyak M N. Remote estimation of chlorophyll content in higher plant leaves. Int J Remote Sens, 1997,18:2691-2697.
doi: 10.1080/014311697217558
[37] 姚霞, 朱艳, 田永超, 冯伟, 曹卫星. 小麦叶层氮含量估测的最佳高光谱参数研究. 中国农业科学, 2009,42:2716-2725.
Yao X, Zhu Y, Tian Y C, Feng W, Cao W X. Research of the optimum hyperspectral vegetation indices on monitoring the nitrogen content in wheat leaves. Sci Agric Sin, 2009,42:2716-2725 (in Chinese with English abstract).
[38] Blackburn G, Ferwerda J. Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis. Remote Sens Environ, 2008,112:1614-1632.
doi: 10.1016/j.rse.2007.08.005
[39] 汤旭光, 宋开山, 刘殿伟, 王宗明, 张柏, 杜嘉, 曾丽红, 姜广甲, 王远东. 基于可见/近红外反射光谱的大豆叶绿素含量估算方法比较. 光谱学与光谱分析, 2011,31:371-374.
Tang X G, Song K S, Liu D W, Wang Z M, Zhang B, Du J, Zeng L H, Jiang G J, Wang Y D. Comparison of methods for estimating chlorophyll content based on visual/near infrared reflection spectra. Spectr Spect Anal, 2011,31:371-374 (in Chinese with English abstract).
[40] Rivard B, Feng J, Gallie A, Sanchez-Azofeifa A G. Continuous wavelets for the improved use of spectral libraries and hyperspectral data. Remote Sens Environ, 2008,112:2850-2862.
doi: 10.1016/j.rse.2008.01.016
[41] 方美红, 刘湘南. 小波分析用于水稻叶片氮含量高光谱反演. 应用科学学报, 2010,28:387-393.
Fang M H, Liu X N. Estimation of nitrogen content in rice leaves with hyperspectral reflectance measurements using wavelet analysis. J Appl Sci, 2010,28:387-393 (in Chinese with English abstract).
[42] Li D, Cheng T, Jia M, Zhou K, Lu N, Yao X, Tian Y, Zhu Y, Cao W X. PROCWT: Coupling PROSPECT with continuous wavelet transform to improve the retrieval of foliar chemistry from leaf bidirectional reflectance spectra. Remote Sens Environ, 2018,206:1-14.
doi: 10.1016/j.rse.2017.12.013
[43] Li D, Wang X, Zheng H B, Zhou K, Yao X, Tian Y C, Zhu Y, Cao W X, Cheng T. Estimation of area- and mass-based leaf nitrogen contents of wheat and rice crops from water-removed spectra using continuous wavelet analysis. Plant Methods, 2018,14:1-20.
doi: 10.1186/s13007-017-0271-6
[44] Cheng T, Riaño D, Ustin S L. Detecting diurnal and seasonal variation in canopy water content of nut tree orchards from airborne imaging spectroscopy data using continuous wavelet analysis. Remote Sens Environ, 2014,143:39-53.
doi: 10.1016/j.rse.2013.11.018
[45] 中国科学院上海植物生理研究所. 现代植物生理学实验指南. 北京, 科学出版社. 1999. pp 79-85.
Shanghai Institute of Plant Physiology, Chinese Academy of Sciences. Guidelines for Modern Plant Physiology Experiments. Beijing: Science Press, 1999. pp 79-85(in Chinese).
[46] Cheng T, Rivard B, Sánchez-Azofeifa A G, Féret J B, Jacquemoud S, Ustin S L. Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis. ISPRS J Photogramm Remote Sens, 2014,87:28-38.
doi: 10.1016/j.isprsjprs.2013.10.009
[47] Cheng T, Rivard B, Sanchez-Azofeifa A G, Feret J B, Jacquemoud S, Ustin S L. Predicting leaf gravimetric water content from foliar reflectance across a range of plant species using continuous wavelet analysis. J Plant Physiol, 2012,169:1134-1142.
doi: 10.1016/j.jplph.2012.04.006
[48] 苗茜. 基于小波变换的芦苇叶绿素含量的地物高光谱反演研究. 首都师范大学硕士学位论文, 北京, 2013.
Miao Q. Hyperspectral Inversion of Chlorophyll Content in Phragmites Australis based on Wavelet Transform. MS Thesis of Capital Normal University, Beijing, China, 2013 (in Chinese with English abstract).
[49] 郭洋洋, 张连蓬, 王德高, 马维维. 小波分析在植物叶绿素高光谱遥感反演中的应用. 测绘通报, 2010, (8):31-33.
Guo Y Y, Zhang L P, Wang D G, Ma W W. Application of wavelet analysis for determining chlorophyll concentration in vegetation by hyperspectral reflectance. Bull Surv Map, 2010, (8):31-33 (in Chinese with English abstract).
[50] 谢巧云, 黄文江, 蔡淑红, 梁栋, 彭代亮, 张清, 黄林生, 杨贵军, 张东彦. 冬小麦叶面积指数遥感反演方法比较研究. 光谱学与光谱分析, 2014,34:1352-1356.
Xie Q Y, Huang W J, Cai S H, Liang D, Peng D L, Zhang Q, Huang L S, Yang G J, Zhang D J. Comparative study on remote sensing inversion methods for estimation winter wheat leaf area index. Spect Spect Anal, 2014,34:1352-1356 (in Chinese with English abstract).
[51] Ghoshal A, Martin W N, Schulz M J, Chattopadhyay A, Prosser W H, Kim H S. Health monitoring of composite plates using acoustic wave propagation, continuous sensors and wavelet analysis. J Reinf Comp, 2015,26:95-112.
[52] Huang G, Newchurch M J, Kuang S, Buckley P I, Cantrell W, Wang L. Definition and determination of ozone laminae using continuous wavelet transform (CWT) analysis. Atmos Environ, 2015,104:125-131.
doi: 10.1016/j.atmosenv.2014.12.027
[53] Hsu Y C, Tseng M C, Wu Y P, Lin M Y, Wei F J, Hwu K K. Genetic factors responsible for eating and cooking qualities of rice grains in a recombinant inbred population of an inter-subspecific cross. Mol Breed, 2014,34:655-673.
doi: 10.1007/s11032-014-0065-8
[54] Yang F, Chen Y, Tong C, Huang Y, Xu F, Li K. Association mapping of starch physicochemical properties with starch synthesis-related gene markers in nonwaxy rice (Oryza sativa L.). Mol Breed, 2014,34:1747-1763.
doi: 10.1007/s11032-014-0135-y
[55] Hirano H Y, Sano Y. Molecular characterization of the waxy locus of rice (Oryza sativa). Plant Cell Physiol, 1991,32:989-997.
doi: 10.1093/oxfordjournals.pcp.a078186
[56] Furon A C, Warland J S, Wagner-Riddle C. Analysis of scaling-up resistances from leaf to canopy using numerical simulations. Agron J, 2007,99:135-141.
[57] Read C, Wright I J, Westoby M. Scaling-up from leaf to canopy-aggregate properties in sclerophyll shrub species. Austr Entomol, 2010,31:310-316.
[58] 魏友华, 王瑶, 何雪梅, 郭科, 常睿春. 基于“高分五号”遥感图像的地物分类方法. 现代电子技术, 2020, (3):85-88.
Wei Y H, Wang Y, He X M, Guo K, Chang R C. Classification method of ground objects based on the remote sensing image of GF-5. Modern Electron Technol, 2020, (3):85-88 (in Chinese with English abstract).
[59] 赵瑞, 崔希民, 刘超. GF-5高光谱遥感影像的土壤有机质含量反演估算研究. 中国环境科学, 2020,40:3539-3545.
Zhao R, Cui X M, Liu C. Inversion estimation of soil organic matter content based on GF-5 hyperspectral remote sensing image. China Environ Sci, 2020,40:3539-3545 (in Chinese with English abstract).
[60] 李小朋, 王术, 黄元财, 贾宝艳, 王岩, 曾群云. 株行距配置对齐穗期粳稻冠层结构及产量的影响. 应用生态学报, 2015,26:3329-3336.
Li X P, Wang S, Huang Y C, Jia B Y, Wang Y, Zeng Q Y. Effects of spacing on the yields and canopy structure of japonica rice at full heading stage. China J Appl Ecol, 2015,26:3329-3336 (in Chinese with English abstract).
[61] 方雨晨, 田庆久. 归一化植被指数的土壤背景影响去除. 遥感信息, 2017, (6):8-13.
Fang Y C, Tian Q J. Soil effect removal of NDVI in farmland based on theory of mixing spectral. Remote Sens Inf, 2017, (6):8-13 (in Chinese with English abstract).
[62] 褚旭. 不同覆盖度条件下水稻叶层氮素营养的高光谱监测研究. 南京农业大学硕士学位论文, 江苏南京, 2013.
Chu X. Monitoring Leaf Nitrogen Nutrition under Different Vegetation Coverage Conditions Using Hyperspectrum Data in Rice. MS Thesis of Nanjing Agricultural University, Nanjing, Jiangsu, China, 2013 (in Chinese with English abstract).
[1] 白宗璠,竞霞,张腾,董莹莹. MDBPSO算法优化的全波段光谱数据协同冠层SIF监测小麦条锈病[J]. 作物学报, 2020, 46(8): 1248-1257.
[2] 牛欣宁,王步军. 玉米粉中伏马毒素FB1基体标准物质的评价[J]. 作物学报, 2020, 46(7): 1128-1133.
[3] 杨海波,张加康,杨柳,贾禹泽,刘楠,李斐. 品种和生育时期对冠层光谱指数(NDVI)估测马铃薯植株氮素浓度的影响[J]. 作物学报, 2020, 46(6): 950-959.
[4] 韩康, 于静, 石晓华, 崔石新, 樊明寿. 不同光谱指数反演马铃薯叶片氮累积量的研究[J]. 作物学报, 2020, 46(12): 1979-1990.
[5] 徐正浩,谢国雄,周宇杰,高屾. 不同株型和化感作用特性水稻对3种稻田主要杂草的干扰控制作用[J]. 作物学报, 2013, 39(07): 1293-1302.
[6] 吴琼,齐波,赵团结,姚鑫锋,朱艳,盖钧镒. 高光谱遥感估测大豆冠层生长和籽粒产量的探讨[J]. 作物学报, 2013, 39(02): 309-318.
[7] 王方永, 王克如, 李少昆, 高世菊, 肖春华, 陈兵, 陈江鲁, 吕银亮, 刁万英. 应用两种近地可见光成像传感器估测棉花冠层叶片氮素状况[J]. 作物学报, 2011, 37(06): 1039-1048.
[8] 陆大雷, 郭换粉, 董策, 陆卫平. 鲜食期和成熟期糯玉米粉理化特性的差异[J]. 作物学报, 2010, 36(12): 2170-2178.
[9] 王方永, 王克如, 李少昆, 陈兵, 陈江鲁. 利用数码相机和成像光谱仪估测棉花叶片叶绿素和氮素含量[J]. 作物学报, 2010, 36(11): 1981-1989.
[10] 朱艳;田永超;马吉锋;姚霞;刘小军;曹卫星. 小麦叶片叶绿素荧光参数与反射光谱特征的关系[J]. 作物学报, 2007, 33(08): 1286-1292.
[11] 周冬琴;朱艳;姚霞;田永超;曹卫星. 基于水稻冠层光谱特征构建粳型水稻籽粒蛋白质含量预测模型[J]. 作物学报, 2007, 33(08): 1219-1225.
[12] 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[J]. 作物学报, 2006, 32(09): 1316-1322.
[13] 李映雪;朱艳;田永超;姚霞;秦晓东;曹卫星. 小麦叶片氮含量与冠层反射光谱指数的定量关系[J]. 作物学报, 2006, 32(03): 358-362.
[14] 李映雪;朱艳;田永超;姚霞 ;秦晓东;曹卫星. 小麦叶片氮素状况与冠层反射光谱指数的关系[J]. 作物学报, 2006, 32(02): 203-209.
[15] 田永超;朱艳;曹卫星. 用冠层反射光谱预测小麦叶片糖氮量及糖氮比[J]. 作物学报, 2005, 31(03): 355-360.
Viewed
Full text


Abstract

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