Study on Dynamic Estimation of Canopy and Plant Level Nitrog
发布时间：2021-04-24 04:34
随着中国城市化进程提速、耕地退化加剧、传统种植模式变革等,长江流域水稻种植面积逐年递减,科学地进行水稻种植,既保护生态环境,又维护粮食安全成为热点问题。传统农田管理措施中,为提高产量而大量施用氮肥,在农田生态环境方面已经引起了诸多问题。氮素是作物生长和发育的基本元素,在整个生长季节氮素的有效供给情况都影响着最终产量。虽然缺氮会导致作物产量和经济效益的大幅度下降,但是过度施氮也会降低氮利用率,并造成严重的环境威胁。中国在水稻种植中氮施用量比世界平均水平高70%左右。为了合理进行氮肥施用,提高氮利用率,精准氮肥管理已成为现代农业特别是华中地区水稻生产的热点问题之一。在减少氮流失环境胁迫的前提下确保最大产量和品质,需要在水稻关键生长期制定有效的养分管理措施。因此,准确快速获取作物地上部分生物量和氮含量信息,将为水稻生长监测和养分管理提供重要决策依据。传统的调查取样和分析方法具有破坏性,而且费时费力;无损高光谱遥感及分析技术有助于提高氮的精确管理。本文主要利用高光谱数据评估水稻不同物候期的氮营养状况,建立模型无损估计冠层和植株水平的重要生理生化指标,包括叶面积指数（LAI）,冠层干物质重量（C...
【文章来源】：华中农业大学湖北省 211工程院校 教育部直属院校
【文章页数】：141 页
【学位级别】：博士
【文章目录】：
摘要
ABSTRACT
List of Acronyms and abbreviations
CHAPTER 1 INTRODUCTION
1.1 ADVANCES IN CROP SPECTRAL MONITORING
1.2 NON-DESTRUCTIVE TECHNOLOGY FOR CROP MONITORING
1.2.1 Remote sensing for crop monitoring
1.2.2 Canopy spectral diagnose
1.3 RICE
1.3.1 Global rice production
1.3.2 Rice phenology
1.3.3 Physiological and biochemical variables for rice assessment
1.4 SPECTRAL ANALYSIS FOR RICE ASSESSMENT
1.4.1 Canopy spectral reflectance （CSR） of rice
1.4.2 Hyperspectral vegetative indices （HVIs）
1.4.3 Sensitivity of HVIs
1.4.4 Nutrient status assessment using（HVIs）
1.5 CANOPY LEVEL VARIABLES
1.5.1 Leaf area index （LAI）
1.5.2 Canopy dry weight （CDW）
1.5.3 Canopy nitrogen contents （CNC）
1.5.4 Canopy nitrogen accumulation （CNA）
1.6 PLANT LEVEL VARIABLES
1.6.1 Plant dry weight （PDW）
1.6.2 Plant nitrogen contents （PNC）
1.6.3 Plant nitrogen accumulation （PNA）
1.7 STATISTICAL MODELS FOR CROP VARIABLES PREDICTION
CHAPTER 2 MATERIALS AND METHODS
2.1 EXPERIMENTAL SITE
2.2 DETERMINATION OF RICE CANOPY PROPERTIES
2.2.1 Leaf area index （LAI） measurements
2.2.2 Canopy nitrogen indicators （CDW, CNC and CNA） measurements
2.2.3 Plant nitrogen indicators （PDW, PNC and PNA） measurements
2.2.4 Canopy hyperspectral reflectance measurements
2.2.5 HVIs investigated for canopy biophysical and biochemical variables
2.3 DATA ANALYSIS AND MODELLING
2.3.1 Statistical analysis methods
2.3.2 Sensitivity analysis
2.3.3 Static and dynamic modelling
2.3.4 Validation of methods
CHAPTER 3 EVALUATING HVIS FOR LAI ESTIMATION OF ORYZA SATIVAL. OVER PHENOLOGICAL STAGES
3.1 VARIATION OF RICE LAI OVER THE PHENOLOGICAL STAGES UNDER VARIED NRATES
3.2 VARIATION IN RICE CSR OVER PHENOLOGICAL STAGES UNDER VARIED N RATES
3.3 RELATIONSHIP OF HVIS TO THE PHENOLOGICAL STAGES
3.4 EVALUATION AND VALIDATION OF HVIS FOR ESTIMATION OF RICE LAI OVERPHENOLOGICAL STAGES
3.5 SENSITIVITY ANALYSIS
3.6 DISCUSSION
3.7 SUMMARY
CHAPTER 4 ESTIMATION OF CANOPY NITROGEN （CN） LEVELINDICATORS OF ORYZA SATIVA L. USING HVIS OVER PHENOLOGICALSTAGES UNDER VARYING N RATES
4.1 VARIATION IN CN LEVEL INDICATORS （CDW, CNC, AND CNA） OVER PHENOLOGICALSTAGES
4.2 VARIATION IN RICE CSR OVER PHENOLOGICAL STAGES UNDER VARIED N RATES
4.3 RELATIONSHIP of CSR WITH CN LEVEL INDICATORS （CDW, CNC AND CNA）OVER PHENOLOGICAL STAGES UNDER VARIED N RATES
4.4 RELATIONSHIP of HVIs WITH CN LEVEL INDICATORS （CDW, CNC AND CNA）OVER PHENOLOGICAL STAGES UNDER VARIED N RATES
4.5 ESTABLISHMENT AND VALIDATION OF STATIC AND DYNAMIC MODELS FOR CNLEVEL INDICATORS （CDW, CNC AND CNA） OVER PHENOLOGICAL STAGES
4.6 DISCUSSION
4.7 SUMMARY
CHAPTER 5 ESTIMATION OF DYNAMIC PLANT NITROGEN （PN） LEVELINDICATORS OF ORYZA SATIVA L. USING HVIS OVER PHENOLOGICALSTAGES
5.1 VARIATION IN PN LEVEL INDICATORS （PDW, PNC AND PNA） OVERPHENOLOGICAL STAGES UNDER VARIED N RATES
5.2 RELATIONSHIP OF CSR WITH PN LEVEL INDICATORS （PDW, PNC AND PNA） OVERPHENOLOGICAL STAGES UNDER VARIED N RATES
5.3 RELATIONSHIP OF HVIs WITH PN LEVEL INDICATORS （PDW, PNC AND PNA） OVERPHENOLOGICAL STAGES UNDER VARIED N RATES
5.4 ESTABLISHMENT AND VALIDATION OF STATIC AND DYNAMIC MODELS FOR PNLEVEL INDICATORS （PDW, PNC AND PNA） OVER PHENOLOGICAL STAGES
5.5 DISCUSSION
5.6 SUMMARY
CHAPTER 6 GENERAL DISCUSSION AND CONCLUSION
6.1 SENSITIVE CSR REGIONS FOR RICE LAI CHARACTERIZATION
6.2 SENSITIVE CSR REGIONS FOR CN AND PN LEVEL INDICATORS
6.3 POTENTIAL OF HVIS FOR LAI,CN AND PN LEVEL INDICATORS
6.4 DYNAMIC MODELS FOR CN AND PN LEVEL INDICATORS OVER PHENOLOGICALSTAGES
6.5 CONCLUSIONS
6.6 NOVELTY POINTS
6.7 SUGGESTIONS FOR FUTURE RESEARCH
REFERENCES
ACKNOWLEDGEMENTS
PUBLICATIONS
【参考文献】：
期刊论文
[1]基于多种光谱仪的水稻前期植株氮积累量监测[J]. 陈青春,田永超,顾凯健,王薇,姚霞,曹卫星,朱艳. 农业工程学报. 2011(01)
[2]高光谱植被指数与水稻叶面积指数的定量关系[J]. 田永超,杨杰,姚霞,朱艳,曹卫星. 应用生态学报. 2009(07)
[3]Effect of N Fertilizers on Root Growth and Endogenous Hormones in Strawberry[J]. WANG Bo1,2, LAI Tao1, HUANG Qi-Wei1, YANG Xing-Ming1 and SHEN Qi-Rong1,2 1College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095 (China). 2Faculty of Horticulture, Soochow University, Suzhou 215006 (China). Pedosphere. 2009(01)
本文编号：3156687
【文章来源】：华中农业大学湖北省 211工程院校 教育部直属院校
【文章页数】：141 页
【学位级别】：博士
【文章目录】：
摘要
ABSTRACT
List of Acronyms and abbreviations
CHAPTER 1 INTRODUCTION
1.1 ADVANCES IN CROP SPECTRAL MONITORING
1.2 NON-DESTRUCTIVE TECHNOLOGY FOR CROP MONITORING
1.2.1 Remote sensing for crop monitoring
1.2.2 Canopy spectral diagnose
1.3 RICE
1.3.1 Global rice production
1.3.2 Rice phenology
1.3.3 Physiological and biochemical variables for rice assessment
1.4 SPECTRAL ANALYSIS FOR RICE ASSESSMENT
1.4.1 Canopy spectral reflectance （CSR） of rice
1.4.2 Hyperspectral vegetative indices （HVIs）
1.4.3 Sensitivity of HVIs
1.4.4 Nutrient status assessment using（HVIs）
1.5 CANOPY LEVEL VARIABLES
1.5.1 Leaf area index （LAI）
1.5.2 Canopy dry weight （CDW）
1.5.3 Canopy nitrogen contents （CNC）
1.5.4 Canopy nitrogen accumulation （CNA）
1.6 PLANT LEVEL VARIABLES
1.6.1 Plant dry weight （PDW）
1.6.2 Plant nitrogen contents （PNC）
1.6.3 Plant nitrogen accumulation （PNA）
1.7 STATISTICAL MODELS FOR CROP VARIABLES PREDICTION
CHAPTER 2 MATERIALS AND METHODS
2.1 EXPERIMENTAL SITE
2.2 DETERMINATION OF RICE CANOPY PROPERTIES
2.2.1 Leaf area index （LAI） measurements
2.2.2 Canopy nitrogen indicators （CDW, CNC and CNA） measurements
2.2.3 Plant nitrogen indicators （PDW, PNC and PNA） measurements
2.2.4 Canopy hyperspectral reflectance measurements
2.2.5 HVIs investigated for canopy biophysical and biochemical variables
2.3 DATA ANALYSIS AND MODELLING
2.3.1 Statistical analysis methods
2.3.2 Sensitivity analysis
2.3.3 Static and dynamic modelling
2.3.4 Validation of methods
CHAPTER 3 EVALUATING HVIS FOR LAI ESTIMATION OF ORYZA SATIVAL. OVER PHENOLOGICAL STAGES
3.1 VARIATION OF RICE LAI OVER THE PHENOLOGICAL STAGES UNDER VARIED NRATES
3.2 VARIATION IN RICE CSR OVER PHENOLOGICAL STAGES UNDER VARIED N RATES
3.3 RELATIONSHIP OF HVIS TO THE PHENOLOGICAL STAGES
3.4 EVALUATION AND VALIDATION OF HVIS FOR ESTIMATION OF RICE LAI OVERPHENOLOGICAL STAGES
3.5 SENSITIVITY ANALYSIS
3.6 DISCUSSION
3.7 SUMMARY
CHAPTER 4 ESTIMATION OF CANOPY NITROGEN （CN） LEVELINDICATORS OF ORYZA SATIVA L. USING HVIS OVER PHENOLOGICALSTAGES UNDER VARYING N RATES
4.1 VARIATION IN CN LEVEL INDICATORS （CDW, CNC, AND CNA） OVER PHENOLOGICALSTAGES
4.2 VARIATION IN RICE CSR OVER PHENOLOGICAL STAGES UNDER VARIED N RATES
4.3 RELATIONSHIP of CSR WITH CN LEVEL INDICATORS （CDW, CNC AND CNA）OVER PHENOLOGICAL STAGES UNDER VARIED N RATES
4.4 RELATIONSHIP of HVIs WITH CN LEVEL INDICATORS （CDW, CNC AND CNA）OVER PHENOLOGICAL STAGES UNDER VARIED N RATES
4.5 ESTABLISHMENT AND VALIDATION OF STATIC AND DYNAMIC MODELS FOR CNLEVEL INDICATORS （CDW, CNC AND CNA） OVER PHENOLOGICAL STAGES
4.6 DISCUSSION
4.7 SUMMARY
CHAPTER 5 ESTIMATION OF DYNAMIC PLANT NITROGEN （PN） LEVELINDICATORS OF ORYZA SATIVA L. USING HVIS OVER PHENOLOGICALSTAGES
5.1 VARIATION IN PN LEVEL INDICATORS （PDW, PNC AND PNA） OVERPHENOLOGICAL STAGES UNDER VARIED N RATES
5.2 RELATIONSHIP OF CSR WITH PN LEVEL INDICATORS （PDW, PNC AND PNA） OVERPHENOLOGICAL STAGES UNDER VARIED N RATES
5.3 RELATIONSHIP OF HVIs WITH PN LEVEL INDICATORS （PDW, PNC AND PNA） OVERPHENOLOGICAL STAGES UNDER VARIED N RATES
5.4 ESTABLISHMENT AND VALIDATION OF STATIC AND DYNAMIC MODELS FOR PNLEVEL INDICATORS （PDW, PNC AND PNA） OVER PHENOLOGICAL STAGES
5.5 DISCUSSION
5.6 SUMMARY
CHAPTER 6 GENERAL DISCUSSION AND CONCLUSION
6.1 SENSITIVE CSR REGIONS FOR RICE LAI CHARACTERIZATION
6.2 SENSITIVE CSR REGIONS FOR CN AND PN LEVEL INDICATORS
6.3 POTENTIAL OF HVIS FOR LAI,CN AND PN LEVEL INDICATORS
6.4 DYNAMIC MODELS FOR CN AND PN LEVEL INDICATORS OVER PHENOLOGICALSTAGES
6.5 CONCLUSIONS
6.6 NOVELTY POINTS
6.7 SUGGESTIONS FOR FUTURE RESEARCH
REFERENCES
ACKNOWLEDGEMENTS
PUBLICATIONS
【参考文献】：
期刊论文
[1]基于多种光谱仪的水稻前期植株氮积累量监测[J]. 陈青春,田永超,顾凯健,王薇,姚霞,曹卫星,朱艳. 农业工程学报. 2011(01)
[2]高光谱植被指数与水稻叶面积指数的定量关系[J]. 田永超,杨杰,姚霞,朱艳,曹卫星. 应用生态学报. 2009(07)
[3]Effect of N Fertilizers on Root Growth and Endogenous Hormones in Strawberry[J]. WANG Bo1,2, LAI Tao1, HUANG Qi-Wei1, YANG Xing-Ming1 and SHEN Qi-Rong1,2 1College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095 (China). 2Faculty of Horticulture, Soochow University, Suzhou 215006 (China). Pedosphere. 2009(01)
本文编号：3156687
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