Study on Dynamic Estimation of Canopy and Plant Level Nitrog
發(fā)布時(shí)間：2021-04-24 04:34
隨著中國(guó)城市化進(jìn)程提速、耕地退化加劇、傳統(tǒng)種植模式變革等,長(zhǎng)江流域水稻種植面積逐年遞減,科學(xué)地進(jìn)行水稻種植,既保護(hù)生態(tài)環(huán)境,又維護(hù)糧食安全成為熱點(diǎn)問(wèn)題。傳統(tǒng)農(nóng)田管理措施中,為提高產(chǎn)量而大量施用氮肥,在農(nóng)田生態(tài)環(huán)境方面已經(jīng)引起了諸多問(wèn)題。氮素是作物生長(zhǎng)和發(fā)育的基本元素,在整個(gè)生長(zhǎng)季節(jié)氮素的有效供給情況都影響著最終產(chǎn)量。雖然缺氮會(huì)導(dǎo)致作物產(chǎn)量和經(jīng)濟(jì)效益的大幅度下降,但是過(guò)度施氮也會(huì)降低氮利用率,并造成嚴(yán)重的環(huán)境威脅。中國(guó)在水稻種植中氮施用量比世界平均水平高70%左右。為了合理進(jìn)行氮肥施用,提高氮利用率,精準(zhǔn)氮肥管理已成為現(xiàn)代農(nóng)業(yè)特別是華中地區(qū)水稻生產(chǎn)的熱點(diǎn)問(wèn)題之一。在減少氮流失環(huán)境脅迫的前提下確保最大產(chǎn)量和品質(zhì),需要在水稻關(guān)鍵生長(zhǎng)期制定有效的養(yǎng)分管理措施。因此,準(zhǔn)確快速獲取作物地上部分生物量和氮含量信息,將為水稻生長(zhǎng)監(jiān)測(cè)和養(yǎng)分管理提供重要決策依據(jù)。傳統(tǒng)的調(diào)查取樣和分析方法具有破壞性,而且費(fèi)時(shí)費(fèi)力;無(wú)損高光譜遙感及分析技術(shù)有助于提高氮的精確管理。本文主要利用高光譜數(shù)據(jù)評(píng)估水稻不同物候期的氮營(yíng)養(yǎng)狀況,建立模型無(wú)損估計(jì)冠層和植株水平的重要生理生化指標(biāo),包括葉面積指數(shù)（LAI）,冠層干物質(zhì)重量（C...
【文章來(lái)源】：華中農(nóng)業(yè)大學(xué)湖北省 211工程院校 教育部直屬院校
【文章頁(yè)數(shù)】：141 頁(yè)
【學(xué)位級(jí)別】：博士
【文章目錄】：
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
【參考文獻(xiàn)】：
期刊論文
[1]基于多種光譜儀的水稻前期植株氮積累量監(jiān)測(cè)[J]. 陳青春,田永超,顧凱健,王薇,姚霞,曹衛(wèi)星,朱艷. 農(nóng)業(yè)工程學(xué)報(bào). 2011(01)
[2]高光譜植被指數(shù)與水稻葉面積指數(shù)的定量關(guān)系[J]. 田永超,楊杰,姚霞,朱艷,曹衛(wèi)星. 應(yīng)用生態(tài)學(xué)報(bào). 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)
本文編號(hào)：3156687
【文章來(lái)源】：華中農(nóng)業(yè)大學(xué)湖北省 211工程院校 教育部直屬院校
【文章頁(yè)數(shù)】：141 頁(yè)
【學(xué)位級(jí)別】：博士
【文章目錄】：
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
【參考文獻(xiàn)】：
期刊論文
[1]基于多種光譜儀的水稻前期植株氮積累量監(jiān)測(cè)[J]. 陳青春,田永超,顧凱健,王薇,姚霞,曹衛(wèi)星,朱艷. 農(nóng)業(yè)工程學(xué)報(bào). 2011(01)
[2]高光譜植被指數(shù)與水稻葉面積指數(shù)的定量關(guān)系[J]. 田永超,楊杰,姚霞,朱艷,曹衛(wèi)星. 應(yīng)用生態(tài)學(xué)報(bào). 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)
本文編號(hào)：3156687
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