优化氮肥施用和秸秆还田技术为途径的农业管理措施被认为是提升农业可持续性的有效手段,然而当前关于氮肥和秸秆还田对小麦产量和N_(2)O排放影响的研究仍十分有限。为此,本研究基于2000—2022年发表的关于长江中下游流域氮肥和秸秆投入...优化氮肥施用和秸秆还田技术为途径的农业管理措施被认为是提升农业可持续性的有效手段,然而当前关于氮肥和秸秆还田对小麦产量和N_(2)O排放影响的研究仍十分有限。为此,本研究基于2000—2022年发表的关于长江中下游流域氮肥和秸秆投入下小麦产量和N_(2)O排放变化的文献,运用随机森林建模,定量分析氮肥和秸秆还田对小麦产量和N_(2)O排放的影响,并结合情景设置进行了特定地点的小麦产量和N_(2)O排放模拟,同时评估了碳排放强度(CEE)和净生态系统经济效益(NEEB)。结果表明,建立的区域尺度小麦产量与N_(2)O排放对氮秸互作响应的随机森林模型,验证结果R^(2)分别为0.66和0.65,RMSE分别为0.70和1.11。结果表明施氮量和土壤有机质是影响小麦产量和N_(2)O排放的重要因素。综合来看,达到最大产量所需的氮肥量为208~212 kg hm^(-2),达到最小CEE所需的氮肥量为113~130 kg hm^(-2),达到最高的NEEB所需的氮肥量为202~205 kg hm^(-2),其中在6.75 t hm^(-2)的秸秆投入下施用202 kg hm^(-2)的氮肥可以获得最高的生态收益1.37万元。优化氮肥和秸秆投入具备减少作物碳排放强度并获得最大净生态环境效益的潜力。展开更多
明确长江中下游地区不同产量水平稻茬小麦氮素需求特征,可为小麦施肥管理提供理论依据。本研究通过在江苏开展的多年多点不同品种、氮肥水平以及播期播量的小麦试验,构建不同产量水平的实测数据集,分析不同产量水平下单位籽粒需氮量、...明确长江中下游地区不同产量水平稻茬小麦氮素需求特征,可为小麦施肥管理提供理论依据。本研究通过在江苏开展的多年多点不同品种、氮肥水平以及播期播量的小麦试验,构建不同产量水平的实测数据集,分析不同产量水平下单位籽粒需氮量、干物质积累量、植株氮积累量、氮浓度(植株氮浓度、秸秆氮浓度、籽粒氮浓度)、收获指数、氮收获指数和氮营养指数的变化规律。结果表明,不同产量水平下单位籽粒需氮量无显著差异,中低产的单位籽粒需氮量最高,其值为27.8 kg t–1;低产水平最低,其值为24.8 kg t–1。随着产量水平的提高,成熟期干物质积累量、植株氮积累量、植株氮浓度均呈上升趋势,不同产量水平间差异显著。小麦产量与植株氮积累量呈显著正相关,播种期—拔节期、拔节期—开花期和开花期—成熟期的干物质积累量和氮积累量均随着产量的提高而提高,但不同生育阶段的植株干物质积累和氮积累占比呈现不同变化趋势。秸秆和籽粒氮浓度均随产量水平的提高而提高,高产水平下的秸秆氮浓度与中产无显著差异,但显著高于中低产和低产水平;而对于籽粒氮浓度,除中产和中低产水平外均存在显著差异。收获指数随产量水平的提高而逐渐提高,其变化范围为0.39~0.49,其中低产和中低产显著低于中产和高产;而不同产量水平间氮收获指数无显著差异,其变化范围为0.60~0.96。氮营养指数随着产量水平的提高逐渐提高,且在不同产量水平间差异显著,高产水平的氮营养指数较高,部分值大于1,表明有的试验氮肥供应过量。随着产量水平的提高,单位籽粒需氮量呈现先增加后下降趋势,而干物质积累量、植株氮积累量、植株氮浓度、秸秆氮浓度和籽粒氮浓度均逐渐提高,其中秸秆氮浓度增幅高于籽粒氮浓度,田间施肥应注意避免小麦对氮素的奢侈吸收。收获指数和氮收获指数的变化范围与前人研究一致,生长后期较高的干物质积累量和植株氮积累量是小麦获得高产的主要原因,利用氮营养指数可以对小麦田间氮肥管理起到较好的指导作用。展开更多
为阐明播期、播量及施氮量对冬小麦生长与光谱指标的影响规律,本研究通过开展连续两年不同播期、播量及施氮量的冬小麦田间试验,系统地研究了三因素及其互作对冬小麦产量、关键生育时期叶面积指数(leaf area index,LAI)和归一化红边指数...为阐明播期、播量及施氮量对冬小麦生长与光谱指标的影响规律,本研究通过开展连续两年不同播期、播量及施氮量的冬小麦田间试验,系统地研究了三因素及其互作对冬小麦产量、关键生育时期叶面积指数(leaf area index,LAI)和归一化红边指数(normalized difference red edge,NDRE)的影响,并进一步分析了三因素对冬小麦冠层NDRE时序曲线的影响。另一方面建立了不同产量水平下冬小麦冠层NDRE适宜时序曲线,以便于实时监测不同产量水平下冬小麦长势动态。结果表明,冬小麦冠层NDRE与LAI随关键生育期的变化相似,且三因素对关键生育时期2个指标的影响规律基本一致。2018—2019年冬小麦产量、不同生育时期LAI和冠层NDRE均随播期推迟而下降;2019—2020年除灌浆期外,晚播冬小麦产量、LAI及冠层NDRE峰值最大。2年冬小麦不同生育时期LAI、冠层NDRE随施氮量增加而增加;而不同播量间无明显差异。三因素中播期、施氮量对冬小麦冠层NDRE时序曲线有显著影响。冬小麦冠层NDRE时序曲线随施氮量增加被纵向拉长;曲线下降部分随播期推迟向左平移,同时2018—2019年随播期推迟曲线峰值下降,2019—2020年晚播、过晚播冬小麦曲线峰值高于适播期冬小麦。将2个年份数据融合在一起建立了3个产量水平下冬小麦冠层NDRE适宜时序曲线(产量水平分别为:小于6.75、6.75~8.25、大于8.25 t hm^(-2));发现随产量水平升高,NDRE时序曲线峰值及幅宽均增大。综上所述,冬小麦应适期早播,但若冬前积温较高,应适当推迟播期;且可通过增加一定播量和施氮量来改善晚播冬小麦群体长势。研究结果可为不同播期及不同产量水平下冬小麦长势监测提供技术支撑。展开更多
Real-time monitoring of nitrogen status in rice and wheat plant is of significant importance for nitrogen diagnosis, fertilization recommendation, and productivity prediction. With 11 field experiments involving diffe...Real-time monitoring of nitrogen status in rice and wheat plant is of significant importance for nitrogen diagnosis, fertilization recommendation, and productivity prediction. With 11 field experiments involving different cultivars, nitrogen rates, and water regimes, time-course measurements were taken of canopy hyperspeetral reflectance between 350-2 500 nm and leaf nitrogen accumulation (LNA) in rice and wheat. A new spectral analysis method through the consideration of characteristics of canopy components and plant growth status varied with phenological growth stages was designed to explore the common central bands in rice and wheat. Comprehensive analyses were made on the quantitative relationships of LNA to soil adjusted vegetation index (SAVI) and ratio vegetation index (RVI) composed of any two bands between 350-2 500 nm in rice and wheat. The results showed that the ranges of indicative spectral reflectance were largely located in 770-913 and 729-742 nm in both rice and wheat. The optimum spectral vegetation index for estimating LNA was SAVI (R822, R738) during the early-mid period (from jointing to booting), and it was RVI (Rs22, R73s) during the mid-late period (from heading to filling) with the common central bands of 822 and 738 nm in rice and wheat. Comparison of the present spectral vegetation indices with previously reported vegetation indices gave a satisfactory performance in estimating LNA. It is concluded that the spectral bands of 822 and 738 nm can be used as common reflectance indicators for monitoring leaf nitrogen accumulation in rice and wheat.展开更多
A knowledge model with temporal and spatial characteristics for the quantitative design of a cultural pattern in wheat production, using systems analysis and dynamic modeling techniques, was developed for wheat manage...A knowledge model with temporal and spatial characteristics for the quantitative design of a cultural pattern in wheat production, using systems analysis and dynamic modeling techniques, was developed for wheat management, as a decision-making tool in digital farming. The fundamental relationships and algorithms of wheat growth indices and management criteria to cultivars, ecological environments, and production levels were derived from the existing literature and research data to establish a knowledge model system for quantitative wheat management using Visual C++. The system designed a cultural management plan for general management guidelines and crop regulation indices for time-course control criteria during the wheat-growing period. The cultural management plan module included submodels to determine target grain yield and quality, cultivar choice, sowing date, population density, sowing rate, fertilization strategy, and water management, whereas the crop regulation indices module included submodels for suitable development stages, dynamic growth indices, source-sink indices, and nutrient indices. Evaluation of the knowledge model by design studies on the basis of data sets of different eco-sites, cultivars, and soil types indicated a favorable performance of the model system in recommending growth indices and management criteria under diverse conditions. Practical application of the knowledge model system in comparative field experiments produced yield gains of 2.4% to 16.5%. Thus, the presented knowledge model system overcame some of the difficulties of the traditional wheat management patterns and expert systems, and laid a foundation for facilitating the digitization of wheat management.展开更多
Plant nitrogen (N) uptake is a good indicator of crop N status. In this study, a new method was designed to determine the central wavelength, optimal bandwidth and vegetation indices for predicting plant N uptake (...Plant nitrogen (N) uptake is a good indicator of crop N status. In this study, a new method was designed to determine the central wavelength, optimal bandwidth and vegetation indices for predicting plant N uptake (g N m-2) in winter wheat (Triticum aestivum L.). The data were collected from the ground-based hyperspectral reflectance measurements in eight field experiments on winter wheat of different years, eco-sites, varieties, N rates, sowing dates, and densities. The plant N uptake index (PNUI) based on NDVI of 807 nm combined with 736 nm was selected as the optimal vegetation index, and a linear model was developed with R2 of 0.870 and RMSE of 1.546 g N m-2 for calibration, and R2 of 0.834, RMSE of 1.316 g N m-2, slope of 0.934, and intercept of 0.001 for validation. Then, the effect of the bandwidth of central wavelengths on model performance was determined based on the interaction between central wavelength and bandwidth expansion. The results indicated that the optimal bandwidth varies with the changes of the central wavelength and with the interaction between the two bands in one vegetation index. These findings are important for prediction and diagnosis of plant N uptake more precise and accurate in crop management.展开更多
Leaf area index (LAI) is used for crop growth monitoring in agronomic research, and is promising to diagnose the nitrogen (N) status of crops. This study was conducted to develop appropriate LAI-based N diagnostic...Leaf area index (LAI) is used for crop growth monitoring in agronomic research, and is promising to diagnose the nitrogen (N) status of crops. This study was conducted to develop appropriate LAI-based N diagnostic models in irrigated lowland rice. Four field experiments were carried out in Jiangsu Province of East China from 2009 to 2014. Different N application rates and plant densities were used to generate contrasting conditions of N availability or population densities in rice. LAI was determined by LI-3000, and estimated indirectly by LAI-2000 during vegetative growth period. Group and individual plant characters (e.g., tiller number (TN) and plant height (H)) were investigated simultaneously. Two N indicators of plant N accumulation (NA) and N nutrition index (NNI) were measured as well. A calibration equation (LAI=1.7787LAI2o00-0.8816, R2=0.870") was developed for LAI-2000. The linear regression analysis showed a significant relationship between NA and actual LAI (R2=0.863^**). For the NNI, the relative LAI (R2=0.808-) was a relatively unbiased variable in the regression than the LAI (R^2=0.33^**). The results were used to formulate two LAI-based N diagnostic models for irrigated lowland rice (NA=29.778LAI-5.9397; NNI=0.7705RLAI+0.2764). Finally, a simple LAI deterministic model was developed to estimate the actual LAI using the characters of TN and H (LAI=-0.3375(THxHx0.01)2+3.665(TH×H×0.01)-1.8249, R2=0.875**). With these models, the N status of rice can be diagnosed conveniently in the field.展开更多
Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content(LCC).This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge reg...Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content(LCC).This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge region of rapeseed(Brassica napus L.) and wheat(Triticum aestivum L.) crops.The ratio of the red edge area less than 718 nm to the entire red edge area was negatively correlated with LCC.This finding allowed the construction of a new red edge parameter,defined as red edge symmetry(RES).Compared to the commonly used red edge parameters(red edge position,red edge amplitude,and red edge area),RES was a better predictor of LCC.Furthermore,RES was easily calculated using the reflectance of red edge boundary wavebands at 675 and 755 nm(R675 and R755) and reflectance of red edge center wavelength at 718 nm(R718),with the equation RES =(R718-R675)/(R755-R675).In addition,RES was simulated effectively with wide wavebands from the airborne hyperspectral sensor AVIRIS and satellite hyperspectral sensor Hyperion.The close relationships between the simulated RES and LCC indicated a high feasibility of estimating LCC with simulated RES from AVIRIS and Hyperion data.This made RES readily applicable to common airborne and satellite hyperspectral data derived from AVIRIS and Hyperion sources,as well as ground-based spectral reflectance data.展开更多
文摘优化氮肥施用和秸秆还田技术为途径的农业管理措施被认为是提升农业可持续性的有效手段,然而当前关于氮肥和秸秆还田对小麦产量和N_(2)O排放影响的研究仍十分有限。为此,本研究基于2000—2022年发表的关于长江中下游流域氮肥和秸秆投入下小麦产量和N_(2)O排放变化的文献,运用随机森林建模,定量分析氮肥和秸秆还田对小麦产量和N_(2)O排放的影响,并结合情景设置进行了特定地点的小麦产量和N_(2)O排放模拟,同时评估了碳排放强度(CEE)和净生态系统经济效益(NEEB)。结果表明,建立的区域尺度小麦产量与N_(2)O排放对氮秸互作响应的随机森林模型,验证结果R^(2)分别为0.66和0.65,RMSE分别为0.70和1.11。结果表明施氮量和土壤有机质是影响小麦产量和N_(2)O排放的重要因素。综合来看,达到最大产量所需的氮肥量为208~212 kg hm^(-2),达到最小CEE所需的氮肥量为113~130 kg hm^(-2),达到最高的NEEB所需的氮肥量为202~205 kg hm^(-2),其中在6.75 t hm^(-2)的秸秆投入下施用202 kg hm^(-2)的氮肥可以获得最高的生态收益1.37万元。优化氮肥和秸秆投入具备减少作物碳排放强度并获得最大净生态环境效益的潜力。
文摘明确长江中下游地区不同产量水平稻茬小麦氮素需求特征,可为小麦施肥管理提供理论依据。本研究通过在江苏开展的多年多点不同品种、氮肥水平以及播期播量的小麦试验,构建不同产量水平的实测数据集,分析不同产量水平下单位籽粒需氮量、干物质积累量、植株氮积累量、氮浓度(植株氮浓度、秸秆氮浓度、籽粒氮浓度)、收获指数、氮收获指数和氮营养指数的变化规律。结果表明,不同产量水平下单位籽粒需氮量无显著差异,中低产的单位籽粒需氮量最高,其值为27.8 kg t–1;低产水平最低,其值为24.8 kg t–1。随着产量水平的提高,成熟期干物质积累量、植株氮积累量、植株氮浓度均呈上升趋势,不同产量水平间差异显著。小麦产量与植株氮积累量呈显著正相关,播种期—拔节期、拔节期—开花期和开花期—成熟期的干物质积累量和氮积累量均随着产量的提高而提高,但不同生育阶段的植株干物质积累和氮积累占比呈现不同变化趋势。秸秆和籽粒氮浓度均随产量水平的提高而提高,高产水平下的秸秆氮浓度与中产无显著差异,但显著高于中低产和低产水平;而对于籽粒氮浓度,除中产和中低产水平外均存在显著差异。收获指数随产量水平的提高而逐渐提高,其变化范围为0.39~0.49,其中低产和中低产显著低于中产和高产;而不同产量水平间氮收获指数无显著差异,其变化范围为0.60~0.96。氮营养指数随着产量水平的提高逐渐提高,且在不同产量水平间差异显著,高产水平的氮营养指数较高,部分值大于1,表明有的试验氮肥供应过量。随着产量水平的提高,单位籽粒需氮量呈现先增加后下降趋势,而干物质积累量、植株氮积累量、植株氮浓度、秸秆氮浓度和籽粒氮浓度均逐渐提高,其中秸秆氮浓度增幅高于籽粒氮浓度,田间施肥应注意避免小麦对氮素的奢侈吸收。收获指数和氮收获指数的变化范围与前人研究一致,生长后期较高的干物质积累量和植株氮积累量是小麦获得高产的主要原因,利用氮营养指数可以对小麦田间氮肥管理起到较好的指导作用。
文摘为阐明播期、播量及施氮量对冬小麦生长与光谱指标的影响规律,本研究通过开展连续两年不同播期、播量及施氮量的冬小麦田间试验,系统地研究了三因素及其互作对冬小麦产量、关键生育时期叶面积指数(leaf area index,LAI)和归一化红边指数(normalized difference red edge,NDRE)的影响,并进一步分析了三因素对冬小麦冠层NDRE时序曲线的影响。另一方面建立了不同产量水平下冬小麦冠层NDRE适宜时序曲线,以便于实时监测不同产量水平下冬小麦长势动态。结果表明,冬小麦冠层NDRE与LAI随关键生育期的变化相似,且三因素对关键生育时期2个指标的影响规律基本一致。2018—2019年冬小麦产量、不同生育时期LAI和冠层NDRE均随播期推迟而下降;2019—2020年除灌浆期外,晚播冬小麦产量、LAI及冠层NDRE峰值最大。2年冬小麦不同生育时期LAI、冠层NDRE随施氮量增加而增加;而不同播量间无明显差异。三因素中播期、施氮量对冬小麦冠层NDRE时序曲线有显著影响。冬小麦冠层NDRE时序曲线随施氮量增加被纵向拉长;曲线下降部分随播期推迟向左平移,同时2018—2019年随播期推迟曲线峰值下降,2019—2020年晚播、过晚播冬小麦曲线峰值高于适播期冬小麦。将2个年份数据融合在一起建立了3个产量水平下冬小麦冠层NDRE适宜时序曲线(产量水平分别为:小于6.75、6.75~8.25、大于8.25 t hm^(-2));发现随产量水平升高,NDRE时序曲线峰值及幅宽均增大。综上所述,冬小麦应适期早播,但若冬前积温较高,应适当推迟播期;且可通过增加一定播量和施氮量来改善晚播冬小麦群体长势。研究结果可为不同播期及不同产量水平下冬小麦长势监测提供技术支撑。
基金supported by the National High-Tech R&D Program of China(2011AA100703)the National Natural Science Foundation of China(30900868)+2 种基金the Natural Science Foundation of Jiangsu Province, China(BK2010453)the Academic Program Development of Jiangsu Higher Education Institutions, China(PAPD)the Science and Technology Support Plan of Jiangsu Province, China(BE2011351)
文摘Real-time monitoring of nitrogen status in rice and wheat plant is of significant importance for nitrogen diagnosis, fertilization recommendation, and productivity prediction. With 11 field experiments involving different cultivars, nitrogen rates, and water regimes, time-course measurements were taken of canopy hyperspeetral reflectance between 350-2 500 nm and leaf nitrogen accumulation (LNA) in rice and wheat. A new spectral analysis method through the consideration of characteristics of canopy components and plant growth status varied with phenological growth stages was designed to explore the common central bands in rice and wheat. Comprehensive analyses were made on the quantitative relationships of LNA to soil adjusted vegetation index (SAVI) and ratio vegetation index (RVI) composed of any two bands between 350-2 500 nm in rice and wheat. The results showed that the ranges of indicative spectral reflectance were largely located in 770-913 and 729-742 nm in both rice and wheat. The optimum spectral vegetation index for estimating LNA was SAVI (R822, R738) during the early-mid period (from jointing to booting), and it was RVI (Rs22, R73s) during the mid-late period (from heading to filling) with the common central bands of 822 and 738 nm in rice and wheat. Comparison of the present spectral vegetation indices with previously reported vegetation indices gave a satisfactory performance in estimating LNA. It is concluded that the spectral bands of 822 and 738 nm can be used as common reflectance indicators for monitoring leaf nitrogen accumulation in rice and wheat.
基金Project supported by the National High-Technology Research and Development Program of China (863 Program) (No. 2003AA209030)the National Natural Science Foundation of China (No. 30030090)and the Hi-Tech Research and Development Program of Jiangsu Province (No. BG2004320).
文摘A knowledge model with temporal and spatial characteristics for the quantitative design of a cultural pattern in wheat production, using systems analysis and dynamic modeling techniques, was developed for wheat management, as a decision-making tool in digital farming. The fundamental relationships and algorithms of wheat growth indices and management criteria to cultivars, ecological environments, and production levels were derived from the existing literature and research data to establish a knowledge model system for quantitative wheat management using Visual C++. The system designed a cultural management plan for general management guidelines and crop regulation indices for time-course control criteria during the wheat-growing period. The cultural management plan module included submodels to determine target grain yield and quality, cultivar choice, sowing date, population density, sowing rate, fertilization strategy, and water management, whereas the crop regulation indices module included submodels for suitable development stages, dynamic growth indices, source-sink indices, and nutrient indices. Evaluation of the knowledge model by design studies on the basis of data sets of different eco-sites, cultivars, and soil types indicated a favorable performance of the model system in recommending growth indices and management criteria under diverse conditions. Practical application of the knowledge model system in comparative field experiments produced yield gains of 2.4% to 16.5%. Thus, the presented knowledge model system overcame some of the difficulties of the traditional wheat management patterns and expert systems, and laid a foundation for facilitating the digitization of wheat management.
基金supported by the National High-Tech R&DProgram of China (2011AA100703)the Natural Science Foundation of Jiangsu Province,China (BK2010453)+1 种基金the Science Technology Support Plan of Jiangsu Province,China (BE2011351)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),China
文摘Plant nitrogen (N) uptake is a good indicator of crop N status. In this study, a new method was designed to determine the central wavelength, optimal bandwidth and vegetation indices for predicting plant N uptake (g N m-2) in winter wheat (Triticum aestivum L.). The data were collected from the ground-based hyperspectral reflectance measurements in eight field experiments on winter wheat of different years, eco-sites, varieties, N rates, sowing dates, and densities. The plant N uptake index (PNUI) based on NDVI of 807 nm combined with 736 nm was selected as the optimal vegetation index, and a linear model was developed with R2 of 0.870 and RMSE of 1.546 g N m-2 for calibration, and R2 of 0.834, RMSE of 1.316 g N m-2, slope of 0.934, and intercept of 0.001 for validation. Then, the effect of the bandwidth of central wavelengths on model performance was determined based on the interaction between central wavelength and bandwidth expansion. The results indicated that the optimal bandwidth varies with the changes of the central wavelength and with the interaction between the two bands in one vegetation index. These findings are important for prediction and diagnosis of plant N uptake more precise and accurate in crop management.
基金supported by the Special Program for Agriculture Science and Technology from the Ministry of Agriculture of China (201303109)the National Key Research & Development Program of China (2016YFD0300604+3 种基金 2016YFD0200602)the Fundamental Research Funds for the Central Universities,China (262201602)the Priority Academic Program Development of Jiangsu Higher Education Institutions of China (PAPD)the 111 Project of China (B16026)
文摘Leaf area index (LAI) is used for crop growth monitoring in agronomic research, and is promising to diagnose the nitrogen (N) status of crops. This study was conducted to develop appropriate LAI-based N diagnostic models in irrigated lowland rice. Four field experiments were carried out in Jiangsu Province of East China from 2009 to 2014. Different N application rates and plant densities were used to generate contrasting conditions of N availability or population densities in rice. LAI was determined by LI-3000, and estimated indirectly by LAI-2000 during vegetative growth period. Group and individual plant characters (e.g., tiller number (TN) and plant height (H)) were investigated simultaneously. Two N indicators of plant N accumulation (NA) and N nutrition index (NNI) were measured as well. A calibration equation (LAI=1.7787LAI2o00-0.8816, R2=0.870") was developed for LAI-2000. The linear regression analysis showed a significant relationship between NA and actual LAI (R2=0.863^**). For the NNI, the relative LAI (R2=0.808-) was a relatively unbiased variable in the regression than the LAI (R^2=0.33^**). The results were used to formulate two LAI-based N diagnostic models for irrigated lowland rice (NA=29.778LAI-5.9397; NNI=0.7705RLAI+0.2764). Finally, a simple LAI deterministic model was developed to estimate the actual LAI using the characters of TN and H (LAI=-0.3375(THxHx0.01)2+3.665(TH×H×0.01)-1.8249, R2=0.875**). With these models, the N status of rice can be diagnosed conveniently in the field.
基金Supported by the Program for New Century Excellent Talents in University of China(No.NCET-08-0797)the National Natural Science Foundation of China(No.30871448)+1 种基金the Natural Science Foundation of Jiangsu Province,China(No.BK2008330)the Program for the Creative Scholars of Jiangsu Province,China(No.BK20081479)
文摘Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content(LCC).This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge region of rapeseed(Brassica napus L.) and wheat(Triticum aestivum L.) crops.The ratio of the red edge area less than 718 nm to the entire red edge area was negatively correlated with LCC.This finding allowed the construction of a new red edge parameter,defined as red edge symmetry(RES).Compared to the commonly used red edge parameters(red edge position,red edge amplitude,and red edge area),RES was a better predictor of LCC.Furthermore,RES was easily calculated using the reflectance of red edge boundary wavebands at 675 and 755 nm(R675 and R755) and reflectance of red edge center wavelength at 718 nm(R718),with the equation RES =(R718-R675)/(R755-R675).In addition,RES was simulated effectively with wide wavebands from the airborne hyperspectral sensor AVIRIS and satellite hyperspectral sensor Hyperion.The close relationships between the simulated RES and LCC indicated a high feasibility of estimating LCC with simulated RES from AVIRIS and Hyperion data.This made RES readily applicable to common airborne and satellite hyperspectral data derived from AVIRIS and Hyperion sources,as well as ground-based spectral reflectance data.