小麦、玉米叶片和植株氮营养高光谱诊断与应用研究
发布时间：2018-08-03 18:48
【摘要】：氮素是作物生长发育所必须的营养元素,是保证作物长势及产量的基本元素。但是氮肥的大量使用给环境带来沉重负担。因此,准确探测作物氮素营养状况,在作物关键生育期给作物补充适量的养分以保证作物的生长是必要的。研究表明氮营养指数(nitrogen nutrition index, NNI)可用于准确探测作物氮素营养状况。NNI的计算需要氮浓度和生物量这两个生化参数,常规测定方法费时费力,难以指导精准农业生产。因此迫切需要用遥感技术来准确估算氮浓度和生物量,即实现NNI的实时遥感估算。本论文致力于围绕叶片和植株的高光谱遥感数据准确估算氮浓度和生物量,建立关键氮浓度曲线以实现NNI的遥感估测,为及时监测作物氮素营养状况、指导变量施肥和产量预报提供有效手段。为此,本文基于北京小汤山国家精准农业示范基地和北京农林科学院小麦试验基地田间试验数据,结合2014/2015年挑旗期和开花期2景无人机高光谱影像数据,以建立叶片及植株的高光谱氮营养指数诊断模型为主要研究目标,把氮营养指数诊断模型应用到无人机高光谱影像上,为大范围氮素营养实时监测和精确诊断、变量施肥和产量预报提供了技术支撑。论文主要工作如下：(1)从原始光谱反射特征、红边参数、连续统去除光谱特征、光谱指数及辐射传输模型等角度详细介绍了作物氮素营养诊断研究进展、氮浓度稀释模型及氮营养指数诊断作物氮素状况的研究进展及不足；并提出了本文的研究思路。(2)基于北京小汤山国家精准农业示范基地和北京农林科学院试验基地开展的多年小麦、玉米试验,详细介绍相关试验设计方案、田间试验数据测定方法、无人机数码影像和高光谱影像获取及处理方法。(3)通过回归建模分析了小麦、玉米叶片原始光谱反射特征、红边参数、连续统光谱吸收特征、EFAST(extended fourier amplitude sensitivity, EFAST)方法和PROSPECT模型整合构建的对氮素敏感的归一化光谱指数(normalized difference spectral index, NDSI)和比值植被指数(ratio spectral index, RSI)光谱指数、氮素常用植被指数与作物叶片氮素营养状况的关系,比较了不同植被指数估算作物叶片氮素营养状况的精度,确定了对小麦、玉米氮素营养状况敏感的光谱指数的顺序。①利用PROSPECT模型随机模拟叶片光谱反射率数据,采用EFAST方法对PROSPECT模型中各个生理生化参数在400-2500nm波段范围的叶片反射光谱进行敏感性分析。结果表明对叶绿素敏感的波段范围是417-728nm,参照归一化植被指数和比值植被指数,本研究构建了对小麦氮素敏感的归一化光谱指数NDS 1(564,728)、 NDSI(543,728)、RSI(564,728)和RSI(543,728)、对玉米氮素敏感的光谱指数是NDSI(629,649)、NDSI(495,669、RSI(629,649)和RSI(495,669)分析了光谱指数NDSI和RSI与作物叶片氮含量及叶片氮累积量的相关性,研究表明除了玉米灌浆期外,其他生育期的光谱指数与叶片氮含量的相关性高于与叶片氮累积量的相关性,叶片氮含量比叶片氮累积量对叶片光谱参数更为敏感。②建立了光谱指数与叶片氮含量、叶片氮累积量的回归模型,用决定系数(coefficient of determination,R2)、均方根误差(root mean square error, RMSE)相对误差(relative error, RE)作为评价确定最佳叶片氮素营养状况光谱指数的指标。结果表明对小麦叶片氮含量较为敏感的前5个光谱指数是mmND705、ND705、SR705、GMI-2、RI-half;对小麦叶片氮累积量较为敏感的前5个光谱指数是R550/R800、GMI-1、RSI(564,728)、RSI(543,728)、RI-2dB;对玉米叶片氮含量较为敏感的前5个光谱指数是VOGb、VOGc、NDRE、VOGa、CIred edge;对玉米叶片氮累积量较为敏感的前5个光谱指数是NDRE、MTCI、RI-2dB、VOGa、VOGb。(4)通过回归建模分析了植株在不同年份、不同生育期原始光谱反射特征、红边参数、连续统光谱吸收特征、EFAST方法和PROSPAIL模型整合构建的对氮素敏感的光谱指数NDSI和RSI、氮素常用光谱指数与氮素营养状况的关系,研究确定了对小麦植株敏感的光谱指数的顺序。①利用PROSAIL模型随机模拟植株光谱反射率数据,采用EFAST方法对PROSAIL模型各个参数在400-2500nm波段范围的植株反射光谱进行敏感性分析。结果表明对叶绿素敏感的波段范围是515-745nm,参照归一化植被指数和比值植被指数,构建了对小麦植株氮含量和植株氮累积量敏感的光谱指数是NDSI(546,698)、 NDSI(667,685)、NDSI(539,745)、RSI(546,694)、RSI(667,684)、RSI(539,745)、分析了光谱指数NDSI和RSI与植株氮含量及植株氮累积量的相关性。②基于经验统计关系建立了光谱指数与植株氮含量、植株氮累积量的回归模型,用尺2、RMSE、RE作为评价植株氮素营养状况的指标。结果表明对小麦植株氮含量敏感的前5个光谱指数是SRPI、NPCI、ND705、MCARI/MTVI2和MTCI,对小麦植株氮累积量敏感的前5个光谱指数是SR705、RI-half、NPCI、VOGb和mSR705。(5)通过回归建模分析了叶片及植株生物量原始光谱反射特征、红边参数、连续统光谱吸收特征、EFAST方法和PROSPECT (PROSAIL)模型整合构建的对生物量敏感的光谱指数、与生物量相关的常用光谱指数与生物量的关系,研究确定了对叶片及植株生物量敏感的光谱指数的顺序。①利用PROSPECT (PROSAIL)模型随机模拟叶片及植株光谱反射率数据,采用EFAST方法对PROSPECT (PROSAIL)模型各个参数在400-2500nm波段范围的植株反射光谱进行敏感性分析。结果表明对叶片及植株生物量敏感的波段范围为749-2410nm,构建了对小麦叶片及植株生物量敏感的光谱指数是NDSI(2126,2347、NDSI(1652,1686)、RSI(2126,2347)和RSI(1652,1686),分析了光谱指数NDSI和RSI与叶片及植株生物量的相关性。②基于经验统计关系建立了光谱指数与叶片及植株生物量的回归模型,用R2、RMSE、RE作为评价叶片及植株生物量的指标。结果表明对小麦叶片生物量敏感的前5个光谱指数是mSR705、RI-1dB、VOGa、GNDVI和NDCI,对小麦植株生物量敏感的前5个光谱指数是VOGa、mSR705、REP、NDVI705和mNDVI705。(6)建立了研究区小麦叶片及植株关键氮稀释曲线模型；建立了“遥感信息—农学参数—氮营养指数”叶片及植株氮营养诊断模型。①建立的研究区小麦叶片关键氮浓度曲线模型为Ncl=4.42×W-0.18L,建立的研究区小麦植株关键氮浓度曲线模型为N印=5.81×W-0.54。②基于叶片“遥感信息—农学参数—氮营养指数”估测的NNI与实际NNI之间的R2为0.77,基于植株“遥感信息—农学参数—氮营养指数”估测的NNI与实测NNI之间的R2为0.83,研究表明建立的植株NNI诊断模型精度高于叶片NNI诊断模型精度。(7)采用小汤山国家精准农业示范基地无人机高光谱影像提取作物NNI,挑旗期提取的NNI与实际NNI间的R2为0.66；开花期提取的NNI与实际NNI间的R2为0.69,均达到了显著相关。结果表明,基于“遥感信息—农学参数—氮营养指数”方法用于NNI估算是可行的,能够得到与实际吻合的结果,为快速、准确实时监测作物氮素状况、变量施肥和产量预报提供了科学依据。
[Abstract]:Nitrogen is a necessary nutrient element for crop growth and development. It is the basic element to ensure crop growth and yield. However, the heavy use of nitrogen fertilizer brings a heavy burden to the environment. Therefore, it is necessary to accurately detect the nitrogen nutrition status of crops and to supplement the appropriate amount of nutrients to crops to ensure the growth of crops in the critical growth period of crops. The nitrogen nutrition index (NNI) can be used to accurately detect the nitrogen nutrition status of the crop,.NNI, which requires two biochemical parameters of nitrogen and biomass. The conventional method is time-consuming and difficult to guide the production of precision agriculture. Therefore, it is urgent to use remote sensing technology to accurately estimate nitrogen concentration and biomass, that is, to achieve NN. I's real-time remote sensing estimation. This paper is devoted to accurately estimating nitrogen concentration and biomass around the hyperspectral remote sensing data of leaves and plants, establishing a key nitrogen concentration curve to achieve remote sensing estimation of NNI, providing effective means for timely monitoring of nitrogen nutrition status of crops, guiding variable fertilization and yield forecasting. This paper is based on Beijing soup. The field test data of Mountain National precision agriculture demonstration base and Beijing Academy of agricultural and Forestry Sciences, combined with the high spectral image data of 2 unmanned aerial vehicles (UAV) in the flag period and the flowering period of 2014/2015, the high light spectrum nitrogen nutrition index diagnosis model of leaves and plants was established as the main target, and the nitrogen nutrition index diagnosis model was applied to no one. The main work of this paper is as follows: (1) the nitrogen nutrition of crop is introduced in detail from the original spectral reflectance characteristics, red edge parameters, continuous spectral characteristics, spectral index and radiative transfer model. The research progress, the nitrogen concentration dilution model and the nitrogen nutrition index in the diagnosis of nitrogen status of the crop, and put forward the research ideas in this paper. (2) many years of wheat and corn test based on the Xiaotangshan national precision agriculture demonstration base and the experimental base of Beijing Academy of agricultural and Forestry Sciences in Beijing, the related experiment design was introduced in detail. Method, field test data determination method, UAV digital image and hyperspectral image acquisition and processing methods. (3) the original spectral reflectance characteristics of wheat and corn leaves, red edge parameters, continuous spectral absorption characteristics, EFAST (Extended Fourier amplitude sensitivity, EFAST) method and PROSPECT model are analyzed by regression modeling. The relationship between nitrogen sensitive spectral index (normalized difference spectral index, NDSI) and ratio vegetation index (ratio spectral index, RSI), nitrogen common vegetation index and nitrogen nutrition status of crop leaves was built, and the precision of nitrogen nutrition status of crop leaves was compared with different vegetation indices. The order of spectral indices sensitive to nitrogen nutrition status in wheat and maize. (1) the PROSPECT model was used to simulate the spectral reflectance of leaves, and the sensitivity analysis of the leaf reflectance spectra of the physiological and biochemical parameters in the 400-2500nm band was analyzed by the EFAST method. According to the normalized vegetation index and the ratio vegetation index, this study constructed the normalized spectral index NDS 1 (564728), NDSI (543728), RSI (564728) and RSI (543728) for wheat nitrogen sensitivity. The spectral index of nitrogen sensitivity to maize was NDSI (629649), NDSI (495669, RSI (629649) and RSI (495669) analyzed the spectral finger. The correlation of NDSI and RSI with nitrogen content of crop leaves and nitrogen accumulation of leaves showed that the correlation between the spectral index of other growth periods and leaf nitrogen content was higher than that of leaf nitrogen accumulation except for the grain filling period. The nitrogen content of leaves was more sensitive than leaf nitrogen accumulation to leaf spectral parameters. The number and leaf nitrogen content, the regression model of the nitrogen accumulation of leaves, the coefficient of determination, R2, the relative error (relative error, RMSE) relative error (relative error, RMSE) as the index of determining the optimum leaf nitrogen nutrient status spectral index. The results showed that the nitrogen content of Wheat leaves was more sensitive. The first 5 spectral indices of the sense are mmND705, ND705, SR705, GMI-2, RI-half; the first 5 spectral indices that are more sensitive to nitrogen accumulation in wheat leaves are R550/R800, GMI-1, RSI (564728), RSI (543728), RI-2dB, and the first 5 spectral indices that are more sensitive to the nitrogen content of maize leaves are VOGb. The first 5 spectral indices are NDRE, MTCI, RI-2dB, VOGa, VOGb. (4), through regression modeling, the original spectral reflectance characteristics of plants in different years, different growth stages, red edge parameters, continuous spectral absorption characteristics, and EFAST method and PROSPAIL model are integrated to construct nitrogen sensitive spectral index NDSI and RSI, nitrogen is commonly used for spectral index. The relationship between number and nitrogen nutrition status, the order of spectral index sensitive to wheat plants was determined. (1) PROSAIL model was used to simulate plant spectral reflectance data, and EFAST method was used to analyze the sensitivity analysis of plant reflectance spectra of each parameter of PROSAIL model in the range of 400-2500nm band. The results showed that the chlorophyll sensitivity was sensitive to chlorophyll sensitivity. The band range of sense is 515-745nm. According to the normalized vegetation index and ratio vegetation index, the spectral index of nitrogen content and plant nitrogen accumulation sensitivity to wheat plants is NDSI (546698), NDSI (667685), NDSI (539745), RSI (546694), RSI (667684), RSI (539745)). The spectral index NDSI and RSI and plant nitrogen content and plant are analyzed. The correlation of nitrogen accumulation, based on the empirical statistical relationship, established the spectral index and plant nitrogen content, the regression model of plant nitrogen accumulation, using scale 2, RMSE, RE as indicators to evaluate plant nitrogen nutrition status. The results showed that the first 5 spectral indices sensitive to nitrogen content in wheat plants were SRPI, NPCI, ND705, MCARI/MTVI2 and MTCI, to wheat. The first 5 spectral indices of plant nitrogen accumulation sensitivity are SR705, RI-half, NPCI, VOGb and mSR705. (5). By regression modeling, the original spectral reflectance characteristics of leaf and plant biomass, red edge parameters, continuum spectral absorption characteristics, EFAST method and PROSPECT (PROSAIL) model integrated and constructed by the EFAST method and PROSPECT (PROSAIL) model, are used to analyze the spectral index of biomass sensitive. The sequence of spectral indices sensitive to the biomass of leaves and plants was determined by the correlation of the related spectral index and biomass. (1) the PROSPECT (PROSAIL) model was used to simulate the spectral reflectance of leaves and plants, and the EFAST method was used to determine the plant parameters of the PROSPECT (PROSAIL) model in the range of 400-2500nm band. The spectral sensitivity analysis showed that the range of sensitivity to leaf and plant biomass was 749-2410nm. The spectral index of wheat leaf and plant biomass sensitive was NDSI (21262347, NDSI (16521686), RSI (21262347) and RSI (16521686). The spectral index NDSI and RSI and leaf and plant biomass were analyzed. " The regression model of spectral index and leaf and plant biomass was established based on the empirical statistical relationship. R2, RMSE and RE were used as indicators to evaluate the biomass of leaves and plants. The results showed that the first 5 spectral indices sensitive to wheat leaf biomass were mSR705, RI-1dB, VOGa, GNDVI and NDCI, and the first 5 sensitive to wheat plant biomass. The spectral index is VOGa, mSR705, REP, NDVI705 and mNDVI705. (6) to establish the key nitrogen dilution curve model of wheat leaves and plants in the study area. A model of "remote sensing information - Agronomy parameter nitrogen nutrition index" leaf and plant nitrogen nutrition diagnosis model was established. (1) the key nitrogen concentration curve model of wheat leaves was established in the study area Ncl=4.42 x W-0.18L The key nitrogen concentration curve model of the wheat plant in the study area is N =5.81 x W-0.54. (2) based on the =5.81 of the leaf "remote sensing information - Agronomy parameter - nitrogen nutrition index" and the R2 between the actual NNI and the actual NNI. The R2 between the NNI and the measured NNI based on the plant "remote sensing information - agronomic parameter nitrogen nutrition index" is 0.83. The results showed that the accuracy of the NNI diagnostic model was higher than that of the blade NNI diagnosis model. (7) the crop NNI was extracted from the UAV hyperspectral image of the national precision agricultural demonstration base in Xiaotangshan, the R2 between the NNI and the actual NNI was 0.66, and the R2 between the NNI and the actual NNI during the flowering period was 0.69, and the results reached significant correlation. The results show that it is feasible to estimate NNI based on the "remote sensing information - agronomic parameter nitrogen nutrition index" method. It can be consistent with the actual results. It provides a scientific basis for fast, accurate and real-time monitoring of crop nitrogen status, variable fertilization and yield prediction.
【学位授予单位】：中国矿业大学(北京)
【学位级别】：博士
【学位授予年份】：2016
【分类号】：S512.1;S513
，
本文编号：2162676
[Abstract]:Nitrogen is a necessary nutrient element for crop growth and development. It is the basic element to ensure crop growth and yield. However, the heavy use of nitrogen fertilizer brings a heavy burden to the environment. Therefore, it is necessary to accurately detect the nitrogen nutrition status of crops and to supplement the appropriate amount of nutrients to crops to ensure the growth of crops in the critical growth period of crops. The nitrogen nutrition index (NNI) can be used to accurately detect the nitrogen nutrition status of the crop,.NNI, which requires two biochemical parameters of nitrogen and biomass. The conventional method is time-consuming and difficult to guide the production of precision agriculture. Therefore, it is urgent to use remote sensing technology to accurately estimate nitrogen concentration and biomass, that is, to achieve NN. I's real-time remote sensing estimation. This paper is devoted to accurately estimating nitrogen concentration and biomass around the hyperspectral remote sensing data of leaves and plants, establishing a key nitrogen concentration curve to achieve remote sensing estimation of NNI, providing effective means for timely monitoring of nitrogen nutrition status of crops, guiding variable fertilization and yield forecasting. This paper is based on Beijing soup. The field test data of Mountain National precision agriculture demonstration base and Beijing Academy of agricultural and Forestry Sciences, combined with the high spectral image data of 2 unmanned aerial vehicles (UAV) in the flag period and the flowering period of 2014/2015, the high light spectrum nitrogen nutrition index diagnosis model of leaves and plants was established as the main target, and the nitrogen nutrition index diagnosis model was applied to no one. The main work of this paper is as follows: (1) the nitrogen nutrition of crop is introduced in detail from the original spectral reflectance characteristics, red edge parameters, continuous spectral characteristics, spectral index and radiative transfer model. The research progress, the nitrogen concentration dilution model and the nitrogen nutrition index in the diagnosis of nitrogen status of the crop, and put forward the research ideas in this paper. (2) many years of wheat and corn test based on the Xiaotangshan national precision agriculture demonstration base and the experimental base of Beijing Academy of agricultural and Forestry Sciences in Beijing, the related experiment design was introduced in detail. Method, field test data determination method, UAV digital image and hyperspectral image acquisition and processing methods. (3) the original spectral reflectance characteristics of wheat and corn leaves, red edge parameters, continuous spectral absorption characteristics, EFAST (Extended Fourier amplitude sensitivity, EFAST) method and PROSPECT model are analyzed by regression modeling. The relationship between nitrogen sensitive spectral index (normalized difference spectral index, NDSI) and ratio vegetation index (ratio spectral index, RSI), nitrogen common vegetation index and nitrogen nutrition status of crop leaves was built, and the precision of nitrogen nutrition status of crop leaves was compared with different vegetation indices. The order of spectral indices sensitive to nitrogen nutrition status in wheat and maize. (1) the PROSPECT model was used to simulate the spectral reflectance of leaves, and the sensitivity analysis of the leaf reflectance spectra of the physiological and biochemical parameters in the 400-2500nm band was analyzed by the EFAST method. According to the normalized vegetation index and the ratio vegetation index, this study constructed the normalized spectral index NDS 1 (564728), NDSI (543728), RSI (564728) and RSI (543728) for wheat nitrogen sensitivity. The spectral index of nitrogen sensitivity to maize was NDSI (629649), NDSI (495669, RSI (629649) and RSI (495669) analyzed the spectral finger. The correlation of NDSI and RSI with nitrogen content of crop leaves and nitrogen accumulation of leaves showed that the correlation between the spectral index of other growth periods and leaf nitrogen content was higher than that of leaf nitrogen accumulation except for the grain filling period. The nitrogen content of leaves was more sensitive than leaf nitrogen accumulation to leaf spectral parameters. The number and leaf nitrogen content, the regression model of the nitrogen accumulation of leaves, the coefficient of determination, R2, the relative error (relative error, RMSE) relative error (relative error, RMSE) as the index of determining the optimum leaf nitrogen nutrient status spectral index. The results showed that the nitrogen content of Wheat leaves was more sensitive. The first 5 spectral indices of the sense are mmND705, ND705, SR705, GMI-2, RI-half; the first 5 spectral indices that are more sensitive to nitrogen accumulation in wheat leaves are R550/R800, GMI-1, RSI (564728), RSI (543728), RI-2dB, and the first 5 spectral indices that are more sensitive to the nitrogen content of maize leaves are VOGb. The first 5 spectral indices are NDRE, MTCI, RI-2dB, VOGa, VOGb. (4), through regression modeling, the original spectral reflectance characteristics of plants in different years, different growth stages, red edge parameters, continuous spectral absorption characteristics, and EFAST method and PROSPAIL model are integrated to construct nitrogen sensitive spectral index NDSI and RSI, nitrogen is commonly used for spectral index. The relationship between number and nitrogen nutrition status, the order of spectral index sensitive to wheat plants was determined. (1) PROSAIL model was used to simulate plant spectral reflectance data, and EFAST method was used to analyze the sensitivity analysis of plant reflectance spectra of each parameter of PROSAIL model in the range of 400-2500nm band. The results showed that the chlorophyll sensitivity was sensitive to chlorophyll sensitivity. The band range of sense is 515-745nm. According to the normalized vegetation index and ratio vegetation index, the spectral index of nitrogen content and plant nitrogen accumulation sensitivity to wheat plants is NDSI (546698), NDSI (667685), NDSI (539745), RSI (546694), RSI (667684), RSI (539745)). The spectral index NDSI and RSI and plant nitrogen content and plant are analyzed. The correlation of nitrogen accumulation, based on the empirical statistical relationship, established the spectral index and plant nitrogen content, the regression model of plant nitrogen accumulation, using scale 2, RMSE, RE as indicators to evaluate plant nitrogen nutrition status. The results showed that the first 5 spectral indices sensitive to nitrogen content in wheat plants were SRPI, NPCI, ND705, MCARI/MTVI2 and MTCI, to wheat. The first 5 spectral indices of plant nitrogen accumulation sensitivity are SR705, RI-half, NPCI, VOGb and mSR705. (5). By regression modeling, the original spectral reflectance characteristics of leaf and plant biomass, red edge parameters, continuum spectral absorption characteristics, EFAST method and PROSPECT (PROSAIL) model integrated and constructed by the EFAST method and PROSPECT (PROSAIL) model, are used to analyze the spectral index of biomass sensitive. The sequence of spectral indices sensitive to the biomass of leaves and plants was determined by the correlation of the related spectral index and biomass. (1) the PROSPECT (PROSAIL) model was used to simulate the spectral reflectance of leaves and plants, and the EFAST method was used to determine the plant parameters of the PROSPECT (PROSAIL) model in the range of 400-2500nm band. The spectral sensitivity analysis showed that the range of sensitivity to leaf and plant biomass was 749-2410nm. The spectral index of wheat leaf and plant biomass sensitive was NDSI (21262347, NDSI (16521686), RSI (21262347) and RSI (16521686). The spectral index NDSI and RSI and leaf and plant biomass were analyzed. " The regression model of spectral index and leaf and plant biomass was established based on the empirical statistical relationship. R2, RMSE and RE were used as indicators to evaluate the biomass of leaves and plants. The results showed that the first 5 spectral indices sensitive to wheat leaf biomass were mSR705, RI-1dB, VOGa, GNDVI and NDCI, and the first 5 sensitive to wheat plant biomass. The spectral index is VOGa, mSR705, REP, NDVI705 and mNDVI705. (6) to establish the key nitrogen dilution curve model of wheat leaves and plants in the study area. A model of "remote sensing information - Agronomy parameter nitrogen nutrition index" leaf and plant nitrogen nutrition diagnosis model was established. (1) the key nitrogen concentration curve model of wheat leaves was established in the study area Ncl=4.42 x W-0.18L The key nitrogen concentration curve model of the wheat plant in the study area is N =5.81 x W-0.54. (2) based on the =5.81 of the leaf "remote sensing information - Agronomy parameter - nitrogen nutrition index" and the R2 between the actual NNI and the actual NNI. The R2 between the NNI and the measured NNI based on the plant "remote sensing information - agronomic parameter nitrogen nutrition index" is 0.83. The results showed that the accuracy of the NNI diagnostic model was higher than that of the blade NNI diagnosis model. (7) the crop NNI was extracted from the UAV hyperspectral image of the national precision agricultural demonstration base in Xiaotangshan, the R2 between the NNI and the actual NNI was 0.66, and the R2 between the NNI and the actual NNI during the flowering period was 0.69, and the results reached significant correlation. The results show that it is feasible to estimate NNI based on the "remote sensing information - agronomic parameter nitrogen nutrition index" method. It can be consistent with the actual results. It provides a scientific basis for fast, accurate and real-time monitoring of crop nitrogen status, variable fertilization and yield prediction.
【学位授予单位】：中国矿业大学(北京)
【学位级别】：博士
【学位授予年份】：2016
【分类号】：S512.1;S513
，
本文编号：2162676
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