玉米生理参数及农田土壤信息高光谱监测模型研究
发布时间：2018-07-22 13:05
【摘要】：精准农业是一种高新技术与农业生产相结合的产业,是可持续农业发展的重要途径。高精度、及时的获取农作物长势和生态环境信息是精准农业实施的前提和基础,也是现代农业发展的关键技术之一。高光谱遥感具有波段多、间隔窄的特点,能构成独特的多维光谱空间,直接捕获地物的微弱光谱差异信息。高光谱为遥感信息的定量应用开辟了新的领域,也为农田信息获取带来了巨大的前景,并逐渐成为新兴精准农业最重要的技术手段之一。因此,应用高光谱遥感建立生理参数及农田土壤信息监测模型,可增强对作物生理参数及农田土壤信息的监测能力,提高作物生长及农田土壤信息监测的精度和准确性。本研究以田间试验的玉米为研究对象,结合地面高光谱遥感技术与生理参数分析技术,系统分析玉米不同生理参数及农田土壤信息的高光谱特征,在相关分析的基础上,提取特征波段、植被指数、高光谱特征参数、特征指数,应用简单统计回归(SSR)、偏最小二乘回归(PLSR)和人工神经网络(ANN)方法,建立玉米生理参数及农田土壤信息的高光谱监测模型,为动态监测玉米生长状况及科学的田间施肥管理提供理论依据和技术支持。主要研究结果如下:(1)随着玉米叶片花青素含量增加,550 nm处吸收峰增大,SVC和SOC光谱与玉米叶片花青素含量最大相关波段分别为548 nm和540.73 nm。以SVC和SOC光谱构建的两波段归一化指数和比值指数与叶片花青素含量相关性最高;基于SVC光谱特征指数建立的ANN模型,训练和验证R2分别为0.776和0.759,验证RMSE为0.111,RPD值为2.041,预测精度较高、模型比较稳定,能有效监测玉米叶片花青素含量;基于SOC光谱特征指数建立的ANN模型是玉米叶片花青素含量监测的最优模型,训练和验证R2分别为0.875和0.851,验证RMSE为0.087,RPD值为2.604。SOC光谱参数建立的模型拟合及验证精度整体高于SVC光谱参数建立的模型,特征指数建立的模型优于植被指数建立的模型;特征指数结合ANN方法是建立玉米叶片花青素含量监测模型的最优方法。(2)玉米不同生育期SPAD值的敏感波段有差异。植被指数D2、GNDVI、MSAVI、NDVI、OSAVI、OSAVI2、TCARI2/OSAVI2、TCARI2、TCARI和高光谱特征参数SDr/SDb、Sg、Ro均与玉米4个生育期叶片的SPAD值极显著相关,通用性较好。基于6-8叶期、10-12叶期的高光谱特征参数,开花吐丝期的植被指数,灌浆期、乳熟期的原始光谱建立的ANN模型训练及验证精度均较高,模型较稳定,是各个生育期玉米叶片SPAD值监测的最优模型。训练R2分别为0.845、0.880、0.806、0.763、0.785,经独立样本验证,R2分别为0.820、0.919、0.822、0.814、0.760,RMSE分别为0.677、0.454、0.746、0.818、0.774,RPD值分别为2.358、3.455、2.374、2.319、2.078。10-12叶期,以3种方法建立的模型均能对玉米叶片SPAD值进行有效监测。(3)不同生育期玉米光谱与生物量的相关性差异较大,植被指数GI、GNDVI、MSAVI、MTCI、NDVI、NDVI3、OSAVI、SR、OSAVI2、TCARI2、TCARI2/OSAVI2、MCARI2、DDn、SPVI、TVI、RTVI均在2个生育期与玉米生物量极显著相关;高光谱特征参数Rg、SRg、SDg在3个生育期与玉米生物量极显著相关,通用性较好。6-8叶期以原始光谱、10-12叶期以植被指数、开花吐丝期以一阶微分光谱建立的ANN模型,训练R2分别为0.908、0.938、0.800,验证R2分别为0.918、0.939、0.762,RMSE分别为0.086 kg·m-2、0.123 kg·m-2、0.400 kg·m-2,RPD值分别为3.507、4.051、2.051,训练和验证结果均较好,是监测各生育期玉米生物量的最优模型。6-8叶期和10-12叶期的监测模型精度高于开花吐丝期;乳熟期建立的模型不能进行生物量有效监测。(4)850-1790 nm和1960-2400 nm范围,随着玉米植株含水量增加波段深度增大,不同生育期玉米植株含水量与光谱的相关性差异较大;FD730-1330和新建光谱指数FDD(725,925)、FDD(725,1140)、FDD(725,1330)与玉米不同生育期植株含水量相关性较好,通用性较强。6-8叶期、10-12叶期、开花吐丝期,基于一阶微分光谱建立的ANN模型,经独立样本验证,预测值与实测值之间的R2分别为0.858、0.877、0.804,RMSE为0.359%、0.479%、0.819%,RPD值为2.654、2.850、2.261,模型的预测精度较高,稳定性较好,是进行各生育期玉米植株含水量监测的最优模型。灌浆期和乳熟期建立的模型预测效果不理想,有待进一步研究。(5)随土壤含水量增加光谱反射率下降,1400、1900 nm附近的水分吸收谷朝长波方向偏移。与土壤含水量相关性最大的光谱位于570、1430、1950 nm,相关性最大的吸收特征参数是最大吸收深度(D)、吸收总面积(A)、吸收峰右面积(RA)、吸收峰左面积(LA)。基于C1950、D1900、RA1900建立的一元线性模型和A1900、A1400建立的对数模型是预测土壤含水量的最优模型,拟合R2位于0.927-0.943之间,验证R2位于0.936-0.96之间,RMSE位于1.299-1.773%之间,RPD值位于3.538-4.885之间。(6)不同全氮含量的土壤光谱差异较大;碱解氮含量增大到一定值时,反射率之间的差异变小;与土壤氮含量相关性最好的两波段光谱指数是差值指数。以PLSR和ANN方法建立的全氮含量监测模型预测效果较好。其中,基于一阶微分光谱建立的ANN模型,训练和验证R2分别为0.886和0.880,RMSE为0.0077%和0.0086%,RPD值为2.971和2.846,训练和验证结果较好,模型最稳定,是监测土壤全氮含量的最优模型。基于CB+CS+CI建立的ANN模型,训练R2为0.757,验证R2为0.758,验证RMSE为2.1262 mg·kg-1,RPD值为2.033,是监测土壤碱解氮含量的最优模型。(7)光谱反射率随土壤磷含量增加而减小,当土壤磷含量增大到一定值时,土壤光谱反射率之间的差异变小。基于归一化微分、CB+CS和CB+CS+CI建立的ANN模型,可以对土壤有效磷含量进行准确预测,其中,CB+CS+CI建立的ANN模型预测效果最好,训练和验证R2分别为0.806和0.811,验证RMSE为2.691 mg·kg-1,RPD值为2.216;PLSR和ANN方法建立的模型精度较低,不能进行土壤全磷含量的有效监测。(8)土壤全钾含量较高时,对土壤光谱反射率影响较大;土壤速效钾含量对土壤光谱影响较小,变化规律不明显。以PLSR和ANN方法建立的模型精度均较高,能对土壤全钾含量进行准确预测。其中,基于波段深度微分建立的ANN模型是监测土壤全钾含量的最优模型,训练和验证R2分别为0.967和0.971,验证RMSE分别为0.033%和0.030%,RPD值分别为5.416和5.957。基于归一化微分光谱建立的ANN模型的训练和验证R2大于0.83,验证RMSE为14.457 mg·kg-1,RPD值为2.591,是土壤速效钾含量预测的最优模型。土壤全钾含量的预测精度高于速效钾含量,微分变换可以提高模型的预测精度。
[Abstract]:Precision agriculture is an industry combining high and new technology with agricultural production. It is an important way for sustainable agricultural development. High precision, timely acquisition of crop growth and ecological environment information is the premise and basis of precision agriculture implementation, and is also one of the key technologies of modern agricultural development. Hyperspectral remote sensing has many bands and narrow spacing. It can form a unique multi-dimensional spectrum space and directly capture the weak spectral difference information of the ground objects. Hyperspectral has opened up a new field for the quantitative application of remote sensing information. It also brings great prospects for the acquisition of farmland information, and has gradually become one of the most important technical means of new precision agriculture. Therefore, the application of hyperspectral remote sensing is established. The physiological parameters and farmland soil information monitoring model can enhance the monitoring ability of crop physiological parameters and farmland soil information, improve the accuracy and accuracy of crop growth and farmland soil information monitoring. Based on the analysis of different physiological parameters of maize and the hyperspectral characteristics of farmland soil information, based on the correlation analysis, the characteristic bands, vegetation index, hyperspectral characteristic parameters, characteristic index, simple statistical regression (SSR), partial least squares regression (PLSR) and artificial neural network (ANN) were used to establish maize physiological parameters and farmland soil information. The hyperspectral monitoring model provides theoretical basis and technical support for dynamic monitoring of maize growth and scientific field fertilization management. The main results are as follows: (1) with the increase of anthocyanin content in maize leaves, the absorption peaks at 550 nm are increased, and the bands of the SVC and SOC spectra and the maximum content of leaf anthocyanins are 548 nm and 540, respectively. The two band normalization index and ratio index of.73 nm. based on SVC and SOC spectra have the highest correlation with the content of leaf anthocyanins. The ANN model based on the SVC spectrum characteristic index is 0.776 and 0.759, respectively. The RMSE is 0.111, the RPD value is 2.041, the prediction accuracy is higher, the model is more stable, and the maize leaf can be effectively monitored. Anthocyanin content; ANN model based on SOC spectral characteristic index is the best model for monitoring anthocyanin content in maize leaves, training and verification R2 are 0.875 and 0.851, RMSE is 0.087, RPD value is set up by 2.604.SOC spectral parameters and the accuracy is higher than the model established by SVC spectral parameters, and the characteristic index is established. The model is superior to the model established by the vegetation index. The characteristic index combined with ANN method is the best method to establish the maize leaf anthocyanin content monitoring model. (2) the sensitive bands of SPAD values in different growth stages of maize are different. The vegetation index D2, GNDVI, MSAVI, NDVI, OSAVI, OSAVI2, TCARI2/OSAVI2, TCARI2, TCARI, and hyperspectral characteristic parameters SDr/SDb The SPAD value of the leaves of 4 growing periods of maize was very significant, and the generality was better. Based on the 6-8 leaf stage, the hyperspectral characteristic parameters of the 10-12 leaf period, the vegetation index of the flowering stage, the grain filling period and the original spectrum of the milk ripening period were higher, the model type was more stable, and the SPAD value monitoring of the leaves of each growth period was the monitoring of the SPAD value of the leaves of the maize. The optimal model. The training R2 is 0.845,0.880,0.806,0.763,0.785 respectively. The independent samples are verified that R2 is 0.820,0.919,0.822,0.814,0.760, RMSE is 0.677,0.454,0.746,0.818,0.774 and RPD value is 2.358,3.455,2.374,2.319,2.078.10-12 leaf period respectively. The model established by 3 methods can effectively monitor the SPAD value of maize leaves. (3) the correlation between the spectral and biomass of Maize at different growth stages was quite different, the vegetation index GI, GNDVI, MSAVI, MTCI, NDVI, NDVI3, OSAVI, SR, OSAVI2, TCARI2, TCARI2/OSAVI2 were all significantly related to the maize biomass at the 2 growth stages, and the hyperspectral characteristic parameters were very significant in 3 growth periods and maize biomass. It is related that the.6-8 leaf phase is better for the.6-8 leaf phase, with the vegetation index and the ANN model of the first order differential spectrum in the flowering stage, and the training R2 is 0.908,0.938,0.800 respectively. The R2 is 0.918,0.939,0.762, and the RMSE is 0.086 kg. M-2,0.123 kg. M-2,0.400 kg. The optimal model of maize biomass in each growth period,.6-8 leaf stage and 10-12 leaf stage, was higher than flowering. (4) the range of 850-1790 nm and 1960-2400 nm, with the increase of the depth of maize plant water content and the increase of different growth bands. The correlation between the water content and the spectrum of the maize plants was great. FD730-1330 and the new spectral index FDD (725925), FDD (7251140), FDD (7251330) were well correlated with the water content of the plants at different growth stages. The general.6-8 leaf stage, the 10-12 leaf stage, the flowering spit period, and the ANN model based on the first order differential spectrum were independent samples. It is proved that the R2 between the predicted value and the measured value is 0.858,0.877,0.804, RMSE is 0.359%, 0.479%, 0.819%, and RPD is 2.654,2.850,2.261. The prediction accuracy of the model is higher and the stability is better. It is the optimal model for monitoring the water content of maize plants at various growth stages. (5) (5) with the increase of soil moisture content, the spectral reflectance decreased and the water absorption Valley in the vicinity of 14001900 nm shifted toward the long wave direction. The maximum correlation of the correlation with soil moisture was located at 57014301950 nm. The maximum absorption characteristic parameters were the maximum absorption depth (D), the total absorption area (A), the absorption peak right area (RA), the absorption peak left Area (LA). The linear model based on C1950, D1900 and RA1900 and the logarithmic model established by A1900 and A1400 are the best models for predicting soil water content. The fitting of R2 is between 0.927-0.943 and R2 is between 0.936-0.96 and RMSE lies between 1.299-1.773% and between 1.299-1.773%. (6) the difference of soil spectral difference of different total nitrogen content The difference of the reflectance is smaller when the content of alkali nitrogen is increased to a certain value. The two band spectral index of the best correlation with the soil nitrogen content is the difference index. The total nitrogen content monitoring model based on PLSR and ANN is better. The ANN model based on the first order differential spectrum is trained and verified that R2 is 0, respectively. .886 and 0.880, RMSE are 0.0077% and 0.0086%, RPD values are 2.971 and 2.846. The results of training and verification are better. The model is the most stable model. The ANN model based on CB+CS+CI, training R2 is 0.757, and R2 is 0.758, RMSE is 2.1262 mg kg-1, RPD is 2.033, which is the best monitoring of soil alkaline nitrogen content. (7) (7) the spectral reflectance decreases with the increase of soil phosphorus content. When the soil phosphorus content increases to a certain value, the difference between soil spectral reflectance is smaller. Based on the normalization and differentiation, the ANN model established by CB+CS and CB+CS+CI can accurately predict the content of soil available phosphorus, of which the ANN model established by CB+CS+CI is the best. The training and verification of R2 were 0.806 and 0.811 respectively, which proved that RMSE was 2.691 mg. Kg-1, RPD value was 2.216; PLSR and ANN methods had low model precision. (8) the soil total potassium content was higher, the soil spectral reflectance was more affected; soil available potassium content had little influence on Soil spectrum. The precision of the model established by PLSR and ANN is high, and the total potassium content of soil can be accurately predicted. Among them, the ANN model based on the differential wave band depth differential is the best model for monitoring the total potassium content of soil. The training and verification of R2 are 0.967 and 0.971 respectively, and the RMSE is 0.033% and 0.030% respectively, and the RPD value is 5.416, respectively. The ANN model based on normalized differential spectrum (5.957.) is trained and verified that R2 is more than 0.83, and RMSE is 14.457 mg. Kg-1, and the RPD value is 2.591. It is the best prediction model of soil available potassium content. The prediction accuracy of soil total potassium content is higher than the content of available potassium. Differential transformation can improve the prediction accuracy of the model.
【学位授予单位】：西北农林科技大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：S513
，
本文编号：2137559
[Abstract]:Precision agriculture is an industry combining high and new technology with agricultural production. It is an important way for sustainable agricultural development. High precision, timely acquisition of crop growth and ecological environment information is the premise and basis of precision agriculture implementation, and is also one of the key technologies of modern agricultural development. Hyperspectral remote sensing has many bands and narrow spacing. It can form a unique multi-dimensional spectrum space and directly capture the weak spectral difference information of the ground objects. Hyperspectral has opened up a new field for the quantitative application of remote sensing information. It also brings great prospects for the acquisition of farmland information, and has gradually become one of the most important technical means of new precision agriculture. Therefore, the application of hyperspectral remote sensing is established. The physiological parameters and farmland soil information monitoring model can enhance the monitoring ability of crop physiological parameters and farmland soil information, improve the accuracy and accuracy of crop growth and farmland soil information monitoring. Based on the analysis of different physiological parameters of maize and the hyperspectral characteristics of farmland soil information, based on the correlation analysis, the characteristic bands, vegetation index, hyperspectral characteristic parameters, characteristic index, simple statistical regression (SSR), partial least squares regression (PLSR) and artificial neural network (ANN) were used to establish maize physiological parameters and farmland soil information. The hyperspectral monitoring model provides theoretical basis and technical support for dynamic monitoring of maize growth and scientific field fertilization management. The main results are as follows: (1) with the increase of anthocyanin content in maize leaves, the absorption peaks at 550 nm are increased, and the bands of the SVC and SOC spectra and the maximum content of leaf anthocyanins are 548 nm and 540, respectively. The two band normalization index and ratio index of.73 nm. based on SVC and SOC spectra have the highest correlation with the content of leaf anthocyanins. The ANN model based on the SVC spectrum characteristic index is 0.776 and 0.759, respectively. The RMSE is 0.111, the RPD value is 2.041, the prediction accuracy is higher, the model is more stable, and the maize leaf can be effectively monitored. Anthocyanin content; ANN model based on SOC spectral characteristic index is the best model for monitoring anthocyanin content in maize leaves, training and verification R2 are 0.875 and 0.851, RMSE is 0.087, RPD value is set up by 2.604.SOC spectral parameters and the accuracy is higher than the model established by SVC spectral parameters, and the characteristic index is established. The model is superior to the model established by the vegetation index. The characteristic index combined with ANN method is the best method to establish the maize leaf anthocyanin content monitoring model. (2) the sensitive bands of SPAD values in different growth stages of maize are different. The vegetation index D2, GNDVI, MSAVI, NDVI, OSAVI, OSAVI2, TCARI2/OSAVI2, TCARI2, TCARI, and hyperspectral characteristic parameters SDr/SDb The SPAD value of the leaves of 4 growing periods of maize was very significant, and the generality was better. Based on the 6-8 leaf stage, the hyperspectral characteristic parameters of the 10-12 leaf period, the vegetation index of the flowering stage, the grain filling period and the original spectrum of the milk ripening period were higher, the model type was more stable, and the SPAD value monitoring of the leaves of each growth period was the monitoring of the SPAD value of the leaves of the maize. The optimal model. The training R2 is 0.845,0.880,0.806,0.763,0.785 respectively. The independent samples are verified that R2 is 0.820,0.919,0.822,0.814,0.760, RMSE is 0.677,0.454,0.746,0.818,0.774 and RPD value is 2.358,3.455,2.374,2.319,2.078.10-12 leaf period respectively. The model established by 3 methods can effectively monitor the SPAD value of maize leaves. (3) the correlation between the spectral and biomass of Maize at different growth stages was quite different, the vegetation index GI, GNDVI, MSAVI, MTCI, NDVI, NDVI3, OSAVI, SR, OSAVI2, TCARI2, TCARI2/OSAVI2 were all significantly related to the maize biomass at the 2 growth stages, and the hyperspectral characteristic parameters were very significant in 3 growth periods and maize biomass. It is related that the.6-8 leaf phase is better for the.6-8 leaf phase, with the vegetation index and the ANN model of the first order differential spectrum in the flowering stage, and the training R2 is 0.908,0.938,0.800 respectively. The R2 is 0.918,0.939,0.762, and the RMSE is 0.086 kg. M-2,0.123 kg. M-2,0.400 kg. The optimal model of maize biomass in each growth period,.6-8 leaf stage and 10-12 leaf stage, was higher than flowering. (4) the range of 850-1790 nm and 1960-2400 nm, with the increase of the depth of maize plant water content and the increase of different growth bands. The correlation between the water content and the spectrum of the maize plants was great. FD730-1330 and the new spectral index FDD (725925), FDD (7251140), FDD (7251330) were well correlated with the water content of the plants at different growth stages. The general.6-8 leaf stage, the 10-12 leaf stage, the flowering spit period, and the ANN model based on the first order differential spectrum were independent samples. It is proved that the R2 between the predicted value and the measured value is 0.858,0.877,0.804, RMSE is 0.359%, 0.479%, 0.819%, and RPD is 2.654,2.850,2.261. The prediction accuracy of the model is higher and the stability is better. It is the optimal model for monitoring the water content of maize plants at various growth stages. (5) (5) with the increase of soil moisture content, the spectral reflectance decreased and the water absorption Valley in the vicinity of 14001900 nm shifted toward the long wave direction. The maximum correlation of the correlation with soil moisture was located at 57014301950 nm. The maximum absorption characteristic parameters were the maximum absorption depth (D), the total absorption area (A), the absorption peak right area (RA), the absorption peak left Area (LA). The linear model based on C1950, D1900 and RA1900 and the logarithmic model established by A1900 and A1400 are the best models for predicting soil water content. The fitting of R2 is between 0.927-0.943 and R2 is between 0.936-0.96 and RMSE lies between 1.299-1.773% and between 1.299-1.773%. (6) the difference of soil spectral difference of different total nitrogen content The difference of the reflectance is smaller when the content of alkali nitrogen is increased to a certain value. The two band spectral index of the best correlation with the soil nitrogen content is the difference index. The total nitrogen content monitoring model based on PLSR and ANN is better. The ANN model based on the first order differential spectrum is trained and verified that R2 is 0, respectively. .886 and 0.880, RMSE are 0.0077% and 0.0086%, RPD values are 2.971 and 2.846. The results of training and verification are better. The model is the most stable model. The ANN model based on CB+CS+CI, training R2 is 0.757, and R2 is 0.758, RMSE is 2.1262 mg kg-1, RPD is 2.033, which is the best monitoring of soil alkaline nitrogen content. (7) (7) the spectral reflectance decreases with the increase of soil phosphorus content. When the soil phosphorus content increases to a certain value, the difference between soil spectral reflectance is smaller. Based on the normalization and differentiation, the ANN model established by CB+CS and CB+CS+CI can accurately predict the content of soil available phosphorus, of which the ANN model established by CB+CS+CI is the best. The training and verification of R2 were 0.806 and 0.811 respectively, which proved that RMSE was 2.691 mg. Kg-1, RPD value was 2.216; PLSR and ANN methods had low model precision. (8) the soil total potassium content was higher, the soil spectral reflectance was more affected; soil available potassium content had little influence on Soil spectrum. The precision of the model established by PLSR and ANN is high, and the total potassium content of soil can be accurately predicted. Among them, the ANN model based on the differential wave band depth differential is the best model for monitoring the total potassium content of soil. The training and verification of R2 are 0.967 and 0.971 respectively, and the RMSE is 0.033% and 0.030% respectively, and the RPD value is 5.416, respectively. The ANN model based on normalized differential spectrum (5.957.) is trained and verified that R2 is more than 0.83, and RMSE is 14.457 mg. Kg-1, and the RPD value is 2.591. It is the best prediction model of soil available potassium content. The prediction accuracy of soil total potassium content is higher than the content of available potassium. Differential transformation can improve the prediction accuracy of the model.
【学位授予单位】：西北农林科技大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：S513
，
本文编号：2137559
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