基于冠层高光谱的加工番茄单产估算模型的研究
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摘要
高光谱分辨率遥感从产生发展至今仅有二十多年的历史,但却发展迅猛,已在很多学科领域(地理学、地质学、生态学、植被研究、环境科学、大气科学和海洋科学)得到广泛的研究和应用。其显著特点是在特定光谱区域内有高光谱分辨率同时获取连续的地物光谱曲线,光谱信息量大,由于高光谱遥感能提供更多的精细光谱信息,有利于选择各种单一地物光谱差异明显的波段,使它也广泛地应用于植被遥感中,并已成为地表植被地学过程中对地观测的强有力工具。
本研究主要探讨加工番茄产量形成的几个关键时期——苗II期至采收期冠层反射光谱特征,采用两类分析方法,一是通过多元统计和逐步回归分析方法建立观测光谱数据或由此衍生的植被指数与加工番茄单位面积产量之间的关系;二是基于加工番茄观测光谱变量的分析技术,筛选出了能较好地估测加工番茄产量的光谱变量和农学参数,说明利用高光谱遥感技术能够提取作物产量的信息状况。主要研究结果如下:
1、建立了加工番茄单时相光谱估产模型,筛选出不同时期最佳光谱估产变量。加工番茄最佳单时相光谱估产模型主要以一元三次的方程、S方程和幂方程的拟合度较好。
2、建立了加工番茄多时相光谱估产模型,两个生育期复合光谱估产模型中以坐果期和青熟期组合的估产模型精度较高;三个生育期复合光谱估产模型中以坐果期、青熟期和采收期组合的估产模型精度较高;四个生育期复合光谱估产模型的精度最高。其结果与前人在水稻和小麦遥感估产中的研究结果一致。
3、筛选出各生育时期与主要农学参数最相关的光谱变量,并建立了主要农学参数与最佳光谱变量以及主要农学参数选与产量的回归估算模型。并寻找出了各个时期拟合度最好的农学参数和光谱变量的回归方程
4、构建了复合光谱估产模型。筛选出加工番茄叶面积指数(LAI)、单株鲜生物量(BPPf)、茎含水量(SMC)、叶绿素密度(CH.D)、单株干生物量(BPPd)和叶面积氮指数(LANI)为复合光谱估产模型的主要农学参数。尤以加工番茄单株鲜生物量(BPPf)与产量的相关性最好。
5、在光谱变量-农学参数-产量7种复合光谱估产模型中,均以四个生育期组合的复合光谱估产模型的精度最好。且这7种复合光谱估产模型的精度均高于单时相光谱估产模型和多时相光谱估产模型的精度。
Hyperspectral remote sensing is just more than 20 years history from the emergence and eevclopment to now.however, it gets booming development and has been widely employed in many research fields(geography、geolgy、ecology、vegetaion study、environmental science、atmospheric science and marine sciences). Hyperspectral remote sensing’s peculiarity is that that can note the continual spectral curves of ground objects with hyperspectral resolution at the same time in specifical waveband range. Because hyperspectral remote sensing can obtain and provide more subtle spectral information. It is propitious to select clear bands for identifying single object spectrum, which made it largely used in vegetation remote sensing.And now hyperspectral remote sensing becomes strongly tool of surface observation in land planting.
The main contents of the study are as following:
1. Sets up The models of single phase spectrum evaluates yield. Filters up the best spectrum evaluates yield variable. When processing tomato best list the spectrum evaluates asstes the model mainly draws up properly by Three timesfunction ,power equationand S model is better.
2. Sets up the models of multi-temporal spectrum evaluates yield.The precision of models during two periods of duration compound spectral evaluate yield of the multi-temporal and initial harvest stage combination were higher, The precision of models during three periodsof duration compound spectra evaluate yield of multi-temporal, initial harvest stage and harvesting time combination evaluate yield were higher, The precision of models of four periods of duration compoundspectra evaluate yield were the highest.
3. Sets up Regression Models of main agronomy parameters and the best spectrum variable ,of main agronomy parameters and processing tomato yield by filtering up the most remarkable correlation Spectrum Variables with main agronomy parameters in different growth stages.Finds out the best fitting degree regression equation of agronomy parameters and spectrum variable in different growth stages .
4.Setting up the models of compound spectrum evaluating yield. Sieve out LAI , BPPF and Chl concentrationfor main agriculture parameter. BPPF were the best agriculture parameter.
5.Among seven compound spectrum estimate the yield models of spectrum variable - agronomy parameters-yield,Their models precisions of four periodscompound spectrum estimate the yield models are all the best.while Their models precisions are better than models precisions of single phase spectrum evaluates yield and models precision of multi-temporal spectrum evaluates yield
本研究主要探讨加工番茄产量形成的几个关键时期——苗II期至采收期冠层反射光谱特征,采用两类分析方法,一是通过多元统计和逐步回归分析方法建立观测光谱数据或由此衍生的植被指数与加工番茄单位面积产量之间的关系;二是基于加工番茄观测光谱变量的分析技术,筛选出了能较好地估测加工番茄产量的光谱变量和农学参数,说明利用高光谱遥感技术能够提取作物产量的信息状况。主要研究结果如下:
1、建立了加工番茄单时相光谱估产模型,筛选出不同时期最佳光谱估产变量。加工番茄最佳单时相光谱估产模型主要以一元三次的方程、S方程和幂方程的拟合度较好。
2、建立了加工番茄多时相光谱估产模型,两个生育期复合光谱估产模型中以坐果期和青熟期组合的估产模型精度较高;三个生育期复合光谱估产模型中以坐果期、青熟期和采收期组合的估产模型精度较高;四个生育期复合光谱估产模型的精度最高。其结果与前人在水稻和小麦遥感估产中的研究结果一致。
3、筛选出各生育时期与主要农学参数最相关的光谱变量,并建立了主要农学参数与最佳光谱变量以及主要农学参数选与产量的回归估算模型。并寻找出了各个时期拟合度最好的农学参数和光谱变量的回归方程
4、构建了复合光谱估产模型。筛选出加工番茄叶面积指数(LAI)、单株鲜生物量(BPPf)、茎含水量(SMC)、叶绿素密度(CH.D)、单株干生物量(BPPd)和叶面积氮指数(LANI)为复合光谱估产模型的主要农学参数。尤以加工番茄单株鲜生物量(BPPf)与产量的相关性最好。
5、在光谱变量-农学参数-产量7种复合光谱估产模型中,均以四个生育期组合的复合光谱估产模型的精度最好。且这7种复合光谱估产模型的精度均高于单时相光谱估产模型和多时相光谱估产模型的精度。
Hyperspectral remote sensing is just more than 20 years history from the emergence and eevclopment to now.however, it gets booming development and has been widely employed in many research fields(geography、geolgy、ecology、vegetaion study、environmental science、atmospheric science and marine sciences). Hyperspectral remote sensing’s peculiarity is that that can note the continual spectral curves of ground objects with hyperspectral resolution at the same time in specifical waveband range. Because hyperspectral remote sensing can obtain and provide more subtle spectral information. It is propitious to select clear bands for identifying single object spectrum, which made it largely used in vegetation remote sensing.And now hyperspectral remote sensing becomes strongly tool of surface observation in land planting.
The main contents of the study are as following:
1. Sets up The models of single phase spectrum evaluates yield. Filters up the best spectrum evaluates yield variable. When processing tomato best list the spectrum evaluates asstes the model mainly draws up properly by Three timesfunction ,power equationand S model is better.
2. Sets up the models of multi-temporal spectrum evaluates yield.The precision of models during two periods of duration compound spectral evaluate yield of the multi-temporal and initial harvest stage combination were higher, The precision of models during three periodsof duration compound spectra evaluate yield of multi-temporal, initial harvest stage and harvesting time combination evaluate yield were higher, The precision of models of four periods of duration compoundspectra evaluate yield were the highest.
3. Sets up Regression Models of main agronomy parameters and the best spectrum variable ,of main agronomy parameters and processing tomato yield by filtering up the most remarkable correlation Spectrum Variables with main agronomy parameters in different growth stages.Finds out the best fitting degree regression equation of agronomy parameters and spectrum variable in different growth stages .
4.Setting up the models of compound spectrum evaluating yield. Sieve out LAI , BPPF and Chl concentrationfor main agriculture parameter. BPPF were the best agriculture parameter.
5.Among seven compound spectrum estimate the yield models of spectrum variable - agronomy parameters-yield,Their models precisions of four periodscompound spectrum estimate the yield models are all the best.while Their models precisions are better than models precisions of single phase spectrum evaluates yield and models precision of multi-temporal spectrum evaluates yield
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