本文選題：土壤濕度　切入點(diǎn)：HASM模型　出處：《山東農(nóng)業(yè)大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：本文以東營(yíng)、德州和濱州為研究區(qū),基于地面實(shí)測(cè)獲取的土壤濕度數(shù)據(jù),利用GS+7.0軟件進(jìn)行空間變異特征分析,選取最適宜半變異方差函數(shù)參數(shù)。采用統(tǒng)計(jì)分析、GIS插值、緩沖區(qū)分析等方法,分析研究區(qū)域土壤濕度狀況和空間變異規(guī)律。常用的插值模型和HASM插值模型相結(jié)合,對(duì)比得出模型的優(yōu)缺點(diǎn)和適用范圍。對(duì)空間變異程度大的區(qū)域增加采樣點(diǎn)數(shù)量,提高了HASM模型插值結(jié)果的精度。采用野外高光譜數(shù)據(jù)與采樣點(diǎn)數(shù)據(jù)建立土壤濕度的高光譜估測(cè)模型,實(shí)現(xiàn)快速獲取關(guān)鍵點(diǎn)的土壤濕度。將插值所得到的土壤濕度值作為“空間擴(kuò)張”后的點(diǎn)數(shù)據(jù),建立與遙感衛(wèi)星波段反射率的關(guān)系模型,實(shí)現(xiàn)大面積土壤濕度值的獲取。土壤濕度屬于中等空間變異性。建立了野外高光譜土壤濕度預(yù)測(cè)模型,模型決定系數(shù)R2為0.85。建立了野外土壤濕度值與對(duì)應(yīng)室內(nèi)測(cè)量值關(guān)系模型,模型決定系數(shù)R2為0.97。在LandSat8影像中,建立了基于波段多種組合形式光譜參量的土壤含水量反演模型,模型決定系數(shù)R2=為0.65。本文研究的主要內(nèi)容包括:(1)根據(jù)土壤濕度實(shí)測(cè)數(shù)據(jù),分別采用統(tǒng)計(jì)分析、GIS插值、緩沖區(qū)分析等方法,對(duì)山東省德州、濱州、東營(yíng)三地區(qū)土壤濕度進(jìn)行空間分析,通過(guò)半變異函數(shù)參數(shù)選取實(shí)驗(yàn),進(jìn)行空間探索性分析,選出最優(yōu)空間變異參數(shù),找到最適合的插值模型,經(jīng)過(guò)交叉驗(yàn)證,比較插值方法優(yōu)缺點(diǎn),提高插值結(jié)果精度。分析研究區(qū)土壤濕度的空間分布特征。(2)為解決采樣點(diǎn)數(shù)量不足的問(wèn)題,采用HASM高精度模型插值方法,對(duì)研究區(qū)的六組數(shù)據(jù)分別進(jìn)行插值實(shí)驗(yàn),得到精度更高的土壤濕度空間分布模擬。對(duì)研究區(qū)空間變異性大的區(qū)域增加采樣點(diǎn)數(shù)量,重新對(duì)土壤濕度進(jìn)行空間分布模擬。(3)利用相關(guān)分析和多元逐步回歸分析,地面實(shí)測(cè)土壤濕度數(shù)據(jù)結(jié)合野外高光譜數(shù)據(jù)建立土壤濕度估測(cè)模型,獲取關(guān)鍵點(diǎn)土壤濕度值。(4)建立高光譜數(shù)據(jù)與土壤濕度關(guān)系模型。選取土壤濕度敏感波段,采用多元線性回歸方法,運(yùn)用插值后的作為“空間擴(kuò)張”后的點(diǎn)數(shù)據(jù),建立高光譜數(shù)據(jù)與土壤濕度關(guān)系模型,獲得大面積土壤濕度。
[Abstract]:Taking Dongying, Texas and Binzhou as the study areas, based on the soil moisture data obtained from the ground measurements, the spatial variation characteristics were analyzed with GS 7.0 software, and the most suitable parameters of semi-variance variance function were selected. The statistical analysis and GIS interpolation were used. Buffer analysis and other methods to analyze and study the regional soil moisture status and spatial variability. Commonly used interpolation model and HASM interpolation model combined, Compare the advantages and disadvantages of the model and the scope of application. For areas with large spatial variation, increase the number of sampling points, The accuracy of interpolation results of HASM model was improved. The hyperspectral estimation model of soil moisture was established by using field hyperspectral data and sampling point data. The soil moisture value obtained by interpolation is regarded as the point data after "spatial expansion", and the model of reflectivity of remote sensing satellite band is established. The prediction model of field hyperspectral soil moisture is established, and the determination coefficient R2 is 0.85. The relationship model between field soil moisture value and corresponding indoor measurement value is established. The model determination coefficient R2 is 0.97. In the LandSat8 image, a soil moisture inversion model based on spectral parameters in various spectral forms is established. The model determination coefficient R _ 2 = 0.65. The main contents of this study include: (1) based on the soil moisture measured data, the model determination coefficient R _ 2 = 0.65. The spatial analysis of soil moisture in three areas of Texas, Binzhou and Dongying in Shandong Province was carried out by means of statistical analysis, GIS interpolation and buffer analysis, and the spatial exploratory analysis was carried out through the experiment of parameter selection of semi-variable function. The optimal spatial variation parameters are selected and the most suitable interpolation model is found. After cross-validation, the advantages and disadvantages of the interpolation method are compared. In order to solve the problem of insufficient number of sampling points, HASM high precision model interpolation method was used to carry out interpolation experiments on six groups of data in the study area. The spatial distribution simulation of soil moisture with higher precision was obtained. The number of sampling points was increased in the study area with high spatial variability, and the spatial distribution simulation of soil moisture was carried out again by means of correlation analysis and multivariate stepwise regression analysis. Soil moisture estimation model was established by combining soil moisture data measured on the ground with field hyperspectral data. The relationship between hyperspectral data and soil moisture was established by obtaining the key point soil moisture value. The sensitive bands of soil moisture were selected. By using the multivariate linear regression method and the interpolated point data after "spatial expansion", a model of the relationship between hyperspectral data and soil moisture was established, and a large area of soil moisture was obtained.
【學(xué)位授予單位】：山東農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：S152.7

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