本文關(guān)鍵詞： 土壤 水分 鹽分 高光譜 參數(shù)反演　出處：《武漢大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：農(nóng)田水分的高效利用對于解決我國水資源短缺和糧食安全問題非常重要,并且這一需求在干旱、半干旱地區(qū)尤其突出。然而已有的有關(guān)農(nóng)田水鹽運(yùn)移的研究多為側(cè)重于土壤的控制實(shí)驗(yàn)或模擬,在作物生長情況下,受氣象條件、土壤條件以及人類活動的多重影響,農(nóng)田水鹽狀況在時間和空間上都會發(fā)生變化,即農(nóng)田四維水鹽信息發(fā)生變化,這些變化是干旱、半干旱地區(qū)鹽漬農(nóng)田節(jié)水灌溉與糧食增產(chǎn)的重要依據(jù)。但考慮到傳統(tǒng)水鹽監(jiān)測技術(shù)耗時長、花費(fèi)大等不足,目前仍然難以在區(qū)域上對這一時空變化進(jìn)行準(zhǔn)確描述。因此有必要開展鹽漬農(nóng)田水鹽時空變化的研究,充分利用遙感數(shù)據(jù),發(fā)展適用于區(qū)域尺度的提取四維水鹽信息的有效方法。本文首先研究了高光譜遙感定量提取表層土壤水鹽信息的方法,通過研究發(fā)現(xiàn)光譜數(shù)據(jù)前處理(如歸一化變換、導(dǎo)數(shù)變換等)對于提取敏感光譜信號非常重要；選擇少數(shù)敏感波段建立定量提取土壤水鹽的模型不僅精度較高,而且模型簡單穩(wěn)定,物理意義明顯。通過基于主成分分析的評價系統(tǒng)(PCAr),本文選擇了對土壤水鹽敏感的14個波段,即440、540、570、1390、1430、1460、1740、1870、1900、1940、2010、2270、2350和2410rim。通過綜合分析土壤水分、鹽分、質(zhì)地三個主要因素和光譜的關(guān)系,發(fā)現(xiàn)土壤鹽分信息提取受土壤水分和質(zhì)地的影響較大,并建立了土壤水分和鹽分的聯(lián)合提取模型,基于提取的水分信息獲取鹽分信息。與直接提取鹽分信息相比,聯(lián)合提取模型可以顯著提高土壤鹽分信息的提取精度。具體而言,針對室內(nèi)土壤樣本,直接估計(jì)土壤鹽分時,決定系數(shù)R2為0.47,相對均方根誤差rRMSE為0.380；采用聯(lián)合模型后,精度有明顯提高,0.63R20.95、0.132rRMSE0.366。以上遙感技術(shù)提取的水鹽數(shù)據(jù)可以補(bǔ)充野外樣本數(shù)據(jù),從而在區(qū)域尺度上較準(zhǔn)確的提取土壤水鹽信息。利用土壤表層和根區(qū)水鹽的關(guān)系,可以研究水鹽的空間變異關(guān)系。但是這種基于空間分析的方法僅能提取三維土壤水鹽信息,而通過水動力學(xué)模型可以模擬土壤水分在包括時間維度上的四維變化情況。這種模擬方法會受水動力參數(shù)選取的影響。本文提出了一種全局參數(shù)反演模型,通過反演全局優(yōu)化因子,修正水動力參數(shù)偏差,并控制參數(shù)在允許范圍內(nèi)變化。本文共建立了三個反演模型,GW5、GW10和GW30,全局優(yōu)化因子的數(shù)量分別為5、10和30。相對于正演模型預(yù)測的剖面水分精度R2=0.23,本文建立的GW5、GW10和GW30反演模型的預(yù)測精度R2分別為0.313、0.765、0.998。使用修正的赤池信息量準(zhǔn)則AICc綜合評價模型的復(fù)雜度和模型擬合的優(yōu)良性,避免過擬合情況,發(fā)現(xiàn)結(jié)構(gòu)最簡單的GW5模型精度最高(AICc=-467.367),這主要是因?yàn)樵撗芯繀^(qū)域質(zhì)地比較均一,水動力參數(shù)的異質(zhì)性不明顯。根據(jù)數(shù)據(jù)情況的不同,基于GW5模型的結(jié)構(gòu)建立一組模型S GW5,并通過兩年的野外實(shí)驗(yàn)研究和驗(yàn)證,發(fā)現(xiàn)該組模型在研究區(qū)域的實(shí)用性良好。其中僅使用遙感數(shù)據(jù)預(yù)測的水分作為目標(biāo)函數(shù)可建立V(RS)模型,使用2013年的野外數(shù)據(jù)校正V(RS)模型,R2=0.892,RMSE=0.01,使用2014年的野外數(shù)據(jù)進(jìn)行驗(yàn)證,R2=0.738, RMSE=0.034。結(jié)果表明結(jié)合遙感數(shù)據(jù)與S GW5反演模型,預(yù)測作物生長各個階段的土壤水分?jǐn)?shù)據(jù)的精度顯著高于正演模型。本文建立的土壤水鹽的聯(lián)合提取模型,可以廣泛運(yùn)用于定量提取易產(chǎn)生混合光譜的各種物質(zhì)。通過遙感數(shù)據(jù)與全局參數(shù)反演模型結(jié)合,本文提出了區(qū)域農(nóng)田四維水鹽信息提取的方法,可廣泛運(yùn)用于研究不同環(huán)境下土壤水鹽信息的時空變化。
[Abstract]:The efficient use of water is very important to solve the shortage of water resources and food security in our country, and the demand in arid and semi arid area in particular. However, research on farmland water and salt transport have been mostly focused on soil control experiments or simulation, in the growth of crops, affected by weather conditions, multiple effects of soil conditions and human activities, water salt status will change both in time and space, namely the farmland four-dimensional water salt information changes, these changes are an important basis for drought, half arid area of salinized farmland water-saving irrigation and crop yield. But considering the traditional water salt monitoring techniques are time-consuming, lack of cost so, it is still hard to in the region of the spatial and temporal changes were described accurately. So it is necessary to study the spatial variation of the saline water salt, make full use of the remote sensing data, development The effective method for information extraction of water and salt for four-dimensional regional scale. This paper studies the quantitative method of hyperspectral remote sensing information extraction of water and salt in surface soil, through research found that the spectral data pretreatment (such as normalized transformation, derivative transformation etc.) for the extraction of sensitive spectral signal is very important; not only the precision of the model is high sensitive bands to establish a quantitative extraction of soil water and salt, and the model is simple and stable, clear physical meaning. Through the evaluation system based on principal component analysis (PCAr), this paper chose to soil water salt sensitive 14 waves, namely 4405405701390143014601740187019001940201022702350 and 2410rim. through the comprehensive analysis of soil moisture, salinity, the relationship between three main factors and the texture spectrum. Found that the soil salinity information extraction is affected by soil moisture and texture, and the establishment of the soil water and salt The combined extraction model, water information extraction based on information get salt. Compared with the direct extraction of salt, combined with extraction model can significantly improve the extraction accuracy of soil salinity information. Specifically, in the indoor soil samples, direct estimation of soil salinity, determination coefficient R2 is 0.47 and the relative root mean square error of rRMSE is 0.380; the combined model, accuracy is obviously improved, the water salinity data above 0.63R20.95,0.132rRMSE0.366. remote sensing technology to extract can complement the field sample data, information extraction of soil water and salt and at the regional scale accurately. The relationship between soil surface and soil water salt spatial variability, relationship study on water and salt. But the method of spatial analysis based on can only extract three-dimensional information of soil water and salt, and the water dynamics model can simulate the soil moisture in the four-dimensional time dimension variable including Situation. This method will be affected by the selection of hydrodynamic parameters. This paper presents a global parameter inversion model, by inversion of global optimization factor, correction of hydrodynamic parameter deviation, and control parameters in the allowable range. This paper has established three inversion model, GW5, GW10 and GW30, the number of global optimization the factors were 5,10 and 30. R2=0.23 moisture profile accuracy compared with the forward prediction model, this paper established the GW5 complexity, excellent prediction accuracy of R2 GW10 and GW30 0.313,0.765,0.998. respectively. The inversion model using the Akaike information criterion AICc comprehensive evaluation model and modified model fitting, avoid over fitting, found the most simple structure GW5 model of the highest accuracy (AICc=-467.367), this is mainly because the study area is relatively uniform texture and heterogeneity of hydrodynamic parameters is not obvious. According to the data situation is different, The structure of GW5 model to establish a set of model S based on GW5, and through the field experiment and verification for two years, found that the model group in good practical research area. The only use of remote sensing data to predict water as objective function can be established V (RS) model, V correction field data using the 2013 (RS) model R2=0.892, RMSE=0.01, field data verify the use of the 2014, R2=0.738 RMSE=0.034., the results show that the combination of remote sensing data and S GW5 inversion model, soil moisture data prediction of crop growth stages were significantly higher than the accuracy of the forward model. The combined extraction model of soil water and salt in this paper, can be widely used in quantitative extraction of various substances easily mixed spectrum. Through the combination of remote sensing data and global parameter inversion model, this paper puts forward a method of extracting four regional farmland water salt information, can be widely used in the study The temporal and spatial variation of soil water and salt information in the same environment.

【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：S156.4;S127
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本文編號：1536188