【摘要】：寧夏回族自治區(qū)地處我國西部黃河上游,屬典型的大陸性半濕潤半干旱氣候。我區(qū)的特色經(jīng)濟(jì)作物產(chǎn)業(yè),是寧夏地區(qū)最有發(fā)展?jié)摿Φ霓r(nóng)業(yè)增收項目之一,而土壤水肥的精確補(bǔ)給直接影響作物高產(chǎn)、優(yōu)質(zhì)。因此,如何采取廉價、快速、省力的手段來獲取干旱區(qū)和半干旱區(qū)地域范圍內(nèi)大面積鹽漬化土壤鹽水分的動態(tài)信息,對鹽漬化土壤的治理、合理規(guī)劃利用具有重要意義。本研究以溫室番茄植株為研究對象,基于Vis-NIR與NIR高光譜成像技術(shù),結(jié)合化學(xué)計量學(xué)方法,對土壤水分、鹽分以及番茄植株水分動態(tài)監(jiān)測,為快速診斷植株水分虧缺程度以及土壤水鹽分檢測機(jī)理研究提供理論依據(jù)。主要研究結(jié)果包括:(1)不同灌水條件下,土柱中的土壤含水率變化情況不同。對不同含鹽量土壤來說,土壤中鹽分的含量對土壤水分的再分布具有較大的影響;相比2%濃度含鹽量灌溉的土柱,0.2%濃度含鹽量灌溉的土柱可以更好的控制水分在土壤中的運(yùn)移。對土壤的不同含水率、含鹽量變化規(guī)律進(jìn)行了分析,建立了4種表層土壤與深層土壤水分的數(shù)學(xué)模型。(2) 土壤的反射率隨著土壤含水率的增加而減小,當(dāng)增大到超過田間持水率時,土壤的反射率會隨著土壤含水率的增加而增大。并探討了土壤水分的不同方法提取特征波長、不同建模方法、不同光譜范圍及特征波長與全波段的建模效果,優(yōu)選900～1700nm波段建立的SPA方法提取特征波長的MLR模型,篩選出特征波長為987、1386、1466、1568、1636、1645 nm, 土壤含水率最優(yōu)模型的預(yù)測相關(guān)系數(shù)(Rp)為0.984,預(yù)測均方根誤差(RMSEP)為0.631。(3)隨著土壤中含鹽量的增加,土壤中水分蒸發(fā)情況受到的影響不同;不同天數(shù)下,不同波段下體現(xiàn)了隨著土壤含鹽量的增加,土壤光譜的反射率增大;而對于高含鹽量土壤,土壤反射率變化差異較小。這為今后智能遙感定性判別土壤含鹽量提供理論依據(jù)。(4)探討了土壤鹽分的不同方法提取特征波長、不同建模方法、不同光譜范圍及特征波長與全波段的建模效果,優(yōu)選900～1700nm波段的β系數(shù)方法提取特征波長的PLSR模型,篩選出特征波長為 936、996、1016、1136、1151、1186、1273、1395、1425、1458、1535、1642nm,土壤含鹽量的預(yù)測相關(guān)系數(shù)(Rp)為0.949,預(yù)測均方根誤差(RMSEP)為2.914g/kg。(5)研究了番茄葉片的光譜信息與水分含量直接的關(guān)系以及鹽-水耦合的生物控制機(jī)制。探討了番茄葉片的不同方法提取特征波長、不同建模方法、不同光譜范圍及特征波長與全波段的建模效果,優(yōu)選900～1700nm波段的SPA提取特征波長的PLSR模型,篩選出特征波長為918、981、1029、1387、1652nm,葉片的含水率的預(yù)測相關(guān)系數(shù)(Rp)為0.9,預(yù)測均方根誤差(RMSEP)為 0.614。(6)利用高光譜成像技術(shù)對土壤的水分、鹽分以及溫室番茄植株的水分進(jìn)行模型構(gòu)建,將深層土壤、表層土壤、番茄冠層與高光譜建立聯(lián)系,為寧夏區(qū)域土壤水鹽含量遙感與植物葉片水分快速檢測奠定基礎(chǔ)。
[Abstract]:Ningxia Hui Autonomous region is located in the upper reaches of the Yellow River in western China, which is a typical continental semi-humid and semi-arid climate. The characteristic cash crop industry in our region is one of the most potential agricultural income increasing projects in Ningxia, and the accurate supply of soil water and fertilizer directly affects the high yield and high quality of crops. Therefore, how to use cheap, rapid and labor-saving means to obtain the dynamic information of salinized soil salt water distribution in arid and semi-arid areas is of great significance to the treatment of salinized soil and rational planning and utilization of salinized soil. Based on Vis-NIR and NIR hyperspectral imaging technique and chemometrics method, the dynamic monitoring of soil moisture, salt content and tomato water content in greenhouse tomato plants was studied. It provides a theoretical basis for the rapid diagnosis of water deficit in plants and the study on the mechanism of soil water salinity detection. The main results are as follows: (1) the variation of soil moisture content in soil column is different under different irrigation conditions. For different salinity soil, the salt content in the soil has a greater impact on the redistribution of soil moisture, compared with the soil column with 2% salinity irrigation, the soil column with 0.2% salt concentration irrigation can better control the movement of water in the soil. The variation law of soil moisture content and salt content was analyzed, and four mathematical models of surface soil and deep soil moisture were established. (2) soil reflectivity decreased with the increase of soil moisture content. The soil reflectivity increases with the increase of soil moisture content when the field water holdup is increased. Different methods of extracting characteristic wavelength, different modeling methods, different spectral range, characteristic wavelength and the modeling effect of the whole wave band were discussed. The MLR model of extracting characteristic wavelength by SPA method in 900~1700nm band was selected. The predicted correlation coefficient (Rp) and root mean square error (RMSEP) of the optimal model of soil moisture content of 987 ~ 1386N 146N 1568336 ~ 1645 nm, were 0.984 and 0.631respectively. (3) with the increase of salt content in the soil, the evaporation of soil water was affected by different days, and the correlation coefficient was 0.984, and the root mean square error (RMSEP) was 0.631. (3) with the increase of salt content in the soil, the evaporation of soil water was affected by different days. The spectral reflectance of soil increased with the increase of soil salt content in different bands, but the variation of soil reflectivity was small for high salinity soil. This provides a theoretical basis for intelligent remote sensing to qualitatively judge soil salinity. (4) the modeling effects of different methods for extracting soil salinity, different modeling methods, different spectral ranges, characteristic wavelengths and full wavelengths are discussed. The PLSR model of characteristic wavelength was extracted by 尾 -coefficient method in 900~1700nm band. The characteristic wavelength was 936 / 9961016 / 11363 / 1151 / 11866 / 12773 / 1395N / 1425 / 1455 / 1535N / 1642nm, the predicted correlation coefficient of soil salt content was 0.949 and the (RMSEP) of predicting root mean square error was 2.914g / kg / g. (5) the direct relationship between spectral information and water content in tomato leaves and the biological control mechanism of salt-water coupling were studied. Different methods of extracting characteristic wavelengths, different modeling methods, different spectral ranges, characteristic wavelengths and full-band modeling effects of tomato leaves were discussed. The PLSR model of extracting characteristic wavelengths of SPA in 900~1700nm band was selected. The characteristic wavelength was 918 ~ 981 ~ 1029 ~ 13877N ~ (1652) nm, the predicted correlation coefficient of water content in leaves was 0.9and the root mean square error (RMSEP) of prediction was 0.614. (6) the model of soil moisture, salt content and tomato water in greenhouse was constructed by hyperspectral imaging technique. The deep soil, surface soil and tomato canopy were linked with hyperspectral data, which laid a foundation for remote sensing of soil water and salt content in Ningxia region and rapid detection of water content in plant leaves.
【學(xué)位授予單位】：寧夏大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：S156.4;S641.2

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 司海青;姚艷敏;王德營;劉影;;含水率對土壤有機(jī)質(zhì)含量高光譜估算的影響[J];農(nóng)業(yè)工程學(xué)報;2015年09期

2 陳皓銳;王少麗;管孝艷;高黎輝;;基于高光譜數(shù)據(jù)的土壤電導(dǎo)率估算模型——以河套灌區(qū)沙壕渠灌域沙壤土為例[J];干旱區(qū)資源與環(huán)境;2014年12期

3 史舟;梁宗正;楊媛媛;郭燕;;農(nóng)業(yè)遙感研究現(xiàn)狀與展望[J];農(nóng)業(yè)機(jī)械學(xué)報;2015年02期

4 王乾龍;李碩;盧艷麗;彭杰;史舟;周煉清;;基于大樣本土壤光譜數(shù)據(jù)庫的氮含量反演[J];光學(xué)學(xué)報;2014年09期

5 徐yN凡;施勇;李云梅;;基于環(huán)境一號衛(wèi)星高光譜數(shù)據(jù)的太湖富營養(yǎng)化遙感評價模型[J];長江流域資源與環(huán)境;2014年08期

6 彭杰;遲春明;向紅英;滕洪芬;史舟;;基于連續(xù)統(tǒng)去除法的土壤鹽分含量反演研究[J];土壤學(xué)報;2014年03期

7 趙少華;張峰;王橋;姚云軍;王中挺;游代安;;高光譜遙感技術(shù)在國家環(huán)保領(lǐng)域中的應(yīng)用[J];光譜學(xué)與光譜分析;2013年12期

8 祖皮艷木·買買提;海米提·依米提;呂云海;;于田綠洲典型區(qū)土壤鹽分及鹽漬土的空間分布格局[J];土壤通報;2013年06期

9 吳見;劉民士;李偉濤;;基于高光譜影像分解的土壤含水量反演技術(shù)[J];水土保持通報;2013年05期

10 劉婭;潘賢章;王昌昆;李燕麗;石榮杰;周睿;解憲麗;;土壤濕潤條件下基于光譜對稱度的鹽漬土鹽分含量預(yù)測[J];光譜學(xué)與光譜分析;2013年10期

相關(guān)博士學(xué)位論文 前6條

1 邱讓建;溫室環(huán)境下土壤—植物系統(tǒng)水熱動態(tài)與模擬[D];中國農(nóng)業(yè)大學(xué);2014年

2 陳禎;基于近紅外光譜分析的土壤水分信息的提取與處理[D];華中科技大學(xué);2010年

3 張婷婷;基于PLS模型的農(nóng)業(yè)土壤成分高光譜遙感反演研究[D];吉林大學(xué);2010年

4 譚琨;基于支持向量機(jī)的高光譜遙感影像分類研究[D];中國礦業(yè)大學(xué);2010年

5 郭全恩;土壤鹽分離子遷移及其分異規(guī)律對環(huán)境因素的響應(yīng)機(jī)制[D];西北農(nóng)林科技大學(xué);2010年

6 馬本學(xué);基于圖像處理和光譜分析技術(shù)的水果品質(zhì)快速無損檢測方法研究[D];浙江大學(xué);2009年

相關(guān)碩士學(xué)位論文 前6條

1 張婷華;土壤水分脅迫對溫室番茄蒸騰的影響及模擬研究[D];南京信息工程大學(xué);2014年

2 王麗娜;基于高光譜技術(shù)的黃河三角洲鹽堿土水鹽含量估測研究[D];山東農(nóng)業(yè)大學(xué);2013年

3 汪泊錦;基于高光譜散射圖像的蘋果粉質(zhì)化特征提取與分類[D];江南大學(xué);2012年

4 高鳳菊;鹽度對不同類型甜高粱品種萌發(fā)、生長發(fā)育及產(chǎn)量的影響[D];山東農(nóng)業(yè)大學(xué);2011年

5 魏娜;土壤含水量高光譜遙感監(jiān)測方法研究[D];中國農(nóng)業(yè)科學(xué)院;2009年

6 吳進(jìn);精準(zhǔn)農(nóng)業(yè)模式研究[D];華中師范大學(xué);2007年

，

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