本文選題：土壤水分反演 + 合成孔徑雷達(dá)��； 參考：《電子科技大學(xué)》2017年博士論文

【摘要】：土壤水分在全球和區(qū)域水文和氣象過程中發(fā)揮著重要作用,被認(rèn)為是地球科學(xué)研究中不可或缺的狀態(tài)變量。特別是在自然環(huán)境惡劣的干旱、半干旱和高寒草原區(qū)域,土壤水分被認(rèn)為是影響草原物候的最重要因素。遙感技術(shù)的快速發(fā)展使得多時(shí)空尺度的土壤水分變化監(jiān)測(cè)成為可能。由于對(duì)土壤水分的高度敏感性及全天時(shí)全天候的觀測(cè)能力,使得微波遙感在裸露地表和植被覆蓋區(qū)域土壤水分反演中應(yīng)用越來越廣泛。然而地表粗糙度和植被的散射貢獻(xiàn)會(huì)降低微波信號(hào)對(duì)土壤水分的敏感性,從而增加土壤水分反演的復(fù)雜度和難度。因此同時(shí)消除地表粗糙度和植被對(duì)土壤水分反演的影響成為構(gòu)建草原地表土壤水分反演方法的關(guān)鍵技術(shù)。本文以青海省烏圖美仁草原、四川省若爾蓋草原和青海省青海湖流域?yàn)檠芯繀^(qū)域,以合成孔徑雷達(dá)(synthetic aperture radar,SAR)數(shù)據(jù)、光學(xué)遙感數(shù)據(jù)和地面實(shí)測(cè)數(shù)據(jù)為重要數(shù)據(jù)源,通過耦合不同的土壤介電常數(shù)模型、地表散射模型和植被散射模型,構(gòu)建適合草原地表的土壤水分反演方法。本論文主要工作概括如下:(1)采用實(shí)測(cè)地表粗糙度參數(shù)初始化地表散射模型,發(fā)展了基于高級(jí)積分方程模型(advanced integral equation model,AIEM)和比值方法的草原地表土壤水分反演方法。該方法將實(shí)測(cè)地表粗糙度參數(shù)作為先驗(yàn)知識(shí)用于模擬裸露地表同極化后向散射系數(shù)與土壤水分之間的經(jīng)驗(yàn)關(guān)系,同時(shí)實(shí)現(xiàn)對(duì)比值方程中未知系數(shù)的求解。構(gòu)建的土壤水分反演方法中,AIEM模型用于模擬裸露地表的后向散射系數(shù),比值方程用于消除植被的散射貢獻(xiàn),其中四個(gè)不同的植被參數(shù)包括葉面積指數(shù)(leaf area index,LAI)、植被含水量(vegetation water content,VWC)、歸一化植被指數(shù)(normalized difference vegetation index,NDVI)和增強(qiáng)植被指數(shù)(enhanced vegetation index,EVI)分別用于植被散射貢獻(xiàn)的參數(shù)化。通過在烏圖美仁草原和青海湖流域的實(shí)驗(yàn)結(jié)果表明,該方法可以用于草原地表的土壤水分反演。同時(shí)研究發(fā)現(xiàn),LAI同其它植被參數(shù)相比更適合于烏圖美仁草原植被的參數(shù)化,而LAI、NDVI和EVI都可以用于表征青海湖流域的植被散射特征。(2)充分利用SAR數(shù)據(jù)的多極化信息消除對(duì)實(shí)測(cè)地表粗糙度參數(shù)的依賴,建立土壤介電常數(shù)與觀測(cè)到的同極化后向散射系數(shù)間的定量函數(shù)關(guān)系,從而實(shí)現(xiàn)對(duì)草原地表的土壤水分反演。該方法解決在實(shí)測(cè)地表粗糙度參數(shù)缺失的情況下如何充分利用多極化SAR數(shù)據(jù)實(shí)現(xiàn)對(duì)草原地表的土壤水分反演。首先通過化簡Dubois模型建立土壤介電常數(shù)與裸露地表同極化后向散射系數(shù)間的函數(shù)關(guān)系,然后分別采用比值方法和水云模型(water cloud model,WCM)實(shí)現(xiàn)對(duì)植被散射貢獻(xiàn)的分離,其中植被的散射機(jī)制分別采用LAI、VWC、NDVI和EVI四個(gè)植被參數(shù)進(jìn)行表征。通過實(shí)驗(yàn)結(jié)果表明,該方法有效地解決了地表粗糙度參數(shù)和植被對(duì)土壤水分反演的影響。從植被參數(shù)化對(duì)土壤水分反演結(jié)果影響發(fā)現(xiàn),在烏圖美仁草原LAI參數(shù)化植被的效果較好,而在若爾蓋草原EVI效果較好。該方法不依賴于任何的地表粗糙度參數(shù),極大地提高了土壤水分反演方法的適用性。(3)基于AIEM模型、比值方法和有效粗糙度參數(shù)構(gòu)建了適合草原地表的土壤水分反演方法。該方法主要是針對(duì)在考慮地表粗糙度參數(shù)的同時(shí)且不依賴于實(shí)測(cè)地表粗糙度參數(shù)的情況下實(shí)現(xiàn)對(duì)草原地表的土壤水分反演。AIEM模型用于模擬裸露地表后向散射系數(shù),其中給定的粗糙度參數(shù)作為模型的輸入?yún)?shù)。比值方法用于將植被的散射貢獻(xiàn)從總后向散射系數(shù)中進(jìn)行分離,其中四個(gè)植被參數(shù)LAI、VWC、NDVI和EVI分別用于表征植被的散射貢獻(xiàn)。通過實(shí)驗(yàn)結(jié)果發(fā)現(xiàn),該方法可以用于草原地表土壤水分反演并且算法精度明顯提高。針對(duì)比值方程中植被的參數(shù)化問題,研究結(jié)果表明LAI適合表征烏圖美仁草原的植被散射,EVI適合描述若爾蓋草原的植被生長狀況,而LAI、NDVI和EVI均可以表征青海湖流域的植被散射貢獻(xiàn)。該方法考慮了地表粗糙度參數(shù)對(duì)后向散射系數(shù)的貢獻(xiàn),但同時(shí)有效粗糙度參數(shù)的加入消除了對(duì)地面實(shí)測(cè)數(shù)據(jù)的依賴,提高了該方法的普適性。(4)基于全極化Radarsat-2數(shù)據(jù)提取的極化特征參數(shù)以及多元線性回歸方程,探索極化特征參數(shù)用于估算草原地表土壤水分的可行性。該方法考慮如何在不消除地表粗糙度參數(shù)和植被散射貢獻(xiàn)的前提下直接采用極化特征參數(shù)實(shí)現(xiàn)對(duì)草原地表的土壤水分估算。本文考慮的極化特征參數(shù)包括Cloude分解參數(shù)極化熵、散射角和反熵,三個(gè)特征值參數(shù),特征值組合參數(shù)包括單次反射特征值相對(duì)差異度、雙次散射特征值相對(duì)差異度和雷達(dá)植被指數(shù),以及Freeman分解參數(shù)表面散射分量、二次散射分量和體散射分量。通過烏圖美仁草原和若爾蓋草原的實(shí)驗(yàn)結(jié)果發(fā)現(xiàn),極化特征參數(shù)可以輔助草原地表的土壤水分反演。針對(duì)草原地表土壤水分反演存在的最大問題是如何同時(shí)消除地表粗糙度參數(shù)和植被對(duì)土壤水分反演的影響。本文提出同時(shí)耦合土壤介電常數(shù)模型、地表散射模型和植被散射模型,構(gòu)建適合草原地表的土壤水分反演方法。這些理論和方法的突破將為草原區(qū)域的土壤水分反演提供新的理論與方法支持。
[Abstract]:Soil moisture plays an important role in the global and regional hydrological and meteorological processes. It is considered to be an indispensable state variable in the research of earth science. Especially in the arid, semi-arid and alpine steppe regions of the natural environment, soil moisture is considered as the most important factor affecting the grassland phenology. The rapid development of remote sensing technology makes it possible. It is possible to monitor soil moisture changes in a space-time scale. Due to the high sensitivity to soil moisture and the all-weather observational ability all day, microwave remote sensing is becoming more and more widely used in the inversion of soil moisture in the exposed and vegetation cover areas. However, the surface roughness and the contribution of vegetation scattering will reduce the microwave signal. The sensitivity of soil moisture can increase the complexity and difficulty of soil moisture inversion, so eliminating the surface roughness and the influence of vegetation on soil moisture inversion is the key technology to build the soil moisture inversion method of the grassland. This paper takes the Qinghai province of uuumeen grassland, Ruoergai grassland in Sichuan province and the Qinghai Lake of Qinghai province. The basin is a study area, with synthetic aperture radar (synthetic aperture radar, SAR) data, optical remote sensing data and ground measured data as important data sources. By coupling different soil dielectric constant model, surface scattering model and vegetation scattering model, the soil moisture inversion method suitable for grass ground surface is constructed. The main work of this paper is the main work of this paper. The following are summarized as follows: (1) the soil surface soil moisture inversion method based on the advanced integral equation model (Advanced integral equation model, AIEM) and the ratio method is developed by using the measured surface roughness parameters. This method uses the measured surface roughness parameters as a priori knowledge to simulate the bare surface homopolar. The empirical relationship between the back scattering coefficient and soil moisture, and the solution of the unknown coefficient in the ratio equation. In the soil moisture inversion method, the AIEM model is used to simulate the backscatter coefficient of the bare surface, and the ratio equation is used to eliminate the scattering contribution of the vegetation, of which four different vegetation parameters include the leaf area. Leaf area index (LAI), vegetation water content (vegetation water content, VWC), normalized vegetation index (normalized difference vegetation index) and enhanced vegetation index for parameterization of the contribution of vegetation scatter respectively. Through the experimental results in the uuumeen grassland and the Qinghai Lake Basin This method can be used to invert soil moisture on the ground surface of grassland. At the same time, it is found that LAI is more suitable to parameterize the vegetation of uuumeen grassland compared with other vegetation parameters, and LAI, NDVI and EVI can be used to characterize the characteristics of vegetation scattering in the Qinghai Lake basin. (2) fully utilize the multi polarization information of SAR data to eliminate the measured surface roughness. Based on the dependence of roughness parameters, the quantitative function relationship between the soil dielectric constant and the observed polarization backscattering coefficient is established to realize the inversion of soil moisture on the ground surface of the grassland. This method solves how to make full use of the multipolar SAR data to realize the soil moisture on the grassland surface under the absence of the measured surface roughness parameters. First, the function relationship between the soil dielectric constant and the scattering coefficient of the bare surface is established by the simplified Dubois model. Then the ratio method and the water cloud model (water cloud model, WCM) are used to separate the contribution of the vegetation scattering, in which the vegetation scattering mechanism uses four vegetation parameters, LAI, VWC, NDVI and EVI, respectively. The experimental results show that the method can effectively solve the effect of surface roughness parameters and vegetation on soil moisture inversion. The effect of parameterized vegetation on LAI vegetation in uuumumeen grassland is better, and the effect of EVI in Ruoergai grassland is better. The applicability of the soil moisture inversion method is greatly improved by any surface roughness parameters. (3) based on the AIEM model, the ratio method and the effective roughness parameter, the soil moisture inversion method suitable for the grassland surface is constructed. This method is mainly aimed at considering the surface roughness parameters and does not depend on the measured surface roughness. In the case of parameters, the.AIEM model of soil moisture inversion on the ground surface is used to simulate the backscatter coefficient of the bare surface, in which the given roughness parameter is used as the input parameter of the model. The ratio method is used to separate the contribution of the vegetation from the total backscatter coefficient, of which four vegetation parameters are LAI, VWC, NDVI and EVI. It is not used to characterize the scattering contribution of vegetation. Through the experimental results, it is found that this method can be used to inverse the soil moisture in the grassland and improve the precision of the algorithm. According to the parameterization of vegetation in the ratio equation, the results show that LAI is suitable for the characterization of the vegetation scattering of uuus meaden, and EVI is suitable for describing the vegetation of Ruoergai grassland. Long condition, and LAI, NDVI and EVI can all represent the contribution of the vegetation scattering in the Qinghai Lake basin. This method considers the contribution of the surface roughness parameters to the backscatter coefficient, but the addition of the effective roughness parameters eliminates the dependence on the ground measured data and improves the universality of the method. (4) based on the full polarization Radarsat-2 data extraction The polarization characteristic parameters and multiple linear regression equations are used to explore the feasibility of the polarization characteristic parameters used to estimate the soil moisture in the grassland. This method considers how to estimate the soil moisture content directly on the grassland surface without eliminating the surface roughness parameters and the contribution of vegetation scattering. The polarization characteristic parameters include the polarization entropy of the Cloude decomposition parameter, the scattering angle and the anti entropy, three eigenvalues, and the combination parameters of the eigenvalues include the relative difference degree of the eigenvalue of the single reflection, the relative difference of the double scattering eigenvalue and the radar vegetation index, the surface scattering component of the Freeman decomposition parameter, the scattering component and the body scattering component. Through the experimental results of the urumumeen grassland and Ruoergai grassland, it is found that the polarization characteristic parameters can assist the inversion of soil moisture in the grassland surface. The biggest problem for soil moisture inversion in the grassland is how to eliminate the influence of surface roughness parameters and vegetation on soil moisture inversion at the same time. The dielectric constant model, the surface scattering model and the vegetation scattering model are used to construct the soil moisture inversion method suitable for the grassland surface. The breakthrough of these theories and methods will provide a new theory and method support for the soil moisture inversion in the grassland area.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：S812.2;TN957.52

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 孔金玲;李菁菁;甄s顂，

本文編號(hào)：1951537