【摘要】：準確、大面積、實時的估算地表土壤濕度是“渤海糧倉”科技示范工程的一項重要內(nèi)容。傳統(tǒng)的土壤濕度獲取是基于地面站點監(jiān)測,精度高、時間連續(xù)性好,但獲取的是點尺度數(shù)據(jù);遙感具有宏觀、動態(tài)、實時、多源等特點,通過反演得到的是像元尺度數(shù)據(jù),精度需要地面實測數(shù)據(jù)進行驗證。為了綜合利用兩者的優(yōu)勢,獲取高精度、空間連續(xù)的土壤濕度數(shù)據(jù),論文以以“渤海糧倉”山東項目區(qū)濱州市無棣縣為研究區(qū),利用地面觀測網(wǎng)數(shù)據(jù)、地面同步實測數(shù)據(jù)和MODIS遙感影像數(shù)據(jù),通過對土壤濕度數(shù)據(jù)插值方法、升尺度方法和遙感反演模型的研究,綜合利用了不同數(shù)據(jù)在土壤濕度提取上的各自優(yōu)勢、取長補短,主要研究工作如下:(1)提出了綜合考慮時間和空間信息的地面觀測土壤濕度數(shù)據(jù)插值方法—BMA-I本文采用協(xié)同克里金法作為空間預(yù)報模型,基于LS-SVM算法的時序遞推預(yù)測方法作為時序預(yù)報模型,基于BMA方法進行模型平均,建立了一種新的插值方法BMAI,并與單純使用普通克里金插值法和協(xié)同克里金插值法進行了對比分析,改善了克里金方法所帶來的平滑效應(yīng),在一定程度上更能反映真實的土壤濕度數(shù)據(jù)。(2)改進表觀熱慣量計算方法,升尺度土壤濕度首先,用地面觀測網(wǎng)的站點實測地表土壤溫度數(shù)據(jù)計算溫差,通過MODIS地表反射率產(chǎn)品計算得到像元尺度ATI,然后,建立BMA-I法插值得到的土壤濕度數(shù)據(jù)與像元尺度ATI之間的函數(shù)關(guān)系,通過貝葉斯線性回歸得到升尺度之后像元尺度的地表土壤濕度。(3)建立了基于改進的溫度-植被干旱指數(shù)模型反演地表土壤濕度的方法利用MODIS遙感影像獲取Ts與NDVI數(shù)據(jù),構(gòu)建特征空間,根據(jù)干濕邊方程計算得到TVDI,建立升尺度獲得的土壤濕度數(shù)據(jù)與TVDI的關(guān)系模型,反演得到區(qū)域地表土壤濕度,經(jīng)驗證,反演結(jié)果明顯好于僅用少量地面實測數(shù)據(jù)建立反演模型。
[Abstract]:Accurate, large area and real time estimation of soil moisture is an important part of the demonstration project of Bohai granary. The traditional soil moisture acquisition is based on ground site monitoring, which has high accuracy and good time continuity, but it acquires point-scale data. Remote sensing has the characteristics of macroscopic, dynamic, real-time, multi-source, etc. By inversion, the pixel scale data are obtained, and the precision needs to be verified by the ground measured data. In order to make comprehensive use of the advantages of the two methods and to obtain soil moisture data with high precision and continuous space, the paper takes Wudi County, Binzhou City, Shandong project area as the research area, and uses the data of ground observation network. Based on the research of soil moisture interpolation method, scaling method and remote sensing inversion model, the ground synchronous measured data and MODIS remote sensing image data are studied. The advantages of different data in soil moisture extraction are synthetically utilized to complement each other. The main research work is as follows: (1) the interpolation method of soil moisture data based on the time and space information is put forward in this paper. In this paper, the cooperative Kriging method is used as the spatial prediction model. The recursive prediction method of time series based on LS-SVM algorithm is used as the prediction model of time series, and a new interpolation method, BMAI, is established based on the BMA method for model averaging. Compared with the common Kriging interpolation method and the cooperative Kriging interpolation method, the smoothing effect of the Kriging method is improved. To some extent, it can better reflect the real soil moisture data. (2) improve the calculation method of apparent thermal inertia. The pixel scale ATI, was calculated by MODIS surface reflectance product. Then the functional relationship between soil moisture data interpolated by BMA-I method and pixel scale ATI was established. Through Bayesian linear regression, the surface soil moisture at pixel scale after scaling is obtained. (3) based on the improved temperature-vegetation drought index model, the method of retrieving the surface soil moisture using MODIS remote sensing image is established to obtain Ts and NDVI data. The characteristic space is constructed and the relationship model between soil moisture data and TVDI obtained by TVDI, is established according to the dry and wet edge equation. The surface soil moisture of the region is retrieved and verified. The inversion results are better than only a small amount of ground measured data to establish the inversion model.
【學(xué)位授予單位】：山東農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：S152.71

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