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系統識別號 U0026-2408202003501600
論文名稱(中文) 利用衛星影像反演臺灣阿里山森林地表土壤含水量
論文名稱(英文) Using Satellite Images for Inverting Surface Soil Moisture over Alishan Forest Area, Taiwan
校院名稱 成功大學
系所名稱(中) 地球科學系
系所名稱(英) Department of Earth Sciences
學年度 108
學期 2
出版年 109
研究生(中文) 肖晶晶
研究生(英文) Jing-Jing Xiao
學號 L46073017
學位類別 碩士
語文別 中文
論文頁數 86頁
口試委員 指導教授-樂鍇.祿璞崚岸
共同指導教授-余騰鐸
口試委員-蔡展榮
口試委員-劉正千
中文關鍵字 Sentinel數據  水雲模型  梯度提升樹  森林地表土壤含水量 
英文關鍵字 Sentinel Data  Water-Cloud Model  Gradient Boosting Decision Tree  Forest Surface Soil Moisture 
學科別分類
中文摘要 摘要
土壤含水量的研究對於農林牧業的發展至關重要,它對水文、氣象、生態等方面都也都有著顯著影響。近年來科技進步,創新不斷,隨著越來越多的衛星發射,遙感探測數據的免費開放與普及,遙感探測反演理論快速發展,利用遙感探測技術反演土壤含水量也成為了一種新興手段。雖然近幾十年來遙感探測技術反演土壤含水量發展日趨成熟,但成果多集中在低矮稀疏植被區,如農用地、草地等區域、以及裸露地表區域,較少研究高大茂密的植被覆蓋區,如森林地表區域。所以對森林地表土壤含水量進行較高精度的遙感探測反演研究十分必要,可以彌補遙感探測反演茂密植被覆蓋區地表土壤含水量這方面的不足。阿里山是臺灣重大林業區,而土壤含水量的情況對其林業發展影響深遠,所以本文選定阿里山部分區域作為森林地表土壤含水量反演的研究區,致力於研究利用遙感探測技術實現精度較高的森林地表土壤含水量反演。
本文對阿里山部分區域進行與下載遙感探測數據同一天的實地採樣地表土壤含水量數據以備於後續遙感探測反演的森林地表土壤含水量精度驗證。本研究下載了此地區Sentinel-1的SAR(Synthetic Aperture Radar)數據和Sentinel-2的光學遙感探測影像數據,通過SNAP(Sentinel Application Platform)軟體對Sentinel-1的SAR數據的一系列處理得到研究區的後向散射係數,而用Sentinel-2光學遙感探測影像數據通過SNAP軟體處理得到各種所需遙感探測指數,然後結合主動微波遙感探測和光學遙感探測通過水雲模型對森林地表土壤含水量進行協同反演,再加入利用ArcGIS軟體採樣為10 m解析度的高程差、坡度、坡向、所獲得的總太陽輻射值、日照時長、通過内插野外實測點地面溫度所得溫度、地面粗糙度、地形起伏度、DEM(Digital Elevation Model)、DSM(Digital Surface Model)以及各遙感探測指數因素包括NDVI(Normalized Difference Vegetation Index)、RVI(Ratio Vegetation Index)、EVI(Enhanced Vegetation Index)、PVI(Perpendicular Vegetation Index)、NDWI(Normalized Difference Water Index)、SAVI(Soil Adjusted Vegetation Index)等利用梯度提升樹模型來得到最終的土壤含水量值。文章主要研究結果為:
(1)通過結合雷達遙感探測影像Sentinel-1 SAR數據和光學遙感探測影像Sentinel-2數據利用水雲模型對研究區森林地表土壤含水量進行協同反演,得到了不同的12種反演土壤含水量的線性模型,證實了水雲模型也可用於本研究區森林地表的土壤含水量反演。
(2)通過對比12種線性模型的反演結果統計以及其五倍交叉驗證所得精度評價指標均方根誤差RMSE,發現模型3反演的土壤含水量的RMSE為4.484%,是捨棄異常模型後誤差最小,精度最高的模型。這說明本文中最適合用來反演研究區土壤含水量的參數是遙感探測指數中的土壤調節植被指數(SAVI)和從Sentinel-1 SAR數據中獲取的vv極化後向散射係數。
(3)遙感探測指數如PVI、EVI、RVI、NDWI、NDVI代入線性模型中則會反演出誤差極大的土壤含水量值,不適合用來進行本文研究區內森林覆蓋地表的土壤含水量反演;而SAVI代入線性模型中,模型則不會擬合出異常值,適合用來進行本文研究區內森林覆蓋地表的土壤含水量反演。
(4)所有12種線性模型在加上高程差、坡度、坡向、所獲得的總太陽輻射值、日照時長、通過内插野外實測點地面溫度所得溫度、地面粗糙度、地形起伏度、DEM、DSM、NDVI、RVI、EVI、PVI、NDWI、SAVI和從Sentinel-1 SAR數據獲得的兩種極化的後向散射係數和入射角,共19種因素進行完梯度提升樹算法計算後,精度都有所提高,證明了加入因素的有效性以及梯度提升樹算法可以用來提升和校正線性模型反演的土壤含水量結果。
(5)本文通過研究得到了解析度為10 m的森林地表土壤含水量反演結果圖,是解析度較高且精度較高的遙感探測土壤含水量反演研究成果。
關鍵字:Sentinel數據;水雲模型;梯度提升樹;森林地表土壤含水量
英文摘要 This research takes part in the Alishan area, which aims to explore the use of Sentinel-1 SAR(Synthetic Aperture Radar) data and Sentinel-2 optical data to retrieve forest surface soil moisture. The primary method used is the water-cloud Model, which is a semi-empirical vegetation backscatter model based on the radiation transmission model proposed by Attema and Ulaby. The second method is the gradient boosting decision tree algorithm proposed by Friedman in 2001. In the first step, I developed 12 linear models for retrieving forest surface soil moisture through the water-cloud model. Next, through adding some other factors that may affect soil moisture, such as elevation difference, slope, aspect, solar radiation, sunshine duration, temperature obtained by interpolation, surface roughness, relief degree of land surface, I used the gradient boosting decision tree algorithm to correct and improve soil moisture estimated by 12 linear models. Finally, we obtained a soil moisture map with high accuracy and a spatial resolution of 10 m in the study area. The result shows that the most suitable parameters for retrieving soil moisture in the study area are the soil-adjusted vegetation index (SAVI) in the remote sensing index and the vv polarization backscatter coefficient obtained from the Sentinel-1 SAR data. And through the gradient boosting decision tree algorithm, the accuracy of all 12 linear models of the RMSE (Root Mean Square Error )value has been improved, indicating the effectiveness of the research method.

Key words: Sentinel Data; Water-Cloud Model; Gradient Boosting Decision Tree; Forest Surface Soil Moisture
論文目次 目錄
摘要 i
Extended Abstract iv
致謝 XV
第1章 緒論 1
1.1研究背景和意義 1
1.2研究現狀 2
1.2.1基於光學遙感探測反演土壤含水量方法 3
1.2.2基於被動微波遙感探測反演土壤含水量方法 7
1.2.3基於主動微波遙感探測反演土壤含水量方法 9
1.2.4基於協同反演的土壤含水量方法 13
1.2.5各種反演土壤含水量方法優缺點總結 15
1.3研究方法與技術路線 16
1.3.1研究內容 16
1.3.2研究方法 17
1.3.3技術路線 18
第2章 研究區概況與數據介紹 20
2.1研究區概況 20
2.2地表觀測數據獲取與處理 21
2.3光學遙感探測影像數據Sentinel-2影像介紹與處理 25
2.4雷達遙感探測影像數據介紹與處理 27
2.5高程數據介紹與處理 33
第3章 土壤含水量反演 44
3.1植被散射模型 44
3.2植被含水量反演模型的建立 46
3.3反演土壤含水量模型的建立 53
3.4反演土壤含水量結果 55
3.5反演土壤含水量結果驗證 67
3.6模型評價分析 72
第4章 結論與展望 74
4.1研究結論 74
4.2創新之處 75
4.3研究限制 76
4.4未來展望 78
參考文獻 80





表目錄
表 1 土壤含水量反演方法優缺點總結 15
表 2 Sentinel-2衛星的各波段特徵介紹 27
表 3 Sentinel-1四種採集模式介紹 29
表4 12種線性模型參數 55
表 5 12種線性模型統計 56
表 6 採樣點誤差範圍統計 61
表 7 線性模型精度驗證結果 68
表 8 線性模型與GBDT的精度驗證結果 70

圖目錄
圖 1 研究技術路線圖 19
圖 2 研究區概況圖 21
圖 3 Spectrum TDR 350儀器 22
圖 4 GARMIN GPSMAP 60CSx 儀器 23
圖 5 研究區採樣點分佈圖 24
圖 6 研究區VV後向散射係數 30
圖 7 研究區VH後向散射係數 31
圖 8 入射角 32
圖 9 數值高程模型 34
圖 10 數值表面模型 35
圖 11 高程差影像 36
圖 12 坡度 37
圖 13 坡向 38
圖 14 太陽輻射值 39
圖 15 日照時長 40
圖 16 地表粗糙度 41
圖 17 地形起伏度 42
圖 18 内插法所得溫度圖 43
圖 19 歸一化植被指數影像 47
圖 20 紅光比植被指數影像 48
圖 21 增強型植被指數影像 49
圖 22 垂直植被指數影像 50
圖 23 歸一化水體指數影像 51
圖 24 土壤調節植被指數影像 52
圖 25 線性模型3反演土壤含水量影像圖 58
圖 26 線性模型3梯度提升樹反演土壤含水量影像圖 60
圖 27 各種因素的土壤含水量誤差範圍顯示圖 66
圖 28 12種線性模型的RMSE-mean值 68
圖 29 12種線性模型的RMSE-std值 69
圖 30 12種線性模型的GBDT_RMSE_mean值 70
圖 31 12種線性模型的GBDT_RMSE_std值 71
圖 32模型精度驗證柱狀圖 71

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