利用MODIS與AMSR-E衛星資料推估地表土壤含水量

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姓名

袁培堯(Pei-yao Yuan) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

利用MODIS與AMSR-E衛星資料推估地表土壤含水量
(Using MODIS and AMSR-E Satellite Data to Estimate Land Surface Soil Moisture)

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摘要(中)

土壤含水量在地表水循環中扮演關鍵角色，其含量是用來推估水循環之重要機制，如地表蒸發、植物蒸散以及區域降雨變率等能量交換作用，也影響當地之農業條件、環境保育與氣候變遷等議題。利用遙測技術可以大量而廣泛的獲得地表土壤含水量資訊，以進行長時期的地表含水量監測，目前遙測衛星AMSR-E（Advanced Microwave Scanning Radiometer for EOS）提供的全球土壤含水量產品影像，空間解析度為25公里，是最便捷的土壤含水量資訊，但解析度較差而無法滿足農業規劃或乾旱監測等需求。而使用MODIS（Moderate-resolution Imaging Spectroradiometer）衛星影像產生乾旱指數（Drought Index）以評估土壤溼度的方法已被證實可行，其空間解析度較高，但乾旱指數不具有物理單位，只能得到土壤相對溼度情形。
本研究是以MODIS衛星影像推估中美洲地區2010年與2011年的乾季土壤含水量。使用1公里解析度的MODIS多光譜影像，以近紅外光波段（Near Infrared, NIR）及短波紅外光波段（Short Wave Infrared, SWIR）計算常態化多波段乾旱指數（Normalized Multi-band Drought Index, NMDI），再以統計方法將NMDI指數與AMSR-E土壤含水量產品迴歸分析至1公里解析度，推估具有高空間解析度及物理單位的土壤含水量，以期能增加資料的應用層面與價值。
研究成果顯示，在植被密度較低的區域，NMDI指數與AMSR-E土壤含水量資料間具有明顯的相關性。利用NMDI指數估計土壤含水量時，成果與AMSR-E土壤含水量資料相比，其殘差在空間中具有規律分布，且殘差之RMSE大於AMSR-E資料本身的RMSE平均值，因此本研究利用最小二乘配置法的概念，利用Kriging方法計算局部系統誤差，成果顯示此方法可以有效降低檢核點的土壤含水量估計誤差，將誤差降至AMSR-E資料本身的最小誤差。而高解析度的土壤含水量推估成果與AMSR-E土壤含水量資料的乾溼分布情形非常相似，但仍需其他來源或地面監測站的資料才能進一步分析驗證。

摘要(英)

Soil moisture is an important factor for the exchange of water between the land surface and plant transpiration. It has tremendous effects on agriculture, the environment and climate. It is hard to evaluate long term land surface dryness by field investigation or ground survey. Using remote sensing technology can get soil moisture information extensively. The Advanced Microwave Scanning Radiometer for EOS (AMSR-E) provide global soil moisture product, the spatial resolution is 25km. The spatial resolution is not good enough to satisfy the demand for agricultural planning or drought monitoring.
In the literary, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite image to observe land surface water content is feasible. A land surface drought index called Normalized Multi-Band Drought Index (NMDI) based on two short wave infrared (SWIR) channel in MODIS as the soil moisture sensitive band, is used for estimating land surface soil moisture, and the spatial resolution is up to 1km. The main objective of this study is to estimate soil moisture conditions of the Central American region using MODIS and AMSR-E data in 2010 and 2011 dry season.

關鍵字(中)

★ 土壤含水量
★ MODIS影像
★ AMSR-E
★ NMDI指數

關鍵字(英)

★ NMDI
★ MODIS
★ AMSR-E
★ Soil moisture

論文目次

摘要 i
Abstract iii
誌謝 iv
目錄 v
圖目錄 vii
表目錄 x
第一章緒論 1
1.1 研究動機與目的 1
1.2 論文架構 3
第二章文獻回顧 4
第三章研究區域與資料 10
3.1 研究區域介紹 10
3.2 研究資料介紹 12
3.2.1 MODIS多光譜影像 12
3.2.2 MODIS葉面積指數影像 14
3.2.3 AMSR-E土壤含水量影像 16
第四章研究方法 18
4.1 資料前處理 20
4.1.1 MODIS多光譜影像前處理 20
4.1.2 MODIS葉面積指數影像前處理 23
4.1.3 AMSR-E土壤含水量影像前處理 25
4.2 NMDI指數原理及特性 27
4.2.1 NMDI指數影像 27
4.2.2 NMDI指數對AMSR-E土壤含水量資料敏感度分析 32
4.3 土壤含水量推估模式 38
4.3.1 NMDI指數與AMSR-E土壤含水量迴歸分析 39
4.3.2 以Kriging內插法補償系統誤差 42
4.3.3 模式驗證 44
第五章成果與討論 47
第六章結論與建議 83
6.1 結論 83
6.2 建議 85
參考文獻 87

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指導教授

陳繼藩(Chi-farn Chen)

審核日期

2012-7-30

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