土壤含水量在地表蒸發與植物蒸散的水文循環中是一個重要的參數，利用MODIS衛星影像可以推估土壤含水量狀況。本研究的主要目標是使用基於經驗參數化地表溫度(LST)與常態植生指標(NDVI)資料產生的TVDI (temperature-vegetation dryness index)指標推估台灣地區2009年與2010年的地表土壤含水量。 由初期研究顯示台灣地區不同高度造成的氣候差異會對TVDI指標影像造成影響，本研究提出依照高度分區的方式計算TVDI指標來克服此一問題，利用台灣地區地形數值模型分析後得到海拔1,000公尺做為高度分區的門檻值，將台灣分成1,000公尺以下的平地區與1,000公尺以上的山區，並各自產生TVDI指標影像。本研究使用地面測站的每日降雨量資料評估台灣地區TVDI指標與土壤含水量的關連性，比較成果顯示TVDI指標與降雨事件之間具有明顯的關連性。 由於使用MODIS衛星資料產生的TVDI指標影像不具有定量的物理單位，本研究將TVDI指標影像與AMSR-E (Advanced Microwave Scanning Radiometer for Earth observing system)的地表土壤含水量影像進行迴歸分析以找出兩種資料之間的線性轉換方程式。AMSR-E地表土壤含水量影像的數值具有定量單位但空間解析度為25公里，利用1公里空間解析度的TVDI指標與AMSR-E地表土壤含水量的轉換方程式可以把TVDI指標影像轉換成具有定量單位的1公里空間解析度土壤含水量影像，以增加資料的應用價值。迴歸分析成果顯示TVDI指標影像與AMSR-E土壤含水量影像具有一致的成果，這代表利用AMSR-E土壤含水量資料將TVDI指標影像轉換成土壤含水量影像是可行的。根據轉換後土壤含水量影像的分析成果，台灣地區TVDI指標影像的轉換後土壤含水量影像的精確度高，而且分區計算的TVDI指標影像相對於不分區計算的TVDI指標影像的轉換成果具有更高的精確度，這代表在台灣地區使用分區TVDI指標影像轉換的土壤含水量影像能更精確的反映出AMSR-E土壤含水量資料的特性。 Soil moisture is a key factor in controlling the exchange of water between land surface evaporation and plant transpiration. The main objective of this study is to assess soil moisture conditions in Taiwan using Moderate Resolution Imaging Spectroradiometer (MODIS) data in 2009 and 2010. A simple surface dryness index called Temperature-Vegetation Dryness Index (TVDI) based on an empirical parameterization of the relationship between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), is used for estimating land surface soil moisture. From the initial findings obtained from the TVDI analysis of MODIS data, we find that the TVDI was affected by climatic variations due to changes of elevation. The analysis result of digital elevation model shows that 1,000 m is a suitable threshold to separate Taiwan into two parts. Thus, the study area was divided into two regions, lower than 1,000 m and another higher than 1,000 m. The TVDI method was then applied for each region. The daily rainfall data from meteorological stations were used to check the relationship between TVDI and soil moisture, the result shows that there is significant relationship between TVDI and rainfall event. Because TVDI is an index, regression analysis between TVDI results and soil moisture data from AMSR-E (Advanced Microwave Scanning Radiometer for Earth observing system) is performed to find the transformation function between the two data. The transformation function can transform TVDI maps into soil moisture maps with same unit of soil moisture from AMSR-E, and the spatial resolution of soil moisture maps transformed from TVDI is higher than soil moisture maps in AMSR-E. The comparison results between TVDI and AMSR-E show that these two dataset represent similar soil moisture condition in Taiwan, indicating that it is acceptable to use soil moisture maps from AMSR-E to transform TVDI maps into soil moisture maps. According to the analysis result of the transformed soil moisture maps, the soil moisture maps transformed from TVDI maps have high accuracy. Compare the result of soil moisture transformed from TVDI maps with or without separated regions, the soil moisture maps transformed from TVDI maps with separated regions have higher accuracy than the ones without separated regions.
