台灣降雨時間與空間分布不均，南部地區豐枯水期尤其明顯，確保枯水期用水成為重要課題。在台灣水庫作為僅次於河川引水、地下水抽用的第三大用水來源，水庫在雨季的儲水量是隔年春天是否缺水的關鍵，因此確保水庫水質、水量至關重要。而水庫水質受點源或非點源汙染、人為活動等因素影響，現有有限的水庫水質監測頻率與監測點位難以有效反應水質的時間與空間分布。近年來隨著遙測技術的發展，具備高光譜解析度及高空間解析度的中尺度地球資源衛星逐漸被應用於水質監測，特別是於2013年發射的Landsat-8(簡稱L-8)，與2015年、2017年先後發射的Sentinel-2A與Sentinel-2B(兩者合稱S-2)。L-8與S-2之再訪天數短分別為16天與5天，相較傳統人工船採低頻率的點監測，衛星遙測屬於高頻率的面監測，能更完整地呈現水庫水質的時空分布，且監測成本低。然而利用衛星遙測獲得準確水質為一大考驗，因水質演算是藉由水體光學性質反演算水質，遙測光徑上的干擾、水質演算法於水體的適用性皆會產生誤差。此外，目前用於判斷水庫水質營養狀態的卡爾森指數(Carlson trophic state index, CTSI)中，因總磷(TP)非屬光敏性物質，而無法直接以光學特性反算濃度。因此確保遙測值的準確度與發展一套得應用於衡量遙測水質狀態的方式為本研究目標。
本研究收集2017~2019年間共147幅S-2影像與環保署水質監測資料，以台灣本島18座水庫為研究區域。將自USGS下載之衛星影像依序經由大氣校正、閃爍校正、水質演算後，產生水庫之反射率與水色產品，並依據環保署監測點位座標提取遙測值與日期最接近之實測值建立匹配資料庫。為了建立可靠的匹配資料庫以評估現有演算法應用於台灣水庫的表現，以及發展演算法修正方式。使用穩健迴歸(robust regression)中的Huber regression方式去除匹配資料中離群值，再依據各水質遙測與實測值相關性，決定使用遙測與實測天數差4天以內的資料，最終匹配資料庫包含遙測與實測資料共301對。本研究從文獻中挑選3種濁度(TB)、1種透明度(SD)與4種葉綠素a (Chl-a)演算法，以匹配資料進行演算法適用性分析，整體而言單一紅光波段TB演算法(本文簡稱Nechad 2009)及半解析SD演算法(本文簡稱SASD)對18座水庫都有不錯的適用性，經水庫分類及線性修正後R分別達0.8809與0.7593，RMSE達1.82 FNU與0.68 m。在Chl-a方面，因匹配資料同時具有Case-1及Case-2海洋水色特性，單一演算法無法適用於所有匹配資料，故本研究對Chl-a進行非線性修正，以模糊均值聚類(Fuzzy c-means clustering，FCM)將匹配資料依光學性質分類並決定各類別適用之演算法，依據光學性質相似程度，分別計算演算法權重並混合兩種Chl-a演算法。修正後Chl-a演算法之R為0.4447，RMSE為6.5〖mg/m〗^3，於71張含3個實測點位以上的水庫影像中，約6成的影像有較佳相關性。本研究將18座水庫經線性修正後之Nechad 2009、SASD及經非線性修正後之Chl-a水色產品，中位數整合成枯豐季水質典型分布特徵，並分析其熱區、熱區差異及成因。同時分析3種水質典型分布特徵之空間互動關係，建立水質互動型態分類圖，呈現水庫藻類生長控制因子分布、水質狀態之演變及濁度與營養鹽貢獻來源，使水庫遙測水質資訊更加全面，提供水質管理者更完善的水質資訊。

Due to uneven distribution of rainfall in Taiwan, reservoirs become an important source water. However, reservoir water may be contaminated by point source or non-point source pollution deriving from human activity in reservoir catchment. Therefore, monitoring its water quality is necessary. Nowadays, remote sensing technology has been developed rapidly. Moderate resolution satellites, which have both high spectral and spatial resolution, such as Landsat-8(L-8) and Sentinel-2(S-2) are applied to monitor the whole area of reservoirs with low cost. In this research, we collect S-2 images of 18 reservoirs in Taiwan USGS website and then made ocean color products by atmospheric correction software, ACOLITE. Water quality data were then extracted from ocean color products to construct a preliminary pairing database with Taiwan EPA in-situ monitoring data. The database was then used to determine suitable TB and Chl-a algorithms from 3 and 4 kinds of algorithms selected at first respectively for futher analysis. Also, acceptable remote sensing and in-situ observation day difference(within 4 days), and data outliers(12 pairs) were determine, according to product quality, R^2and Interpretability of robust regression result. Finally, the filtered pairing database contains 301 pairs of data. Through evaluating pearson corelation coefficient(R) of each algorithm, it shows that TB and SD have mid to high R, meanwhile Chl-a has low R because of water body applicability. Therefore, we then develop linear correcting methods(linear regression) for TB and SD algorithms, and non-linear correcting methods(blending algorithm) for Chl-a. Also, a water quality classifying standard was developed to applied on seasonal median water quality images of 18 reservoirs. After applying calssifying standard to median products, we then made classification images of water quality interaction of each reservoir. Distribution of algae growth limiting factors, potential pollution hot spots, appropriate location for water intake etc. can be clearly presented in a single image, providing complete information for decision makers.

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