海洋與人們生活息息相關，人們從事的許多生產活動皆受此影響包括初級生產力、沿岸動力學、海洋碳循環、有害藻類、水產養殖漁業及娛樂業等等，傳統上大多以船出海採樣或設置浮標的方式來監測水質，但因人力及成本限制，所以無法得到全面性的水質資訊，使用水色衛星影像遙測大範圍水質成為近30年來取得海洋水質資訊的最重要工具。水色衛星監測水質的準確度與感測器特性與水體分類、大氣校正、水質參數、環境條件及使用的演算法有關，目前全世界正在使用或曾有文獻發表的演算法有超過100種以上。本研究以美國NASA之SeaWiFs衛星之生光資料庫SeaBASS (SeaWiFS Bio-optical Archive and Storage System) 之內容，包含1995年至2006年所調查之共804筆資料，每筆資料皆包含AOPs與IOPs，使用SeaWiFs衛星在遙測反射率Rrs波段412、443、490、510、555nm五個波段即AOPs所測得之數值為輸入參數(input)，葉綠素濃度、浮游植物吸收係數aph443、無機性懸浮固體物及溶解有機物吸收係數adg443、CDOM吸收係數ag443作為IOPs當作輸出參數(output)套入遺傳規劃法中建立四項水質反算模式。
本研究建立之模式在使用統計方法R2、RMSE、MSE及MAPE驗證後皆具有高準確度，並在相同條件下與現有NASA之三項模式分別在全部水體，Case-1及Case-2水體進行上述四項驗證方式之比較，經比較過後本研究建立之模式在R2、RMSE、MSE及MAPE均具有較佳之結果。
最後將本研究建立之模式藉由實際衛星影像所匯出之資料選取相同之五個波段套用於本研究之模式，另外繪出衛星影像並與實際之衛星影像相比，在葉綠素濃度部分，台灣近岸周圍表現非常良好，與實際之衛星影像趨勢一致，但在遠洋部分與實際相比則略有高估之趨勢，整體而言在近岸與遠岸之趨勢皆為合理，並可以反映出實際衛星影像。由此便可知本研究建立之水體反算模式不僅在統計上對SeaBASS資料庫而言具有較高準確度之外，還能處理實際影像，可見遺傳規劃法應用於沿岸水體海洋水色反演模式具有代表性。
本研究建立之模式並非以數學式呈現，而是以程式語言C#呈現，除了包含一般數學運算子外另包含程式指令，本文皆有將運算子試驗過程進行詳述，並會將建立之模式放置於網路平台上。

People’s livelihoods are strongly dependent on coastal waters. Many people engage in production activities that involve plant and animal primary productivity and coastal dynamics. These activities include ocean carbon cycle, harmful algae, fisheries and aquaculture, and recreation activities. Because of this, monitoring water quality along coastal waters is a vital task to maintain primary productivity.
Traditional method for monitoring water quality involves both boat sampling and use of a buoy. However, these traditional methods are often exhausting and laborious. Over the past thirty years satellites monitoring ocean color have been more and more frequently used to monitor larger areas. The accuracy of satellites monitoring ocean water quality is based on five categories: ocean type, water quality parameters, algorithms, environmental conditions, and atmospheric correction. This study will only focus on ocean type, water quality parameters, and algorithms.
The objective of this study is to build an inverse model algorithm based on water quality parameters (i.e. optical properties) for case-1 and case-2 waters. The SeaBASS (SeaWiFs Bio-optical Archive and Storage System) database was used to get AOPs and IOPs dating only from 1995 to 2006. AOP data included five reflectance Rrs satellite telemetry SeaWiFs bands: 412, 443, 490, 510, 555 nm. Each band contained 804 data points. IOP data included 4 parameters: chlorophyll concentration; phytoplankton absorption coefficient (aph443); inorganic suspended solids and dissolved organic matter absorption coefficient (adg443); and CDOM absorption coefficient (ag443). These AOP and IOP data collected from SeaBASS were then input into the genetic programming method, which created an inverse model and simulated IOPs as output parameters.
Results from this study showed three major findings. First, absorption coefficients for both aph443 and adg443 were more accurate and showed less error than compared to the model from NASA. Second, in comparison of simulated model IOP results, overall there is a consistent trend that is comparable and can sufficiently represent real satellite imagery. Chl along the Taiwan coast was better simulated with the inverse model than compared to far ocean. Third, the C#-based inverse model developed in this study is more accurate than mathematical-based models, likely because the software allows for program instructions within C#. The program instructions in the model, which have been tested and accounted, will be made publicly available via the internet. Therefore based on these three major findings, my study of model algorithms can help better account for water quality parameters, particularly in coastal waters.

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