水體遙感反射率的擷取值是使用衛星遙感技術進行水質監測的一個基本且重要的步驟。大氣影響通常顯著而複雜，使得大氣校正（ACs）難以準確得出地表反射率。輻射傳輸模式是大氣校正很有前途的方法之一。然而，該方法需要使用複雜的模型計算包含氣溶膠模型、大氣條件和感測器的幾何資訊等參數，因此相當耗費時間，有時甚至會因鄰近效應、雲的陰影和氣溶膠的過度校正產生負的水體遙感反射率。因此本研究提出基於卷積和全連接神經網路的大氣校正網路—AC-Net，利用Landsat 8 OLI影像擷取熱帶地區內陸水體的遙感反射率。為了訓練AC-Net，神經網路選擇採用現有大氣校正模型所生成的遙感反射率作為訓練資料。現有的大氣校正模型當中，iCOR因考量目標區域周圍物體造成的相鄰效應，為有前途的輻射傳輸模型，故本研究所提出的模型將透過iCOR產生AC-Net模型所需的訓練資料，使AC-Net可直接透過神經網路，將頂層大氣反射率、幾何角度和氣溶膠光學厚度等輸入資料直接輸出成遙感反射率。AC-Net亦使用現地資料進行驗證並測試其他湖泊的數據，評估模型的可行性與有效性。實驗中AC-Net與其他常用模型進行比較，包括暗體辨識法（DOS）、快速大氣校正（QUAC）、ACOLITE大氣校正、嚴謹大氣校正（FLAASH）、LaSRC演算法和iCOR，並透過均方根誤差（RMSE）、偏差（BIAS）和平均絕對百分比誤差（MAPE）評估大氣校正模型的性能。結果顯示AC-Net的RMSE為0.004，偏差為0.0005，MAPE為4.19，性能表現優於其他常用模型。此外AC-Net能避免產生負的遙感反射率，並且適用於熱帶地區的內陸水體。

關鍵字：大氣校正、遙感反射率、類神經網路、Landsat 8 OLI影像

The retrieval of water remote-sensing reflectance is an important and fundamental step for water quality monitoring using satellite remote sensing techniques. The atmospheric effects are generally significant and complex, which make atmospheric corrections (ACs) difficult to accurately derive the remote-sensing reflectance. The radiative transfer model is considered as a promising method in atmospheric correction. Nevertheless, the approach requires calculating a set of parameters using complicated models and formulas, including aerosol model, atmospheric conditions, and sensor geometric information. This leads to time-consuming and sometimes produces negative water remote sensing reflectance because of the overcorrection for adjacency effects, cloud shadows, and aerosol. With a revisit cycle of 16-days, free available, and a high resolution of 30 meters, Landsat 8 OLI imagery is widely utilized for water quality monitoring in inland waters. In this study, an atmospheric correction network, called AC-Net, based on convolutional and fully-connected neural networks is proposed to retrieve remote-sensing reflectance of inland waters in tropical regions by using Landsat 8 imagery. To train AC-Net, the remote-sensing reflectance generated by an existing AC model is used as the simulated training label data. The image CORrection for atmospheric effects named iCOR algorithm was assessed to be a promising radiative transfer model due to counting for the adjacency effects caused by objects surrounding the target area. This algorithm was used to generated the training label of remote-sensing reflectance. After the model training, the remote-sensing reflectance can be derived by AC-Net with the inputs of top of atmosphere reflectance, geometric angles, and aerosol optical thickness. The proposed model was validated with in-situ remote-sensing reflectance measured in the field campaigns, and the validated model was further tested by using data in other lakes for feasibile and effective assessments. The proposed model was compared with AC models, including dark object subtraction (DOS), quick atmospheric correction (QUAC), atmospheric correction for OLI lite (ACOLITE), fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH), Landsat 8 surface reflectance code (LaSRC), and Image correction for atmospheric effects (iCOR) by using in-situ measurements. The performances of AC models were evaluated using root means square error (RMSE), bias, and mean absolute percentage error (MAPE). The results demonstated that AC-Net with the performance of RMSE=0.004, Bias=0.0005, and MAPE=4.19 outperforms the compared AC models. In addition, the testing results reveal that the AC-Net can avoid producing negative remote-sensing reflectance, and the AC-Net is effective for inland waters in tropical regions.
Keywords: Atmospheric correction, remote sensing reflectance, artificial neural network, Landsat 8 OLI imagery

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