论文标题
案例2的检索机器学习参数
Retrieval of Case 2 Water Quality Parameters with Machine Learning
论文作者
论文摘要
水质参数是在Case2Extreme数据集（C2X）上应用多种机器学习回归方法的得出的。使用的数据基于Sentinel-3 Olci波带的水面内辐射转移模拟，并且该应用仅用于吸收高浓度有色溶解有机物（CDOR）的水。回归方法是：正则线性，随机森林，内核脊，高斯工艺和支持向量回归器。该验证是使用独立的仿真数据集进行的。也与OLCI神经网络群（ONSS）进行了比较。最好的方法应用于样本场景，并与Eumetsat/esa提供的标准OLCI产品进行了比较
Water quality parameters are derived applying several machine learning regression methods on the Case2eXtreme dataset (C2X). The used data are based on Hydrolight in-water radiative transfer simulations at Sentinel-3 OLCI wavebands, and the application is done exclusively for absorbing waters with high concentrations of coloured dissolved organic matter (CDOM). The regression approaches are: regularized linear, random forest, Kernel ridge, Gaussian process and support vector regressors. The validation is made with and an independent simulation dataset. A comparison with the OLCI Neural Network Swarm (ONSS) is made as well. The best approached is applied to a sample scene and compared with the standard OLCI product delivered by EUMETSAT/ESA
