Effective water quality monitoring of coastal areas through the measurement of Chlorophyll-a (Chl-a) has remarkably progressed by ocean color remote sensing. Among different sensors, Moderate Resolution Imaging Spectroradiometer (MODIS) Level 3 products provide reliable global representations of the Chl-a concentration. On the other hand, due to the coarse spatial resolution of MODIS data, its applicability is limited for spatially complex coastal regions. To overcome this limitation, a few downscaling techniques have been suggested based on the polynomial regression method. However, this type of regression has some restrictions, such as sensitivity to outliers, and nonlinear types of machine learning algorithms have not been tested in downscaling Chl-a datasets. Therefore, three machine learning (ML) techniques, support vector regression (SVR), random forest regression (RFR), and long short-term memory (LSTM), were developed using the Sentinel-2A/MSI bands as predictors and MODIS Chl-a as a predictand and compared their results with the results of multiple polynomial regression (MPR), to find the most suitable model for downscaling MODIS Chl-a in coastal area of South Korea. The obtained results showed that the 2nd degree MPR and SVR-Radial Basis Function (RBF) illustrate the best performance in the winter and summer days, respectively. In addition, LSTM is less sensitive to the changes in all variables (sensitivity index range from 0.31 to 0.48). Overall, we conclude that the downscaling approach based on ML models, especially SVR-RBF, can serve as a suitable alternative in some cases to produce high-resolution Chl-a maps, especially for coastal marine water quality monitoring.

中文翻译：

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在韩国黄海西海岸使用机器学习技术对 MODIS 叶绿素-a 进行空间降尺度

通过测量叶绿素a（Chl-a）对沿海地区的水质进行有效监测，海洋颜色遥感取得了显着进展。在不同的传感器中，中分辨率成像光谱仪 (MODIS) 3 级产品可提供 Chl-a 浓度的可靠全球表示。另一方面，由于 MODIS 数据的空间分辨率较粗，其适用性受限于空间复杂的沿海地区。为了克服这个限制，一些基于多项式回归方法的缩减技术被提出。然而，这种类型的回归有一些限制，例如对异常值的敏感性，非线性类型的机器学习算法尚未在缩小 Chl-a 数据集时进行测试。因此，三种机器学习 (ML) 技术，支持向量回归 (SVR)，随机森林回归 (RFR) 和长短期记忆 (LSTM) 是使用 Sentinel-2A/MSI 波段作为预测因子和 MODIS Chl-a 作为预测因子开发的，并将它们的结果与多项式回归 (MPR) 的结果进行比较)，寻找最适合韩国沿海地区 MODIS Chl-a 降尺度的模型。获得的结果表明，二阶 MPR 和 SVR-径向基函数 (RBF) 分别说明了在冬季和夏季的最佳性能。此外，LSTM 对所有变量的变化都不那么敏感（敏感指数范围从 0.31 到 0.48）。总体而言，我们得出的结论是，基于 ML 模型的降尺度方法，尤其是 SVR-RBF，在某些情况下可以作为生成高分辨率 Chl-a 地图的合适替代方案，特别是用于沿海海水质量监测。