Remote sensing techniques have been increasingly utilised in aquatic applications in recent years. A common challenge in using optical satellite data is the presence of missing observations due to cloud cover. These data gaps can lead to missed detection of critical events, such as algal blooms, in lakes of high interest to water authorities. As a result, enhancing the completeness of optical satellite datasets is crucial for improving the monitoring and prediction of algal blooms. In this study, we compared a traditional data imputation method (i.e., linear interpolation) with deep learning models for reconstructing missing spectral bands across four lakes with historical records of algal blooms. The deep learning models adopted include CNN-based architectures (i.e., CNN, Inception Resnet, and Autoencoder) and CNN-LSTM-based architectures (i.e., CNN-LSTM, Resnet-LSTM, and Autoencoder-LSTM). Our results demonstrated that deep learning models substantially outperformed the baseline linear interpolation method in imputing spectral band values within artificially masked regions. Among these models, CNN delivered the best performance across most lakes. Furthermore, we evaluated the performance of algal bloom indices (i.e., Green/Red and NDCI) derived from the imputed imagery by comparing them with the observed data. Our results demonstrate that deep learning models are effective for imputing missing data in PlanetScope SuperDove imagery, enabling more reliable applications in water monitoring.

翻译：近年来，遥感技术在水体应用领域的应用日益广泛。使用光学卫星数据的一个常见挑战是云层覆盖导致的观测数据缺失。这些数据空缺可能导致水管理部门高度关注的湖泊中关键事件（如藻华）的漏检。因此，提高光学卫星数据集的完整性对于改进藻华监测与预测至关重要。本研究比较了传统数据填补方法（即线性插值）与深度学习模型在四个具有藻华历史记录湖泊中的缺失光谱波段重建效果。所采用的深度学习模型包括基于CNN的架构（即CNN、Inception Resnet和Autoencoder）以及基于CNN-LSTM的架构（即CNN-LSTM、Resnet-LSTM和Autoencoder-LSTM）。结果表明，在人工掩膜区域内的光谱波段值填补任务中，深度学习模型显著优于基线线性插值方法。其中，CNN模型在大多数湖泊中表现最佳。此外，我们通过将填补影像衍生的藻华指数（即绿/红比和NDCI）与观测数据进行对比，评估了其性能。研究结果表明，深度学习模型能有效填补PlanetScope SuperDove影像的缺失数据，从而为水体监测提供更可靠的应用支持。