Optical remote sensing imagery is commonly used to monitor the spatial and temporal distribution patterns of inland waters. Its usage, however, is limited by cloud contamination, which results in low-quality images or missing values. Selecting cloud-free scenes or combining multi-temporal images to produce a cloud-free composite image can partially overcome this problem at the cost of the monitoring frequency. Predicting the spectral values of cloudy areas based on the spectral characteristics is a possible solution; however, this is not appropriate for water because it changes rapidly. Reconstructing cloud-covered water areas using historical water-distribution data has good performance, but such methods are typically only suitable for lakes and reservoirs, not over vast and complex terrain. This paper proposes a category-based approach to reconstruct the water distribution in cloud-contaminated images using a spatiotemporal dependence model. The proposed method predicts the class label (water or land) of a cloudy pixel based on the neighboring pixel labels and those at the same position in images acquired on other dates according to historical spatiotemporal water-distribution data. The method was evaluated through eight experiments in different study regions using Landsat and Sentinel-2 images. The results demonstrated that the proposed method could yield high-quality cloud-free classification maps and provide good water-extraction accuracy and consistency in most hydrological conditions, with an overall accuracy of up to 98%. The accuracy and practicality of the method render it promising for applications across a wide range of future research and monitoring efforts.

光学遥感影像通常用于监测内陆水域的时空分布格局。然而，它的使用受到云污染的限制，这会导致低质量的图像或缺失值。选择无云场景或组合多时相图像来生成无云合成图像可以部分克服这个问题，但代价是监控频率。根据光谱特征预测多云区域的光谱值是一种可能的解决方案；然而，这不适用于水，因为它变化很快。使用历史水分布数据重建云覆盖水域具有良好的性能，但这种方法通常只适用于湖泊和水库，不适用于广阔而复杂的地形。本文提出了一种基于类别的方法，使用时空依赖模型重建受云污染的图像中的水分布。所提出的方法根据相邻的像素标签和根据历史时空水分布数据在其他日期获取的图像中相同位置的像素标签预测多云像素的类别标签（水或土地）。使用 Landsat 和 Sentinel-2 图像，通过在不同研究区域的八项实验对该方法进行了评估。结果表明，所提出的方法可以生成高质量的无云分类图，并在大多数水文条件下提供良好的取水精度和一致性，总体精度高达 98%。