Monitoring the spatiotemporal dynamics of surface water from remote sensing imagery is essential for understanding water's impact on the global ecosystem and climate change. There is often a tradeoff between the spatial and temporal resolutions of imagery acquired from current satellite sensors and as such various spatiotemporal image fusion methods have been explored to circumvent the challenges this situation presents (e.g., STARFM). However, some challenges persist in mapping surface water at the desired fine spatial and temporal resolution. Principally, the spatiotemporal changes of water bodies are often abrupt and controlled by topographic conditions, which are usually unaddressed in current spatiotemporal image fusion methods. This paper proposes the SpatioTemporal Surface Water Mapping (STSWM) method, which aims to predict Landsat-like, 30 m, surface water maps at an 8-day time step (same as the MODIS 8-day composite product) by integrating topographic information into the analysis. In addition to MODIS imagery acquired on the date of map prediction and a pair of MODIS and Landsat images acquired temporally close to the date of prediction, STSWM also uses the surface water occurrence (SWO, which represents the frequency with which water is present in a pixel) and DEM data to provide, respectively, topographic information below and above the water surface. These data are used to translate the coarse spatial resolution water distribution representation observed by MODIS into a 30 m spatial resolution water distribution map. The STSWM was used to generate an 8-day time series surface water maps of 30 m resolution in six inundation regions globally, and was compared with several other state-of-the-art spatiotemporal methods. The stratified random sampling design was used, and unbiased estimators of the accuracies were provided. The results show that STSWM generated the most accurate surface water map in which the spatial details of surface water were well-represented.

从遥感图像监测地表水的时空动态对于了解水对全球生态系统和气候变化的影响至关重要。从当前卫星传感器获取的图像的空间和时间分辨率之间通常存在权衡，因此已经探索了各种时空图像融合方法来规避这种情况带来的挑战（例如，STARFM）。然而，在以所需的精细空间和时间分辨率绘制地表水地图方面仍然存在一些挑战。主要是水体的时空变化往往是突然的，并且受地形条件的控制，这在当前的时空图像融合方法中通常没有得到解决。本文提出了时空地表水制图（STSWM）方法，旨在预测类陆地卫星，30 m，通过将地形信息集成到分析中，以 8 天时间步长（与 MODIS 8 天复合产品相同）绘制地表水图。除了在地图预测日期获得的 MODIS 影像以及在时间接近预测日期时获得的一对 MODIS 和 Landsat 影像之外，STSWM 还使用地表水发生率（SWO，它表示水存在于一个区域的频率）像素）和 DEM 数据，分别提供水面以下和以上的地形信息。这些数据用于将 MODIS 观测到的粗略空间分辨率水分布表示转换为 30 m 空间分辨率水分布图。STSWM 用于在全球六个淹没区域生成 30 m 分辨率的 8 天时间序列地表水图，并与其他几种最先进的时空方法进行了比较。使用分层随机抽样设计，并提供了准确度的无偏估计。结果表明，STSWM 生成了最准确的地表水图，其中可以很好地表示地表水的空间细节。