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Monitoring high spatiotemporal water dynamics by fusing MODIS, Landsat, water occurrence data and DEM
Remote Sensing of Environment  (IF10.164),  Pub Date : 2021-09-03, DOI: 10.1016/j.rse.2021.112680
Xiaodong Li, Feng Ling, Giles M. Foody, Doreen S. Boyd, Lai Jiang, Yihang Zhang, Pu Zhou, Yalan Wang, Rui Chen, Yun Du

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.