Monitoring high spatiotemporal water dynamics by fusing MODIS, Landsat, water occurrence data and DEM

Xiaodong Li,Feng Ling,Giles M Foody,Doreen S Boyd,Lai Jiang,Yihang Zhang,Pu Zhou,Yalan Wang,Rui Chen,Yun Du

doi:10.1016/j.rse.2021.112680

Abstract

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.

Highlights

Water is a key land cover type on the Earth’s surface, and its spatiotemporal dynamics have major interactions with environmental systems and processes (Holgerson and Raymond, 2016; Vorosmarty et al, 2000)
The Global Surface Water Mapping (GSW) yearly surface water count map is produced according to the twelve GSW monthly water history maps in that year, and the SpatioTemporal Surface Water Mapping (STSWM) yearly surface water count map is produced according to the forty-six prediction maps in that year
A new STSWM method that uses a pair of LandsatMODIS images products and surface water occurrence (SWO) and digital elevation models (DEM) data was proposed to generate 30 m spatial resolution surface water maps at an 8-day time step

Summary

Introduction

Water is a key land cover type on the Earth’s surface, and its spatiotemporal dynamics have major interactions with environmental systems and processes (Holgerson and Raymond, 2016; Vorosmarty et al, 2000). Remote sensing has a key role in monitoring the spatiotemporal dynamics of surface water at a range of scales Data from systems such as the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) (Dietz et al, 2017), Moderate Resolution Imaging Spectro-radiometer (MODIS) (Pekel et al, 2014), and Landsat sensors (Mueller et al, 2016; Ogilvie et al, 2018; Pekel et al, 2016; Pickens et al, 2020; Tulbure et al, 2016) have been widely used to study surface water. Landsat sensors can provide imagery with low-temporal (16 days) and high-spatial-resolution (30 m) images The latter is suitable for capturing the fine detail of the surface water distribution. Cloud contamination results in series of missing data in the Landsat images, which becomes a big challenge for the Landsat data application in monitoring surface water (Ju and Roy, 2008; Zhu and Helmer, 2018; Zhu and Woodcock, 2014)

Methods

Results

Discussion

Conclusion