Automatic Surface Water Mapping Using Polarimetric SAR Data for Long-Term Change Detection

Wen Zhang,Glen S. Brown,Baoxin Hu

doi:10.3390/w12030872

Abstract

Mapping the distribution and persistence of surface water in a timely fashion has broad value for tracking dynamic events like flooding, and for monitoring the effects of climate and human activities on natural resource values and biodiversity. Traditionally, surface water is mapped from optical imagery using semi-automatic approaches. However, this process is time-consuming and the accuracy of results can vary among image interpreters. In recent years, Synthetic Aperture Radar (SAR) images have been increasingly used. Microwave signals sensitive to water content make SAR systems useful for mapping surface water, saturated soils, and flooded vegetation. In this study, a fully automatic method based on robust stepwise thresholding was developed to map and track the change in the extent of surface water using Polarimetric SAR data. The application of this method in both Radarsat-2 and Sentinel-1 data in central Ontario, Canada demonstrates that the developed robust stepwise thresholding approach could facilitate rapid mapping of open water areas with a promising accuracy of over 95%. In addition, the time-series extent of surface water extracted from May 2008 to August 2016 reveals the dynamic nature of surface inundation, and the trend was consistent with the local precipitation data.

Highlights

Surface water bodies are the most significant resources on our planet
The result of developed fully automatic stepwise automatic thresholding (SAT) method demonstrates the ability to map the dynamics of surface water with promising accuracy
The whole process is fully automatic, and the default values that have been encoded are found to be efficient in extracting surface water extent for different scenes

Summary

Introduction

Surface water bodies are the most significant resources on our planet. Human activities, climate, and other environmental changes have strong impacts on the locations and sizes of open water bodies, which in turn can affect surrounding environments, biodiversity, and human wellbeing [1,2,3,4].Advanced remote sensing technologies provide an excellent opportunity to obtain spatially extensive and temporally repeated data relevant to mapping surface water [5,6,7,8]. Surface water bodies are the most significant resources on our planet. Surface water is usually extracted based on the difference in the spectral reflectance between land and water. In addition to the direct use of the surface reflectance in multispectral satellite imagery, several indices are commonly employed, including the normalized difference water index (NDWI) that uses the visible wavelength (0.52–0.60 micrometers) and NIR (0.77–0.90 micrometers) and modified normalized difference water index (MNDWI) that uses the visible and Mid-infrared wavelengths (1.55–1.75 micrometers) [9,10]. Flooding usually happens during the heavy rain, which makes it difficult for the optical sensors to obtain data of the earth surface covered by clouds [11]

Methods

Results

Discussion

Conclusion