Automated Extraction of Surface Water Extent from Sentinel-1 Data

Wenli Huang,John Jones,Ben Devries,Irena Creed,Megan Lang,Mark Carroll,Chengquan Huang

doi:10.3390/rs10050797

Abstract

Accurately quantifying surface water extent in wetlands is critical to understanding their role in ecosystem processes. However, current regional- to global-scale surface water products lack the spatial or temporal resolution necessary to characterize heterogeneous or variable wetlands. Here, we proposed a fully automatic classification tree approach to classify surface water extent using Sentinel-1 synthetic aperture radar (SAR) data and training datasets derived from prior class masks. Prior classes of water and non-water were generated from the Shuttle Radar Topography Mission (SRTM) water body dataset (SWBD) or composited dynamic surface water extent (cDSWE) class probabilities. Classification maps of water and non-water were derived over two distinct wetlandscapes: the Delmarva Peninsula and the Prairie Pothole Region. Overall classification accuracy ranged from 79% to 93% when compared to high-resolution images in the Prairie Pothole Region site. Using cDSWE class probabilities reduced omission errors among water bodies by 10% and commission errors among non-water class by 4% when compared with results generated by using the SWBD water mask. These findings indicate that including prior water masks that reflect the dynamics in surface water extent (i.e., cDSWE) is important for the accurate mapping of water bodies using SAR data.

Highlights

Wetlands are among the world’s most productive and ecologically diverse ecosystems; yet, they are being lost at alarming rates [1,2]
Products derived from optical sensors such as Landsat [11] and Sentinel-2 [12] normally use a variety of spectral bands ranging from visible to the shortwave infrared (SWIR) regions of the electromagnetic spectrum
The general patterns were similar over major water bodies, more spatial details were preserved in the composited dynamic surface water extent (cDSWE) product

Summary

Introduction

Wetlands are among the world’s most productive and ecologically diverse ecosystems; yet, they are being lost at alarming rates [1,2]. Quantifying the spatial and temporal dynamics of surface water in wetlands is critical to understanding ecosystem processes, including land-atmosphere energy balance [3], carbon and nutrient cycles [4,5], wetland modeling [6] and surface-groundwater dynamics [7,8] Despite this critical need, most regional- to global-scale surface water extent products do not adequately characterize spatially complex or temporally dynamic wetlands due to their limited spectral, spatial or temporal resolutions [9,10]. Products derived from optical sensors such as Landsat [11] and Sentinel-2 [12] normally use a variety of spectral bands ranging from visible to the shortwave infrared (SWIR) regions of the electromagnetic spectrum Active sensors, such as synthetic aperture radars (SAR), have advantages over optical sensors to quantify the spatial and temporal variation of surface water extent [13]. Numerous operational mapping algorithms rely on SAR backscatter from single-polarized (e.g., HH, HV, or VV), dual-polarized (HH/HV or VV/VH) [28,29,30], or quad-polarized (HH/HV/VV/VH) data [23,31], where the first and second letter denote transmit and receive polarizations

Objectives

Methods

Results

Conclusion