Automated Extraction of Surface Water Extent from Sentinel-1 Data

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.

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

“…Another approach to map surface waters is using active contour models, which exploit local tone and texture measures to delineate flood extension [5,6]. However, emergent vegetation [7,8] and/or waves [9] increase the amount of backscattered radiation from flooded surfaces to the satellite, making the delineation between water and land more difficult.…”

Active‐Passive Surface Water Classification: A New Method for High‐Resolution Monitoring of Surface Water Dynamics

“…Data from passive sensors (e.g., National Aeronautics and Space Administration (NASA)'s Landsat missions) have been used to create global-and continental-scale maps of surface water bodies (e.g., Carroll et al, 2016;Feng et al, 2016;Haas et al, 2009;Mueller et al, 2016;Pekel et al, 2016;Yamazaki et al, 2015). Synthetic aperture radar (SAR) data from active microwave sensors have been used to generate high temporal and spatial resolution time series of surface water extent (e.g., Huang et al, 2018;Pulvirenti et al, 2011;Schumann et al, 2011;Takeuchi et al, 1999) and wetlands (e.g., Bourgeau-Chavez et al, 2005;Chapman et al, 2015;Rebelo et al, 2012). These remotely sensed data sets provide critical information for Earth science studies.…”

Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands

“…These approaches provide valuable tools for quantifying wetland dynamics at continental scales with important applications such as characterising greenhouse gas flux [17,18,24,25]. However, assessments of wetland extent and dynamics using these approaches tend to be generated at relatively coarse spatial resolutions (e.g., 25-36 km) and have limited applicability for informing decisions at a national or sub-national level, particularly related to more fine-scale environmental challenges such as those related to biodiversity, public health, and flood hazard.In terms of inundation mapping, perhaps the most mature area of research is the use of EO satellite imagery for mapping flood water hazards [2][3][4][5]7,[26][27][28][29][30][31][32]. Radar imagery provides one of the most reliable means of detecting flood water mainly due to the fact that this imagery is: (i) independent of cloud cover, (ii) relatively high resolution (e.g., Sentinel-1: 10 m), (iii) relatively high revisit times (e.g., Sentinel-1: 6-12 days), and (iv) there is a strong signal (low backscatter) over relatively smooth water surfaces.…”

“…By harnessing the GEE cloud-based resources, this approach accurately detects floods by interrogating dense-time series Sentinel-1 radar imagery, with refinement provided by optical Landsat-based surface water frequency occurrence maps (Global Surface Water product [33]). However, like many operational flood monitoring systems [1][2][3][4][5][6][7], this approach is limited to the detection of open water and does not account for vegetated water bodies or emergent flooded vegetation that would be commonplace in a natural wetland landscape [34].L-band radar EO systems are possibly the most promising data for detecting both open water and inundated vegetation [34][35][36]. L band radar imagery not only exhibits distinctive low backscatter signals over relatively smooth open water surfaces, but also at this frequency, L band pulses have the ability to penetrate some vegetation canopies, enabling either a direct estimation of water surface or inference of inundated vegetation resulting from a double-bounce high backscatter mechanism [34][35][36].…”

“…In terms of inundation mapping, perhaps the most mature area of research is the use of EO satellite imagery for mapping flood water hazards [2][3][4][5]7,[26][27][28][29][30][31][32]. Radar imagery provides one of the most reliable means of detecting flood water mainly due to the fact that this imagery is: (i) independent of cloud cover, (ii) relatively high resolution (e.g., Sentinel-1: 10 m), (iii) relatively high revisit times (e.g., Sentinel-1: 6-12 days), and (iv) there is a strong signal (low backscatter) over relatively smooth water surfaces.…”