Deep learning approach for Sentinel-1 surface water mapping leveraging Google Earth Engine.
Published in MDPI Remote Sensing, 2020
This study leverages the Google Earth Engine to compare two unsupervised histogram-based thresholding surface water mapping algorithms utilizing two distinct pre-processed Sentinel-1 SAR datasets, specifically one with and one without terrain correction. The resulting surface water maps from the four different collections were validated with user-interpreted samples from high-resolution Planet Scope data.
Recommended citation: Markert, Kel N., Amanda M. Markert, Timothy Mayer, Claire Nauman, Arjen Haag, Ate Poortinga, Biplov Bhandari et al. "Comparing sentinel-1 surface water mapping algorithms and radiometric terrain correction processing in southeast asia utilizing google earth engine." Remote Sensing 12, no. 15 (2020): 2469.
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