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WET Water Resources: A Google Earth Engine Python API Tool to Automate Wetland Extent Mapping Using Radar Satellite Sensors for Wetland Management and MonitoringWetland ecosystems are annually or seasonally wet transition zones between land and water. They provide a range of ecosystem services such as water filtration, flood mitigation, and carbon sequestration, as well as hosting biodiversity hotspots. Although they fulfill fundamental physical and natural processes, wetland extent and health are threatened by anthropogenic influences related to urbanization, population increase, pollution, and climate change. Recognizing the need to quantitatively monitor changes in these recently threatened ecosystems in a timely and cost-effective way, we developed a Google Earth Engine (GEE) Python API tool for automated wetland extent mapping using optical and radar satellite sensors that can be applied globally. The tool will significantly improve wetland change analysis and monitoring as SAR data provides high resolution (5-10 m) imagery, unaffected by cloud cover and light availability (day vs. night), common limitations for other remotely sensed sensors. The tool utilizes Copernicus Sentinel-1 C-band and NISAR L-band (once operational and available on the GEE repository) synthetic aperture radar (SAR) imagery. During image preprocessing, we applied a Terra Moderate Resolution Imaging Spectroradiometer (MODIS) snow product to determine regional snow coverage, which affects land classification sensitivity. Calibration and validation were conducted through a historical change and sensitivity analysis of the Sudd wetland located in central Sudan. The tool was the first of its kind, as it enables NISAR data processing through an open-source GEE repository, further expanding and improving the utility of NASA Earth observations and contributing to NASA Open Science initiatives. We anticipate the tool will be used by researchers and practitioners interested in wetland monitoring and management.
Document ID
20230006078
Acquisition Source
Langley Research Center
Document Type
Other - DEVELOP Technical Report
Authors
Lori Berberian
(Science Systems and Applications (United States) Lanham, Maryland, United States)
Kaely Harris
(Science Systems and Applications (United States) Lanham, Maryland, United States)
Mitch Porter
(Science Systems and Applications (United States) Lanham, Maryland, United States)
Emma Waugh
(Science Systems and Applications (United States) Lanham, Maryland, United States)
Date Acquired
April 19, 2023
Publication Date
March 30, 2023
Subject Category
Earth Resources and Remote Sensing
Funding Number(s)
WBS: 970315.02.02.01.08
CONTRACT_GRANT: NNL16AA05C
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
Professional Review
Keywords
Inundation
Land Cover Classification
Machine Learning
NISAR
Remote Sensing Imagery
SAR
Sentinel-1
Wetland
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