Mapping Surface Water from Satellites
Satellites have been capturing multispectral images of the Earth for over five decades—starting with Landsat in 1972. For regions lacking consistent ground-based monitoring, this rich historical dataset is an invaluable resource for water professionals.
Following are background resources and links related to the July 2025 Australian Water School webinar on surface water mapping from satellites.
View the webinar recording here:
The contents of this page focus on available data sources and methodologies for developing time-lapse data from satellite imagery.
Click here for AI and machine learning-aided post-processing of imagery.
Surface Water and Ocean Topography (SWOT)
SWOT Data Cookbook Tutorials:
SWOT Data Access Workshop:
SWOT Hydrology Products Overview:
Get a preview of NASA’s free training resources for monitoring global surface water height from presenter Dr. Matthew Bonnema:
Get a preview of NASA’s free training resources for mapping and monitoring lakes and reservoirs from presenter Dr. Matthew Bonnema:
Online tutorials by Dr. Qiusheng Wu
Case StudY
Mapping surface water from satellites is especially beneficial in data-poor areas of the world.
View a practical case study from Western Australia here:
REferenced Papers
2021 paper by Short, et al.: Two centuries of water-level records at Lake George, NSW:
2015 paper by Mueller, et al.: Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia
2023 paper by Di Ciacca, et al.: Deriving transmission losses in ephemeral rivers using satellite imagery and machine learning:
Data Sources
Geoscience Australia: Water Observations from Space
Open Source Earth Observation at Scale: Open Data Cube
TU Munich’s Database for Hydrological Time Series of Inland Waters (DAHITI) available satellite data:
DAHITI’s Australian station data:
DAHITI’s API V2 documentation
Global Reservoirs and Lakes Monitor: G-REALM
G-REALM Australian Data for Lake Raeside
Machine Learning Resources
lidar Python package: GitHub links
lidar Python package: Real world examples
Mapping wetland hydrological dynamics using Google Earth Engine (GEE) by Dr. Qiusheng Wu: Source code and documentation
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Please feel free to contact us with any queries in the meantime.