Inland Water Body Fraction Map for Canada and Adjacent Regions at 250-m Spatial Resolution

We present a novel raster dataset of surface inland water body fraction over Canada and neighbouring regions, including the northern parts of the United States, as well as Greenland, Iceland, and the northeastern sector of Russia, at 250-m spatial resolution. It was derived from the Global Surface Water (GSW) dataset (version 5) using a two-step resampling to ensure an accurate replication of the original data and spatial consistency in terms of extent and resolution with the Long-Term Satellite Data Records derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. Additional input data and several coastline vector shape databases were utilized to refine the delineation of waterbodies and land-ocean interface. The resulting dataset is an 8-bit signed integer map, where each pixel represents either the water fraction or a land/ocean mask. Positive values indicate water bodies within Canada, while negative values represent areas outside of Canada. This dataset provides a more precise and up-to-date tool for medium-resolution studies of surface inland water in Canada, aligning closely with satellite imagery of similar spatial resolution. The dataset is freely available through the Government of Canada’s Open Government Portal.

Authors

Institutions

Publication Details

Journal
Canadian Journal of Remote Sensing
Published
2025-02-06
DOI
https://doi.org/10.1080/07038992.2025.2451934
Citations
1
Primary Topic
Hydrology and Watershed Management Studies
Type
article
Field-Weighted Citation Impact
0.82
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Inland Water Body Fraction Map for Canada and Adjacent Regions at 250-m Spatial Resolution

Alexander P. Trishchenko, Calin Ungureanu, Shaheen Ghayourmanesh
1 citations
Canadian Journal of Remote Sensing
Hydrology and Watershed Management Studies
0.82
article

Inland Water Body Fraction Map for Canada and Adjacent Regions at 250-m Spatial Resolution

Alexander P. Trishchenko, Calin Ungureanu, Shaheen Ghayourmanesh
article en
1 citations

Abstract

We present a novel raster dataset of surface inland water body fraction over Canada and neighbouring regions, including the northern parts of the United States, as well as Greenland, Iceland, and the northeastern sector of Russia, at 250-m spatial resolution. It was derived from the Global Surface Water (GSW) dataset (version 5) using a two-step resampling to ensure an accurate replication of the original data and spatial consistency in terms of extent and resolution with the Long-Term Satellite Data Records derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. Additional input data and several coastline vector shape databases were utilized to refine the delineation of waterbodies and land-ocean interface. The resulting dataset is an 8-bit signed integer map, where each pixel represents either the water fraction or a land/ocean mask. Positive values indicate water bodies within Canada, while negative values represent areas outside of Canada. This dataset provides a more precise and up-to-date tool for medium-resolution studies of surface inland water in Canada, aligning closely with satellite imagery of similar spatial resolution. The dataset is freely available through the Government of Canada’s Open Government Portal.

Canadian Journal of Remote SensingVol. 51(1)
Natural Resources Canada (CA)
Clean water and sanitation
Openalex Percentile: Top 29%
Hydrology and Watershed Management Studies
0.82
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.