A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

TLDR

Accurate, high‑resolution mapping of surface water is essential for terrestrial ecosystem science and management, yet seasonal snow, ice, clouds, and acquisition timing complicate coverage. We generated a global 30‑m inland water map by applying an automated Landsat‑based algorithm that integrates surface reflectance, multispectral water and vegetation indices, terrain metrics, and coarse‑resolution water masks. The resulting 30‑m dataset records 3.65 million km² of inland water—about three‑quarters in North America and Asia—shows that boreal forests hold the largest share, achieves commission errors below 4 % and omission errors below 14 % versus MODIS and national land‑cover products, and is freely available from the GLCF website.

Abstract

The science and management of terrestrial ecosystems require accurate, high-resolution mapping of surface water. We produced a global, 30-m-resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates, multispectral water and vegetation indices, terrain metrics, and prior coarse-resolution water masks. The dataset identified 3,650,723 km2 of inland water globally – nearly three quarters of which was located in North America (40.65%) and Asia (32.77%), followed by Europe (9.64%), Africa (8.47%), South America (6.91%), and Oceania (1.57%). Boreal forests contained the largest portion of terrestrial surface water (25.03% of the global total), followed by the nominal 'inland water' biome (16.36%), tundra (15.67%), and temperate broadleaf and mixed forests (13.91%). Agreement with respect to the Moderate-resolution Imaging Spectroradiometer water mask and Landsat-based national land-cover datasets was very high, with commission errors <4% and omission errors <14% relative to each. Most of these were accounted for in the seasonality of water cover, snow and ice, and clouds – effects which were compounded by differences in image acquisition date relative to reference datasets. The Global Land Cover Facility (GLCF) inland surface water dataset is available for open access at the GLCF website (http://www.landcover.org).