Abstract
In order to produce useful hydrologic and aquatic habitat data from the Landsat system, the U.S. Geological Survey has developed the “Dynamic Surface Water Extent” (DSWE) Landsat Science Product. DSWE will provide long-term, high-temporal resolution data on variations in inundation extent. The model used to generate DSWE is composed of five decision-rule based tests that do not require scene-based training. To allow its general application, required inputs are limited to the Landsat at-surface reflectance product and a digital elevation model. Unlike other Landsat-based water products, DSWE includes pixels that are only partially covered by water to increase inundation dynamics information content. Previously published DSWE model development included one wetland-focused test developed through visual inspection of field-collected Everglades spectra. A comparison of that test’s output against Everglades Depth Estimation Network (EDEN) in situ data confirmed the expectation that omission errors were a prime source of inaccuracy in vegetated environments. Further evaluation exposed a tendency toward commission error in coniferous forests. Improvements to the subpixel level “partial surface water” (PSW) component of DSWE was the focus of this research. Spectral mixture models were created from a variety of laboratory and image-derived endmembers. Based on the mixture modeling, a more “aggressive” PSW rule improved accuracy in herbaceous wetlands and reduced errors of commission elsewhere, while a second “conservative” test provides an alternative when commission errors must be minimized. Replication of the EDEN-based experiments using the revised PSW tests yielded a statistically significant increase in mean overall agreement (4%, p = 0.01, n = 50) and a statistically significant decrease (11%, p = 0.009, n = 50) in mean errors of omission. Because the developed spectral mixture models included image-derived vegetation endmembers and laboratory spectra for soil groups found across the US, simulations suggest where the revised DSWE PSW tests perform as they do in the Everglades and where they may prove problematic. Visual comparison of DSWE outputs with an unusual variety of coincidently collected images for locations spread throughout the US support conclusions drawn from Everglades quantitative analyses and highlight DSWE PSW component strengths and weaknesses.
Highlights
With the release of the Landsat Archive and on-going Landsat/Sentinel data collects at no cost to the user [1], great progress is being made in the development of long-term databases on inundation, including data summarizing the probability of surface water occurrence at continental [2] and global [3] scales
This research focused on the development and evaluation of Landsat-based tests for the detection of inundation when pixels are partially covered by water and vegetation, places where algorithms designed to detect open surface water using Landsat typically perform inadequately [4,5]
In combination, revised Dynamic Surface Water Extent (DSWE) partial surface water” (PSW) tests demonstrated improved overall accuracy with a desired reduction in errors of omission based on quantitative analyses of in situ data from the Everglades, Florida
Summary
With the release of the Landsat Archive and on-going Landsat/Sentinel data collects at no cost to the user [1], great progress is being made in the development of long-term databases on inundation, including data summarizing the probability of surface water occurrence at continental [2] and global [3] scales. These products primarily represent the occurrence of open surface water at Landsat pixel resolution and do not target the detection of inundation when vegetation is present at the subpixel scale [4,5]. The resulting historic and on-going data records will be useful for a wide variety of hydrology, climate, and ecology scientific research, as well as for water and land resource management purposes
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