Accuracy Assessment of Estimated River Water Surface Elevations from Landsat 8 and 9 Imagery among Twenty Water Indices

A method for estimating river water surface elevation (WSE) from Landsat imagery using the river inundation area–water surface elevation (RIA-WSE) rating curve constructed from the U.S. Geological Survey Topobathymetric Elevation Model (TEM) data was developed and tested at six gauging stations along the Upper Mississippi River. Otsu’s automatic threshold selection algorithm was employed for the image classification and estimation of inundation areas within each predefined polygon around each gauging station. In addition to the commonly used green-band-based water indices, Landsat 8 and 9 OLI’s ultra-blue, blue, and red band-based water indices were also tested in this study, which resulted in twenty different water indices: NDWIv (Normalized Difference Water Index), MNDWI1v and MNDWI2v (Modified Normalized Difference Water Index), AWEIsv (Automatic Water Extraction Index with shadows), and AWEInsv (AWEI without shadows), where v represents the visible light band used in the water index. At each station, about 60–80 Landsat 8 or 9 images during 2013–2023 were used to assess the performances of the twenty water indices by comparing the estimated WSEs with the measured WSEs. The results showed that the ultra-blue or red band-based AWEIs yielded the most accurate estimations of WSEs among the twenty tested water indices.

Authors

Institutions

Publication Details

Journal
Remote Sensing
Published
2024-08-20
DOI
https://doi.org/10.3390/rs16163054
Citations
1
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
0.28
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Accuracy Assessment of Estimated River Water Surface Elevations from Landsat 8 and 9 Imagery among Twenty Water Indices

Feifei Pan
1 citations
Remote Sensing
Flood Risk Assessment and Management
0.28
article

Accuracy Assessment of Estimated River Water Surface Elevations from Landsat 8 and 9 Imagery among Twenty Water Indices

Feifei Pan
article en
1 citations

Abstract

A method for estimating river water surface elevation (WSE) from Landsat imagery using the river inundation area–water surface elevation (RIA-WSE) rating curve constructed from the U.S. Geological Survey Topobathymetric Elevation Model (TEM) data was developed and tested at six gauging stations along the Upper Mississippi River. Otsu’s automatic threshold selection algorithm was employed for the image classification and estimation of inundation areas within each predefined polygon around each gauging station. In addition to the commonly used green-band-based water indices, Landsat 8 and 9 OLI’s ultra-blue, blue, and red band-based water indices were also tested in this study, which resulted in twenty different water indices: NDWIv (Normalized Difference Water Index), MNDWI1v and MNDWI2v (Modified Normalized Difference Water Index), AWEIsv (Automatic Water Extraction Index with shadows), and AWEInsv (AWEI without shadows), where v represents the visible light band used in the water index. At each station, about 60–80 Landsat 8 or 9 images during 2013–2023 were used to assess the performances of the twenty water indices by comparing the estimated WSEs with the measured WSEs. The results showed that the ultra-blue or red band-based AWEIs yielded the most accurate estimations of WSEs among the twenty tested water indices.

Remote SensingVol. 16(16)
University of North Texas (US)
Clean water and sanitation
Openalex Percentile: Top 43%
Flood Risk Assessment and Management
0.28
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.