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Mapping river turbidity at a large basin-scale with a spatially transferable ensemble model using Landsat 8 multispectral imagery.

Authors :
Xu, Min
Liu, Hongxing
Mitchell, David
Lu, Yuehan
Beck, Richard
Cohen, Sagy
Shu, Song
Dimova, Natasha
Source :
International Journal of Remote Sensing. Jul2023, Vol. 44 Issue 14, p4486-4505. 20p.
Publication Year :
2023

Abstract

Mapping river water quality at a basin scale with satellite image data represents a tremendous technical challenge, since traditional empirical remote sensing models calibrated for specific reaches of a river cannot be applied to all the rivers and streams in the basin with an adequate prediction accuracy. In this study, we developed a multi-predictor ensemble model to map and assess water turbidity for all rivers and streams in the Tombigbee River Basin with Landsat 8 multispectral images. Coincident with Landsat 8 satellite overpass on 2 May 2019, we conducted boat-based water quality data collection in the confluence area of Tombigbee River and Black Warrior River near the city of Demopolis, Alabama. Based on the coincident satellite image and field water samples, we calibrated and evaluated the multi-predictor ensemble model, which consists four component empirical models. Our evaluations suggest that the multi-predictor ensemble model can improve turbidity estimation accuracy by 79% in comparison with the best traditional empirical model. More importantly, our multi-predictor ensemble model can be applied to other river and stream reaches across three major sub-basins with a high prediction accuracy. With the multi-predictor ensemble model, we derived a turbidity map for all rivers and streams with a width greater than 90 m in the basin, by processing three Landsat 8 image scenes. This basin-wide turbidity map indicates that the turbidity of rivers and streams exhibits longitudinal gradients and distinct spatial trends within the basin, which were related to the basin land cover/land use, hydrometeorological condition, and the spatial distribution of dams. Owing to its strong spatial transferability and high prediction accuracy, the ensemble model, in conjunction with synoptic multispectral satellite image data, is recommended as an effective and efficient tool for mapping and monitoring river and stream water quality at a large basin or regional scale. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01431161
Volume :
44
Issue :
14
Database :
Academic Search Index
Journal :
International Journal of Remote Sensing
Publication Type :
Academic Journal
Accession number :
169708617
Full Text :
https://doi.org/10.1080/01431161.2023.2237663