Preprint Article Version 1 This version not peer reviewed

Variance Inflation Factor-Based Forward-Selection Method for Water-Quality Estimation via Combining Landsat TM, ETM+, and OLI/TIRS Images and Ancillary Environmental Data

Version 1 : Received: 16 October 2017 / Approved: 17 October 2017 / Online: 17 October 2017 (03:38:28 CEST)

How to cite: Tu, M.; Smith, P.; Filippi, A. Variance Inflation Factor-Based Forward-Selection Method for Water-Quality Estimation via Combining Landsat TM, ETM+, and OLI/TIRS Images and Ancillary Environmental Data. Preprints 2017, 2017100108 (doi: 10.20944/preprints201710.0108.v1). Tu, M.; Smith, P.; Filippi, A. Variance Inflation Factor-Based Forward-Selection Method for Water-Quality Estimation via Combining Landsat TM, ETM+, and OLI/TIRS Images and Ancillary Environmental Data. Preprints 2017, 2017100108 (doi: 10.20944/preprints201710.0108.v1).

Abstract

A simple approach to enable water-management agencies employing free data to achieve the goal of using a single set of predictive equations for water-quality retrievals with satisfactory accuracy is proposed. Multiple regression-derived equations based on surface reflectance, band ratios, and environmental factors as predictor variables for concentrations of Total Suspended Solids (TSS), Total Nitrogen (TN), and Total Phosphorus (TP) were derived using a hybrid forward-selection method that considers Variance Inflation Factor (VIF) in the forward-selection process. Landsat TM, ETM+, and OLI/TIRS images were jointly utilized with environmental factors, such as wind speed and water surface temperature, to derive the single set of equations. The coefficients of determination of the best-fitting resultant equations varied from 0.62 to 0.79. Among all chosen predictor variables, ratio of reflectance of visible red (Band 3 for Landsat TM and ETM+, or Band 4 for Landsat OLI/TIRS) to visible blue (Band 1 for Landsat TM and ETM+, or Band 2 for Landsat OLI/TIRS) has a strong influence on the predictive power for TSS retrieval. Environmental factors including wind speed, remote sensing-derived water surface temperature, solar altitude, and time difference (in days) between the image acquisition and water sampling were found important in water-quality parameter estimation.

Subject Areas

Variance Inflation Factor; VIF; multiple regression; Landsat; Austin; Lady Bird Lake; water quality; environmental factor; energy flux; urban runoff

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