Surface Water Quality Assessment through Remote Sensing Based on the Box–Cox Transformation and Linear Regression

A methodology to estimate surface water quality using remote sensing is presented based on Landsat satellite imagery and in situ measurements taken every six months at four separate sampling locations in a tropical reservoir from 2015 to 2019. The remote sensing methodology uses the Box–Cox transfor...

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Bibliographic Details
Published inWater (Basel) Vol. 15; no. 14; p. 2606
Main Authors Loaiza, Juan G., Rangel-Peraza, Jesús Gabriel, Monjardín-Armenta, Sergio Alberto, Bustos-Terrones, Yaneth A., Bandala, Erick R., Sanhouse-García, Antonio J., Rentería-Guevara, Sergio A.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2023
Subjects
Analysis
Aquatic resources
Box–Cox optimization
Chlorophyll
Landsat imagery
linear modeling
Mathematical models
Metadata
Mexico
Radiation
Regression analysis
Remote sensing
Sensors
Software
Surface water
surface water quality
Water quality
Mexico
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Summary:A methodology to estimate surface water quality using remote sensing is presented based on Landsat satellite imagery and in situ measurements taken every six months at four separate sampling locations in a tropical reservoir from 2015 to 2019. The remote sensing methodology uses the Box–Cox transformation model to normalize data on three water quality parameters: total organic carbon (TOC), total dissolved solids (TDS), and chlorophyll a (Chl-a). After the Box–Cox transformation, a mathematical model was generated for every parameter using multiple linear regression to correlate normalized data and spectral reflectance from Landsat 8 imagery. Then, significant testing was conducted to discard spectral bands that did not show a statistically significant response (α = 0.05) from the different water quality models. The r2 values achieved for TOC, TDS, and Chl-a water quality models after the band discrimination process were found 0.926, 0.875, and 0.810, respectively, achieving a fair fitting to real water quality data measurements. Finally, a comparison between estimated and measured water quality values not previously used for model development was carried out to validate these models. In this validation process, a good fit of 98% and 93% was obtained for TDS and TOC, respectively, whereas an acceptable fit of 81% was obtained for Chl-a. This study proposes an interesting alternative for ordered and standardized steps applied to generate mathematical models for the estimation of TOC, TDS, and Chl-a based on water quality parameters measured in the field and using satellite images.
ISSN:2073-4441
2073-4441
DOI:10.3390/w15142606