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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/14/2606 |
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| author | Juan G. Loaiza Jesús Gabriel Rangel-Peraza Sergio Alberto Monjardín-Armenta Yaneth A. Bustos-Terrones Erick R. Bandala Antonio J. Sanhouse-García Sergio A. Rentería-Guevara |
| author_facet | Juan G. Loaiza Jesús Gabriel Rangel-Peraza Sergio Alberto Monjardín-Armenta Yaneth A. Bustos-Terrones Erick R. Bandala Antonio J. Sanhouse-García Sergio A. Rentería-Guevara |
| author_sort | Juan G. Loaiza |
| collection | DOAJ |
| description | 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 <i>r</i><sup>2</sup> 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. |
| first_indexed | 2024-03-11T00:33:34Z |
| format | Article |
| id | doaj.art-bbb5b51ce2784909aa6563a797aa2ec8 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T00:33:34Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-bbb5b51ce2784909aa6563a797aa2ec82023-11-18T21:47:43ZengMDPI AGWater2073-44412023-07-011514260610.3390/w15142606Surface Water Quality Assessment through Remote Sensing Based on the Box–Cox Transformation and Linear RegressionJuan G. Loaiza0Jesús Gabriel Rangel-Peraza1Sergio Alberto Monjardín-Armenta2Yaneth A. Bustos-Terrones3Erick R. Bandala4Antonio J. Sanhouse-García5Sergio A. Rentería-Guevara6Tecnológico Nacional de México/Instituto Tecnológico de Culiacán, Juan de Dios Bátiz 310, Col. Guadalupe, Culiacán 80220, Sinaloa, MexicoTecnológico Nacional de México/Instituto Tecnológico de Culiacán, Juan de Dios Bátiz 310, Col. Guadalupe, Culiacán 80220, Sinaloa, MexicoFacultad de Ciencias de la Tierra y el Espacio, Universidad Autónoma de Sinaloa, Circuito Interior Oriente, Cd Universitaria, Culiacán 80040, Sinaloa, MexicoCONAHCYT-Instituto Tecnológico de Culiacán, Juan de Dios Bátiz 310, Col. Guadalupe, Culiacán 80220, Sinaloa, MexicoDivision of Hydrologic Sciences, Desert Research Institute, 755 Flamingo Road, Las Vegas, NV 89119, USATecnológico Nacional de México/Instituto Tecnológico de Culiacán, Juan de Dios Bátiz 310, Col. Guadalupe, Culiacán 80220, Sinaloa, MexicoFacultad de Ingeniería, Universidad Autónoma de Sinaloa, Circuito Interior Oriente, Cd Universitaria, Culiacán 80040, Sinaloa, MexicoA 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 <i>r</i><sup>2</sup> 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.https://www.mdpi.com/2073-4441/15/14/2606surface water qualityremote sensingBox–Cox optimizationlinear modelingLandsat imagery |
| spellingShingle | Juan G. Loaiza Jesús Gabriel Rangel-Peraza Sergio Alberto Monjardín-Armenta Yaneth A. Bustos-Terrones Erick R. Bandala Antonio J. Sanhouse-García Sergio A. Rentería-Guevara Surface Water Quality Assessment through Remote Sensing Based on the Box–Cox Transformation and Linear Regression Water surface water quality remote sensing Box–Cox optimization linear modeling Landsat imagery |
| title | Surface Water Quality Assessment through Remote Sensing Based on the Box–Cox Transformation and Linear Regression |
| title_full | Surface Water Quality Assessment through Remote Sensing Based on the Box–Cox Transformation and Linear Regression |
| title_fullStr | Surface Water Quality Assessment through Remote Sensing Based on the Box–Cox Transformation and Linear Regression |
| title_full_unstemmed | Surface Water Quality Assessment through Remote Sensing Based on the Box–Cox Transformation and Linear Regression |
| title_short | Surface Water Quality Assessment through Remote Sensing Based on the Box–Cox Transformation and Linear Regression |
| title_sort | surface water quality assessment through remote sensing based on the box cox transformation and linear regression |
| topic | surface water quality remote sensing Box–Cox optimization linear modeling Landsat imagery |
| url | https://www.mdpi.com/2073-4441/15/14/2606 |
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