Estimation and mapping of water quality parameters using satellite images: a case study of Two Rivers Dam, Kenya
The continuous water quality monitoring (WQM) of watersheds and the existing water supplies is a crucial step in realizing sustainable water development and management. However, the conventional approaches are time-consuming, labor intensive, and do not give spatial–temporal variations of the water...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2023-02-01
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| Series: | Water Practice and Technology |
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| Online Access: | http://wpt.iwaponline.com/content/18/2/428 |
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| author | Alice Nureen Omondi Yashon Ouma Job Rotich Kosgei Victor Kongo Ednah Jelagat Kemboi Simon Mburu Njoroge Achisa Cleophas Mecha Emmanuel Chessum Kipkorir |
| author_facet | Alice Nureen Omondi Yashon Ouma Job Rotich Kosgei Victor Kongo Ednah Jelagat Kemboi Simon Mburu Njoroge Achisa Cleophas Mecha Emmanuel Chessum Kipkorir |
| author_sort | Alice Nureen Omondi |
| collection | DOAJ |
| description | The continuous water quality monitoring (WQM) of watersheds and the existing water supplies is a crucial step in realizing sustainable water development and management. However, the conventional approaches are time-consuming, labor intensive, and do not give spatial–temporal variations of the water quality indices. The advancements in remote sensing techniques have enabled WQM over larger temporal and spatial scales. This study used satellite images and an empirical multivariate regression model (EMRM) to estimate chlorophyll-a (Chl-a), total suspended solids (TSS), and turbidity. Furthermore, ordinary Kriging was applied to generate spatial maps showing the distribution of water quality parameters (WQPs). For all the samples, turbidity was estimated with an R2 and Pearson correlation coefficient (r) of 0.763 and 0.818, respectively while TSS estimation gave respective R2 and r values of 0.809 and 0.721. Chl-a was estimated with accuracies of R2 and r of 0.803 and 0.731, respectively. Based on the results, this study concluded that WQPs provide a spatial–temporal view of the water quality in time and space that can be retrieved from satellite data products with reasonable accuracy.
HIGHLIGHTS
Remote sensing could avail a cost-effective option for the continuous monitoring of watersheds and water resources.;
Satellite-derived data could inform water quality monitoring decisions.;
Ordinary Kriging enabled the development of water quality spatial distribution maps for the water supply reservoir.;
An empirical multivariate regression modeling (EMRM) approach is used for the development of model coefficients.; |
| first_indexed | 2024-04-09T19:03:22Z |
| format | Article |
| id | doaj.art-26bbeee3cda54f1f95b13f98a91a0251 |
| institution | Directory Open Access Journal |
| issn | 1751-231X |
| language | English |
| last_indexed | 2024-04-09T19:03:22Z |
| publishDate | 2023-02-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Practice and Technology |
| spelling | doaj.art-26bbeee3cda54f1f95b13f98a91a02512023-04-07T14:42:48ZengIWA PublishingWater Practice and Technology1751-231X2023-02-0118242844310.2166/wpt.2023.010010Estimation and mapping of water quality parameters using satellite images: a case study of Two Rivers Dam, KenyaAlice Nureen Omondi0Yashon Ouma1Job Rotich Kosgei2Victor Kongo3Ednah Jelagat Kemboi4Simon Mburu Njoroge5Achisa Cleophas Mecha6Emmanuel Chessum Kipkorir7 Department of Civil and Structural Engineering, Moi University, P.O. Box 3900-30100, Eldoret, Kenya Department of Civil and Structural Engineering, Moi University, P.O. Box 3900-30100, Eldoret, Kenya Department of Civil and Structural Engineering, Moi University, P.O. Box 3900-30100, Eldoret, Kenya Global Water Partnership (SA)/Tanzania Water Partnership, P.O. Box 32334, Dar es Salaam, Tanzania Department of Civil and Structural Engineering, Moi University, P.O. Box 3900-30100, Eldoret, Kenya Department of Civil and Structural Engineering, Moi University, P.O. Box 3900-30100, Eldoret, Kenya Department of Chemical and Processing Engineering, Moi University, P.O. Box 3900-30100, Eldoret, Kenya Department of Civil and Structural Engineering, Moi University, P.O. Box 3900-30100, Eldoret, Kenya The continuous water quality monitoring (WQM) of watersheds and the existing water supplies is a crucial step in realizing sustainable water development and management. However, the conventional approaches are time-consuming, labor intensive, and do not give spatial–temporal variations of the water quality indices. The advancements in remote sensing techniques have enabled WQM over larger temporal and spatial scales. This study used satellite images and an empirical multivariate regression model (EMRM) to estimate chlorophyll-a (Chl-a), total suspended solids (TSS), and turbidity. Furthermore, ordinary Kriging was applied to generate spatial maps showing the distribution of water quality parameters (WQPs). For all the samples, turbidity was estimated with an R2 and Pearson correlation coefficient (r) of 0.763 and 0.818, respectively while TSS estimation gave respective R2 and r values of 0.809 and 0.721. Chl-a was estimated with accuracies of R2 and r of 0.803 and 0.731, respectively. Based on the results, this study concluded that WQPs provide a spatial–temporal view of the water quality in time and space that can be retrieved from satellite data products with reasonable accuracy. HIGHLIGHTS Remote sensing could avail a cost-effective option for the continuous monitoring of watersheds and water resources.; Satellite-derived data could inform water quality monitoring decisions.; Ordinary Kriging enabled the development of water quality spatial distribution maps for the water supply reservoir.; An empirical multivariate regression modeling (EMRM) approach is used for the development of model coefficients.;http://wpt.iwaponline.com/content/18/2/428chlorophyll-aempirical multivariate regression modelinglandsat-8tssturbidity |
| spellingShingle | Alice Nureen Omondi Yashon Ouma Job Rotich Kosgei Victor Kongo Ednah Jelagat Kemboi Simon Mburu Njoroge Achisa Cleophas Mecha Emmanuel Chessum Kipkorir Estimation and mapping of water quality parameters using satellite images: a case study of Two Rivers Dam, Kenya Water Practice and Technology chlorophyll-a empirical multivariate regression modeling landsat-8 tss turbidity |
| title | Estimation and mapping of water quality parameters using satellite images: a case study of Two Rivers Dam, Kenya |
| title_full | Estimation and mapping of water quality parameters using satellite images: a case study of Two Rivers Dam, Kenya |
| title_fullStr | Estimation and mapping of water quality parameters using satellite images: a case study of Two Rivers Dam, Kenya |
| title_full_unstemmed | Estimation and mapping of water quality parameters using satellite images: a case study of Two Rivers Dam, Kenya |
| title_short | Estimation and mapping of water quality parameters using satellite images: a case study of Two Rivers Dam, Kenya |
| title_sort | estimation and mapping of water quality parameters using satellite images a case study of two rivers dam kenya |
| topic | chlorophyll-a empirical multivariate regression modeling landsat-8 tss turbidity |
| url | http://wpt.iwaponline.com/content/18/2/428 |
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