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Ιδρυματικό Αποθετήριο [SANDBOX]
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| URI | http://purl.tuc.gr/dl/dias/50837AFB-B477-40F9-8E71-2B408B1A8179 | - |
| Αναγνωριστικό | https://doi.org/10.3390/w11030556 | - |
| Αναγνωριστικό | https://www.mdpi.com/2073-4441/11/3/556 | - |
| Γλώσσα | en | - |
| Μέγεθος | 14 pages | en |
| Τίτλος | Assessment of water quality parameters using temporal remote sensing spectral reflectance in arid environments, Saudi Arabia | en |
| Δημιουργός | Elhag Mohamed | en |
| Δημιουργός | Gitas Ioannis Zois | en |
| Δημιουργός | Othman Anas | en |
| Δημιουργός | Bahrawi Jarbou A. | en |
| Δημιουργός | Gikas Petros | en |
| Δημιουργός | Γκικας Πετρος | el |
| Εκδότης | MDPI | en |
| Περίληψη | Remote sensing applications in water resources management are quite essential in watershed characterization, particularly when mega basins are under investigation. Water quality parameters help in decision making regarding the further use of water based on its quality. Water quality parameters of chlorophyll a concentration, nitrate concentration, and water turbidity were used in the current study to estimate the water quality parameters in the dam lake of Wadi Baysh, Saudi Arabia. Water quality parameters were collected daily over 2 years (2017-2018) from the water treatment station located within the dam vicinity and were correspondingly tested against remotely sensed water quality parameters. Remote sensing data were collected from Sentinel-2 sensor, European Space Agency (ESA) on a satellite temporal resolution basis. Data were pre-processed then processed to estimate the maximum chlorophyll index (MCI), green normalized difference vegetation index (GNDVI) and normalized difference turbidity index (NDTI). Zonal statistics were used to improve the regression analysis between the spatial data estimated from the remote sensing images and the nonspatial data collected from the water treatment plant. Results showed different correlation coefficients between the ground truth collected data and the corresponding indices conducted from remote sensing data. Actual chlorophyll a concentration showed high correlation with estimated MCI mean values with an R2 of 0.96, actual nitrate concentration showed high correlation with the estimated GNDVI mean values with an R2 of 0.94, and the actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R2 of 0.94. The research findings support the use of remote sensing data of Sentinel-2 to estimate water quality parameters in arid environments. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2020-08-28 | - |
| Ημερομηνία Δημοσίευσης | 2019 | - |
| Θεματική Κατηγορία | Green normalized difference vegetation index (GNDVI) | en |
| Θεματική Κατηγορία | Maximum chlorophyll index (MCI) | en |
| Θεματική Κατηγορία | Normalized difference turbidity index (NDTI) | en |
| Θεματική Κατηγορία | Sentinel-2 | en |
| Βιβλιογραφική Αναφορά | M. Elhag, I. Gitas, A. Othman, J. Bahrawi and P. Gikas, "Assessment of water quality parameters using temporal remote sensing spectral reflectance in arid environments, Saudi Arabia," Water, vol. 11, no. 3, Mar. 2019. doi: 10.3390/w11030556 | en |
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