Airborne drones for water quality mapping in inland, transitional and coastal waters—MapEO water data processing and validation
De Keukelaere, L.; Moelans, R.; Knaeps, E.; Sterckx, S.; Reusen, I.; De Munck, D.; Simis, S.G.H.; Constantinescu, A.M.; Scrieciu, A.; Katsouras, G.; Mertens, W.; Hunter, P.D.; Spyrakos, E.; Tyler, A. (2023). Airborne drones for water quality mapping in inland, transitional and coastal waters—MapEO water data processing and validation. Remote Sens. 15(5): 1345. https://dx.doi.org/10.3390/rs15051345
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| Trefwoorden |
Marien/Kust; Brak water; Zoet water |
| Author keywords |
airborne drone; UAV; optical water quality; automated drone image processing; MapEO water; inland and coastal waters; georeferencing; sky glint; iCOR |
| Auteurs | | Top |
- De Keukelaere, L., meer
- Moelans, R., meer
- Knaeps, E., meer
- Sterckx, S., meer
- Reusen, I., meer
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- De Munck, D., meer
- Simis, S.G.H.
- Constantinescu, A.M.
- Scrieciu, A.
- Katsouras, G.
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- Mertens, W., meer
- Hunter, P.D.
- Spyrakos, E.
- Tyler, A.
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| Abstract |
Using airborne drones to monitor water quality in inland, transitional or coastal surface waters is an emerging research field. Airborne drones can fly under clouds at preferred times, capturing data at cm resolution, filling a significant gap between existing in situ, airborne and satellite remote sensing capabilities. Suitable drones and lightweight cameras are readily available on the market, whereas deriving water quality products from the captured image is not straightforward; vignetting effects, georeferencing, the dynamic nature and high light absorption efficiency of water, sun glint and sky glint effects require careful data processing. This paper presents the data processing workflow behind MapEO water, an end-to-end cloud-based solution that deals with the complexities of observing water surfaces and retrieves water-leaving reflectance and water quality products like turbidity and chlorophyll-a (Chl-a) concentration. MapEO water supports common camera types and performs a geometric and radiometric correction and subsequent conversion to reflectance and water quality products. This study shows validation results of water-leaving reflectance, turbidity and Chl-a maps derived using DJI Phantom 4 pro and MicaSense cameras for several lakes across Europe. Coefficients of determination values of 0.71 and 0.93 are obtained for turbidity and Chl-a, respectively. We conclude that airborne drone data has major potential to be embedded in operational monitoring programmes and can form useful links between satellite and in situ observations. |
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