Lake volumetric fluctuations
Satellite(s)Jason- 1, Jason- 2, Jason- 3, Topex/Poseidon, Landsat ETM+. | Monitoring elementWater surface height and water surface reflectance. |
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Description techniqueSichangi et al. (2017) utilised a combination of satellite altimetry, optical data (Landsat ETM+) and a bathymetry map to monitor the volumetric fluctuation of an inland water body/lake. | Accuracy / ResolutionSpatial resolution: 100m, due to the resolution of the bathymetric raster map. |
Case studyLake Victoria (part of Kenya, Tanzania and Uganda) with an area of c. 68,800 km2, largest tropical lake in the world and second largest freshwater lake. | |
BenefitsThis method allows to estimate total lake volume, which is important to effectively study lakes. Most of the methods in that field are usually limited to water level, surface area and level change parameters. | LimitationsBiggest challenge comes from availability and accuracy of lake bathymetry data. Cloud cover also limits optical observations and the Accuracy of elevation retrieval is also dependent on lake size and also on surface roughness. |
Applicability for NorthlandYes, the method looks applicable for lakes where bathymetric maps are available. Spatial and temporal resolutions will depend on imagery/altimetric data availability (e.g., TOPEX/Poseidon or Jason-1 and Jason-2 satellites have a cycle of 10 days). Techniques applying optical data will be limited in coverage and temporal granularity by the persistent cloud cover in the region, particularly during the winter months. Mature cloud-masking techniques are directly available for open access multispectral data (e.g. Landsat and Sentinel-2). When using commercial data, care must be taken to ensure that there is sufficiently cloud free imagery available, as cloud masking is not as mature and ordering a large volume of imagery to ensure complete cloud free coverage between multiple observations can become cost prohibitive.
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Publication referencesSichangi A, Makokha G. 2017. Monitoring water depth, surface area and volume changes in Lake Victoria: integrating the bathymetry map and remote sensing data during 1993–2016. Modeling Earth Systems and Environment. 3. doi:10.1007/s40808-017-0311-2. | |
Other comments or informationOther imagery sources/upcoming missions (e.g., Jason-3, Jason CS, Sentinel-3a and b, and SWOT) will likely increase the multiple data sources therefore densifying the time series observations. | |
Other referencesSchwatke C, Dettmering D, Bosch W, Seitz F (2015) DAHITI—an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry. Hydrol Earth Syst Sci 19:4345–4364. doi:10.5194/hess-19-4345-2015 https://hess.copernicus.org/articles/19/4345/2015/ Crétaux JF, Abarca-del-Río R, Bergé-Nguyen M, Arsen A, Drolon V, Clos G, Maisongrande P. 2016. Lake Volume Monitoring from Space. Surveys in Geophysics. 37(2):269-305. doi:10.1007/s10712-016-9362-6. https://link.springer.com/article/10.1007%2Fs10712-016-9362-6 Kleinherenbrink, M., Lindenbergh, R.C., Ditmar, P.G., 2015. Monitoring of lake level changes on the Tibetan Plateau and Tian Shan by retracking Cryosat SARIn waveforms. J. Hydrol. 521, 119–131. doi:10.1016/j.jhydrol.2014.11.063.
Nielsen, K., Stenseng, L., Andersen, O.B., Knudsen, P., 2017. The performance and potentials of the CryoSat-2 SAR and SARIn modes for Lake level estimation. Water 9, 374. doi:10.3390/w9060374.
Villadsen, H., Deng, X., Andersen, O.B., Stenseng, L., Nielsen, K., Knudsen, P., 2016. Improved inland water levels from SAR altimetry using novel empirical and physical retrackers. J. Hydrol. 537, 234–247. doi: 10.1016/j.jhydrol.2016.03.051.
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