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Article

Determining Temporal Uncertainty of a Global Inland Surface Water Time Series

1
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Münchener Str. 20, 82234 Wessling, Germany
2
Institute of Geography, University of Innsbruck, Innrain 52f, 6020 Innsbruck, Austria
3
Institute of Geology and Geography, Chair of Remote Sensing, University of Würzburg, Oswald-Külpe-Weg, 97074 Würzburg, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Susanne Kratzer and Deepak R. Mishra
Remote Sens. 2021, 13(17), 3454; https://doi.org/10.3390/rs13173454
Received: 20 July 2021 / Revised: 19 August 2021 / Accepted: 26 August 2021 / Published: 31 August 2021
Earth observation time series are well suited to monitor global surface dynamics. However, data products that are aimed at assessing large-area dynamics with a high temporal resolution often face various error sources (e.g., retrieval errors, sampling errors) in their acquisition chain. Addressing uncertainties in a spatiotemporal consistent manner is challenging, as extensive high-quality validation data is typically scarce. Here we propose a new method that utilizes time series inherent information to assess the temporal interpolation uncertainty of time series datasets. For this, we utilized data from the DLR-DFD Global WaterPack (GWP), which provides daily information on global inland surface water. As the time series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, the requirement of data gap interpolation due to clouds constitutes the main uncertainty source of the product. With a focus on different temporal and spatial characteristics of surface water dynamics, seven auxiliary layers were derived. Each layer provides probability and reliability estimates regarding water observations at pixel-level. This enables the quantification of uncertainty corresponding to the full spatiotemporal range of the product. Furthermore, the ability of temporal layers to approximate unknown pixel states was evaluated for stratified artificial gaps, which were introduced into the original time series of four climatologic diverse test regions. Results show that uncertainty is quantified accurately (>90%), consequently enhancing the product’s quality with respect to its use for modeling and the geoscientific community. View Full-Text
Keywords: Earth observation; interpolation; MODIS; optical remote sensing; probability; reliability; validation; variability Earth observation; interpolation; MODIS; optical remote sensing; probability; reliability; validation; variability
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MDPI and ACS Style

Mayr, S.; Klein, I.; Rutzinger, M.; Kuenzer, C. Determining Temporal Uncertainty of a Global Inland Surface Water Time Series. Remote Sens. 2021, 13, 3454. https://doi.org/10.3390/rs13173454

AMA Style

Mayr S, Klein I, Rutzinger M, Kuenzer C. Determining Temporal Uncertainty of a Global Inland Surface Water Time Series. Remote Sensing. 2021; 13(17):3454. https://doi.org/10.3390/rs13173454

Chicago/Turabian Style

Mayr, Stefan, Igor Klein, Martin Rutzinger, and Claudia Kuenzer. 2021. "Determining Temporal Uncertainty of a Global Inland Surface Water Time Series" Remote Sensing 13, no. 17: 3454. https://doi.org/10.3390/rs13173454

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