Editorial for the Special Issue “Assimilation of Remote Sensing Data into Earth System Models”

Calvet, Jean‐Christophe; Rosnay, Patricia de; Penny, Stephen G.

doi:10.3390/rs11182177

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…To unlock the full potential of remote sensing datasets, information on uncertainties and error magnitudes is essential [23]. For instance, comprehensive information on data uncertainties is a prerequisite for sophisticated modeling and data assimilation applications [22,[24][25][26]. Furthermore, consideration of product uncertainties increases the validity of research results.…”

Section: Introductionmentioning

confidence: 99%

Determining Temporal Uncertainty of a Global Inland Surface Water Time Series

et al. 2021

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Earth observation time series are well suited to monitoring 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.

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