Digital elevation models (DEM) are recognized as a core spatial dataset required for many environmental applications. However, the availability of comprehensive DEMs for water resources studies is rather limited and limitations of current, free or open-access DEMs are well-known. Freely available and global scale DEMs, such as that from the Shuttle Radar Topography Mission (SRTM) or from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) mission exhibit large vertical errors which are exacerbated over complex topography and they cannot resolve microtopographic variations in relatively flat terrain For instance, SRTM mission requirements defined absolute and relative elevation errors of 16 and 6 m, respectively Even though several studies have found actual errors to be considerably smaller than the requirements (see e.g. Over the years, several processing algorithms and approaches for merging with other elevation datasets have been proposed to increase accuracy and remove vegetation biases However, whilst such derived versions are widely used, they still typically exhibit errors in the vertical much larger than those acceptable for many applications.
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Schumann et al. (2018) studied this question.
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