Comparison and Validation of Satellite-Derived Digital Surface/Elevation Models over India
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RESEARCH ARTICLE
Comparison and Validation of Satellite-Derived Digital Surface/ Elevation Models over India R. Goyal1,2
•
W. E. Featherstone2,1
•
O. Dikshit1
•
N. Balasubramania1
Received: 28 August 2020 / Accepted: 12 November 2020 Indian Society of Remote Sensing 2020
Abstract India presents among the world’s most topographically complex geomorphologies, with land elevations ranging from –2 m to ? 8586 m and terrain gradients sometimes exceeding 45. Here, we present an evaluation of four freely available digital surface models (DSMs) on a model-to-model basis, as well as a validation using independent ground-truth data from levelled benchmarks in India. The DSMs tested comprise SRTM100 , SRTM300 , ASTER100 and Cartodem100 [an India-only model]. Along with these four DSMs, the MERIT300 digital elevation model (DEM) is also tested with the ground-truth data. Our results for India indicate some mismatch of these DEMs/DSMs from their claimed accuracies/precisions. All DSMs/DEMs (except for ASTER) have [ 90% of pixels satisfying ± 16 m at the one-sigma level, but only in the lowlying (\ 500 m) parts of India, i.e. the Gangetic plains and the Thar desert. Keywords Vertical accuracy/precision assessment Digital surface models Digital elevation models India
Introduction A digital surface model (DSM) is a representation of the shape of the Earth’s surface. Several near-global DSMs have been produced from satellite-borne platforms from either radar, e.g. SRTM (Farr et al. 2007) or stereoscopic optical imagery, e.g. ASTER (Meyer et al. 2011). We deliberately distinguish between a DSM and a digital elevation model (DEM) also sometimes known as a digital terrain model (DTM), where a DEM/DTM represents the solid topographic surface, whereas a DSM represents the surface sensed, which includes the height of vegetation & R. Goyal [email protected]; [email protected] W. E. Featherstone [email protected] O. Dikshit [email protected] N. Balasubramania [email protected] 1
Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
2
School of Earth and Planetary Sciences, Curtin University of Technology, GPO Box U1987, Perth, WA 6845, Australia
canopy and man-made structures (cf. Hirt 2014). A satellite-derived DSM should be treated for speckle noise (Gallant 2011) and stripe noise (Tarekegn and Sayama 2013), and then, it can be converted to a DEM by accounting for absolute biases (Crippen et al. 2016) and tree height biases (O’Loughlin et al. 2016). Yamazaki et al. (2017) have treated the SRTM v 2.1 DSM for all these four sources to produce the MERIT300 DEM. DEMs and DSMs should also be checked for other artefacts such as spikes, pits and line defects (e.g. Hirt 2018). DEMs and DSMs are used synonymously in several applications such as mapping soil and vegetation (e.g. Dobos and Hengl 2009; Cavazzi et al. 2013), studying natural hazards (e.g. Gruber et al. 2009; Demirkesen 2012), catchment geomorphology and hydrology (e.g. Barnes et al. 2014; Zhao et al. 20
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