Vertical accuracy of ASTER, SRTM, and NASADEM digital surface models across Malawi’s diverse topography
5 affiliations
- 1Department of Land and Water Resources, Faculty of Life Sciences and Natural Resources, Lilongwe University of Agriculture and Natural Resources, Lilongwe, Malawi.
- 2Department of Water and Sanitation, Faculty of Environmental Sciences, Mzuzu University, Mzuzu, Malawi.
- 3Institute for Sustainability and Corporate Citizenship, University of South Africa, Pretoria, South Africa.
- 4Stockholm Environment Institute, World Agroforestry Centre, United Nations Avenue, Gigiri, Nairobi, Kenya.
- 5Department of Surveys, Ministry of Lands, Housing and Urban Development, Lilongwe, Malawi.
Abstract
Digital Surface Models (DSMs) are widely used as important sources of topographic information for earth science and environmental applications. The accuracy of DSMs has direct impacts on several environmental calculations and processes. Therefore, it is imperative to select the most suitable DSM based on the aim and accuracy requirements of each application. However, the use of an appropriate DSM has always been a challenge. This study evaluated the vertical accuracy and performance of the freely available Shuttle Radar Topography Mission (SRTM), NASA DEM (NASADEM), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The DSM elevations were measured against high-precision national geodetic control points. Among other accuracy metrics, we computed Root Mean Square Error (RMSE) and examined the influence of elevation, slope, aspect, curvature, and land cover. At the national scale, SRTM (RMSE of 19.55 m) and NASADEM (RMSE of 19.99 m) showed comparable performance, while ASTER produced a higher RMSE of 22.13 m. However, the principal finding was that DSM accuracy varied substantially across Malawi’s diverse terrain. All the DSMs performed particularly well in the low-lying Lower Shire Valley, with RMSEs of 3.25 m (NASADEM), 4.30 m (SRTM), and 6.91 m (ASTER). Errors increased markedly in the Highlands, where RMSE reached 35.03 m for NASADEM, 33.62 m for SRTM, and 36.94 m for ASTER. In addition, accuracy decreased with increasing elevation and slope, particularly in highland regions and on slopes steeper than 30°. Aspect showed the highest relative importance in the Random Forest analysis. However, the differences in accuracy among aspect classes were comparatively modest, and greater underestimation was observed on north- and west-facing slopes. These findings demonstrate that national-scale accuracy statistics alone do not adequately describe DSM suitability across Malawi’s diverse landscapes. DSM selection should therefore consider the terrain and land-cover characteristics of the intended application area.
Related
- Evaluating the performance of the surface water and ocean topography (SWOT) mission for monitoring reservoirs across the conterminous United States
- Flood Inundation Modeling Using the Surface Water Ocean Topography Mission
- Three-dimensional numerical simulation of ambient seismic noise for arbitrary surface source distributions: application to the study of horizontal-to-vertical spectral ratios and their variability
- Vertical distribution of pollution trapped by wintertime surface inversions in Fairbanks, Alaska, during the ALPACA experiment
- Cloud vertical structure across China from a national Ka-band cloud radar network: thermodynamic, dynamical, and land-surface controls
BibTeX
@article{nazombe2026vertical,
title = {{Vertical accuracy of ASTER, SRTM, and NASADEM digital surface models across Malawi’s diverse topography}},
author = {Kennedy Sones Nazombe and Brighton A Chunga and Charles Bakolo and Rodrick Mlewa and Godwell Nhamo},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0361397},
url = {https://doi.org/10.1371/journal.pone.0361397},
note = {CC BY},
}
RIS
TY - JOUR
TI - Vertical accuracy of ASTER, SRTM, and NASADEM digital surface models across Malawi’s diverse topography
AU - Kennedy Sones Nazombe
AU - Brighton A Chunga
AU - Charles Bakolo
AU - Rodrick Mlewa
AU - Godwell Nhamo
PY - 2026
DA - 2026/10/29
JO - PLOS One
DO - 10.1371/journal.pone.0361397
UR - https://doi.org/10.1371/journal.pone.0361397
AB - Digital Surface Models (DSMs) are widely used as important sources of topographic information for earth science and environmental applications. The accuracy of DSMs has direct impacts on several environmental calculations and processes. Therefore, it is imperative to select the most suitable DSM based on the aim and accuracy requirements of each application. However, the use of an appropriate DSM has always been a challenge. This study evaluated the vertical accuracy and performance of the freely available Shuttle Radar Topography Mission (SRTM), NASA DEM (NASADEM), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The DSM elevations were measured against high-precision national geodetic control points. Among other accuracy metrics, we computed Root Mean Square Error (RMSE) and examined the influence of elevation, slope, aspect, curvature, and land cover. At the national scale, SRTM (RMSE of 19.55 m) and NASADEM (RMSE of 19.99 m) showed comparable performance, while ASTER produced a higher RMSE of 22.13 m. However, the principal finding was that DSM accuracy varied substantially across Malawi’s diverse terrain. All the DSMs performed particularly well in the low-lying Lower Shire Valley, with RMSEs of 3.25 m (NASADEM), 4.30 m (SRTM), and 6.91 m (ASTER). Errors increased markedly in the Highlands, where RMSE reached 35.03 m for NASADEM, 33.62 m for SRTM, and 36.94 m for ASTER. In addition, accuracy decreased with increasing elevation and slope, particularly in highland regions and on slopes steeper than 30°. Aspect showed the highest relative importance in the Random Forest analysis. However, the differences in accuracy among aspect classes were comparatively modest, and greater underestimation was observed on north- and west-facing slopes. These findings demonstrate that national-scale accuracy statistics alone do not adequately describe DSM suitability across Malawi’s diverse landscapes. DSM selection should therefore consider the terrain and land-cover characteristics of the intended application area.
SN - 1932-6203
ER -