Topography and Land Cover Effects on Snow Water Equivalent Estimation Using AMSR-E and GLDAS Data

  • Hadi Ansari
  • Safar MarofiEmail author
  • Mohamad Mohamadi


Accurate predictions of snow characteristics have an essential function in water resources management, especially in the high mountainous areas. Remote sensing presents a possibility for snow characteristics observation, such as snow water equivalent (SWE), in the large basins. Many studies are focused on the assessment of remote sensing product, especially global SWE data. However, regional effects such as topography, land cover, and meteorological conditions may lead to uncertainty in the estimation of the snow characteristics. In this research, the Advanced Microwave Scanning Radiometer-Eos (AMSR-E) data and the GLDAS model data (2006–2011) were used to estimate SWE in the northwest basins of Iran. The evaluation was performed by the root mean square error (RMSE) and percent bias (PBIAS) criteria. The results indicated a significant correlation (at 1% level) between the observed and estimated SWE data. According to the results, the estimation accuracy decreased with increasing altitude, land slope, and the normalized difference vegetation index (NDVI). The best estimation was detected at altitudes between 1350 and 1600 m. Generally, the SWE products of the AMSR-E and GLDAS data on the north-facing slope shows good accuracy in the SWE estimation compared to the other aspects.


AMSR-E sensor Aqua satellite GLDAS model Passive microwave Snow water equivalent 



The authors would like to thank the National Snow and Ice Data Center (NSIDC) for the AMSR-E data.

Compliance with Ethical Standards

Conflict of Interest



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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Water Science Engineering, Faculty of AgricultureBu-Ali Sina UniversityHamadanIran

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