Journal metrics

Journal metrics

  • IF value: 4.524 IF 4.524
  • IF 5-year value: 5.558 IF 5-year 5.558
  • CiteScore value: 4.84 CiteScore 4.84
  • SNIP value: 1.425 SNIP 1.425
  • SJR value: 3.034 SJR 3.034
  • IPP value: 4.65 IPP 4.65
  • h5-index value: 52 h5-index 52
  • Scimago H index value: 55 Scimago H index 55
Volume 12, issue 5 | Copyright
The Cryosphere, 12, 1629-1642, 2018
https://doi.org/10.5194/tc-12-1629-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 04 May 2018

Research article | 04 May 2018

On the need for a time- and location-dependent estimation of the NDSI threshold value for reducing existing uncertainties in snow cover maps at different scales

Stefan Härer1, Matthias Bernhardt1, Matthias Siebers2, and Karsten Schulz1 Stefan Härer et al.
  • 1Institute of Water Management, Hydrology and Hydraulic Engineering (IWHW), University of Natural Resources and Life Sciences (BOKU), 1190 Vienna, Austria
  • 2Commission for Glaciology, Bavarian Academy of Sciences and Humanities, 80539 Munich, Germany

Abstract. Knowledge of current snow cover extent is essential for characterizing energy and moisture fluxes at the Earth's surface. The snow-covered area (SCA) is often estimated by using optical satellite information in combination with the normalized-difference snow index (NDSI). The NDSI thereby uses a threshold for the definition if a satellite pixel is assumed to be snow covered or snow free. The spatiotemporal representativeness of the standard threshold of 0.4 is however questionable at the local scale. Here, we use local snow cover maps derived from ground-based photography to continuously calibrate the NDSI threshold values (NDSIthr) of Landsat satellite images at two European mountain sites of the period from 2010 to 2015. The Research Catchment Zugspitzplatt (RCZ, Germany) and Vernagtferner area (VF, Austria) are both located within a single Landsat scene. Nevertheless, the long-term analysis of the NDSIthr demonstrated that the NDSIthr at these sites are not correlated (r = 0.17) and different than the standard threshold of 0.4. For further comparison, a dynamic and locally optimized NDSI threshold was used as well as another locally optimized literature threshold value (0.7). It was shown that large uncertainties in the prediction of the SCA of up to 24.1% exist in satellite snow cover maps in cases where the standard threshold of 0.4 is used, but a newly developed calibrated quadratic polynomial model which accounts for seasonal threshold dynamics can reduce this error. The model minimizes the SCA uncertainties at the calibration site VF by 50% in the evaluation period and was also able to improve the results at RCZ in a significant way. Additionally, a scaling experiment shows that the positive effect of a locally adapted threshold diminishes using a pixel size of 500m or larger, underlining the general applicability of the standard threshold at larger scales.

Download & links
Publications Copernicus
Download
Short summary
The paper presents an approach which can be used to process satellite-based snow cover maps with a higher-than-today accuracy at the local scale. Many of the current satellite-based snow maps are using the NDSI with a threshold as a tool for deciding if there is snow on the ground or not. The presented study has shown that, firstly, using the standard threshold of 0.4 can result in significant derivations at the local scale and that, secondly, the deviations become smaller for coarser scales.
The paper presents an approach which can be used to process satellite-based snow cover maps with...
Citation
Share