the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Validation of VIIRS snow cover in Central European Highlands
Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the most attractive remote sensing datasets used for mapping snow cover. The MODIS product is expected to be replaced by the Visible Infrared Imaging Radiometer Suite (VIIRS) snow cover product in the near future. Therefore, a reliable and accurate evaluation of this product is needed for future hydrological applications. This study aims to assess the mapping accuracy of the VIIRS snow product at the regional scale (i.e., at 631 climate stations in Austria) and within a small experimental catchment, the Jalovecký Creek catchment in northern Slovakia, using extensive snow course measurements conducted at both open and forested sites between January 2012 and December 2020. In the VIIRS snow cover product, the Normalized Difference Snow Index (NDSI) is used to detect snow. A threshold of NDSI (TNDSI) is needed for distinguishing snow from snow-free land. Based on the daily snow depth observations from climate stations/snow course locations, the optimal NDSI threshold (OTNDSI) is first determined through a detailed sensitivity test (100 different TNDSI from 1 to 100 with a step of 1). The overall accuracy (OA) of VIIRS data is then evaluated based on the OTNDSI. The assessment of the OTNDSI/OA is performed for all climate stations/snow profiles, as well as for different groups of stations/snow profiles representing different physiographic and land cover conditions.
The findings demonstrate that the classification accuracy for 631 Austrian stations using the optimal thresholds ranges from 52.3 % to 99.3 %, with a median of 92.5 %. The NDSI thresholds vary seasonally and decrease with increasing elevation. The NDSI thresholds fitted to different months and elevations show the smallest differences to OTNDSI in overall accuracy. The NDSI thresholds fitted to different elevations improve regional snow cover mapping by 1.5 % between 900 – 1200 m a.s.l. and by 3.1 % above 1200 m a.s.l. At the catchment scale, the difference is found between open and forested sites, where the mapping accuracy is lower in the forest. VIIRS enables snow mapping accuracy from 71.5 to 95.9 % at the open site (during winter, median OA is up to 98.8 %) and from 58.1 to 93.7 % at the forest site (during winter, median OA is up to 91.5 %). The overall accuracy at the site with the most measurements is 95.9 % (Červenec – open site) and 93.7 % (Červenec – forest site), respectively. The accuracy at the forest is more sensitive to seasonal variation compared to the open area, where the accuracy is more stable and accurate across the year.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2026-1120', Anonymous Referee #1, 25 Apr 2026
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AC1: 'Reply on RC1', Patrik Sleziak, 28 Apr 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2026-1120/egusphere-2026-1120-AC1-supplement.pdf
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AC1: 'Reply on RC1', Patrik Sleziak, 28 Apr 2026
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RC2: 'Comment on egusphere-2026-1120', Anonymous Referee #2, 17 Jun 2026
(1) This study provides a valuable validation of the VIIRS snow cover product over the Central European Highlands, with a design that spans both regional and catchment scales. The sensitivity analysis of the NDSI threshold across 100 levels is thorough, and the differentiation between open and forest sites adds practical insight. However, several methodological choices and interpretations need clarification or justification.
(2) The optimal NDSI threshold for the entire dataset is found to be 40, yet for forest sites it drops to around 14. This raises a concern: do you consider that the VIIRS 375 m pixel in a forested catchment may represent a mixed signal (canopy, shadow, snow), and if so, does a single “optimal” threshold per site truly capture the physical snow presence, or does it merely optimize a statistical match against point snow depth measurements that are poorly representative of the pixel?
(3) The use of a snow depth threshold of 0 cm for ground truth means that any trace or patchy snow is classified as snow. Given that VIIRS NDSI responds to fractional snow cover within a pixel, how sensitive are your OA and OT results to using a more conservative snow depth threshold (e.g., 1 or 5 cm), especially at low-elevation stations where ephemeral snow is common?
(4) In the regional analysis, you report a strong negative correlation between OT and elevation (r > 0.67), but you also show that for stations above 1200 m, the median OT is only 11, which is far below the typical 40 value. Could this extremely low threshold at high elevations be partly an artefact of persistent cloud cover or illumination effects (e.g., shadows in alpine terrain) that reduce NDSI values even when snow is present, rather than a true optimal discrimination?
(5) Figure 6 and Table 4 show that winter OA at high elevations drops to ~58–62%, which is surprisingly low. You attribute this to early snowfall and mixed conditions, but do you have any independent data (e.g., cloud mask validation, or comparison with nearby automatic weather stations) to confirm whether this low accuracy is due to VIIRS misclassification, or simply due to the poor spatial representativeness of valley-based climate stations for alpine pixels?
(6) The comparison between VIIRS and MODIS at the catchment scale (Figures 11–12) is insightful, but the analysis is limited to two selected seasons. Given that the entire study period spans 2012–2020, it would be more convincing to present a systematic quantitative comparison (e.g., contingency tables or time-series of OA) for all available overlapping days, rather than only selected SWE events.
(7) The threshold of NDSI is present in decimal and percentage, which is suggested to unified.
(8) Fig. 1, where is the Austria and Slovakia?
(9) The discussion and conclusion section is suggested to divided into two sections.
Citation: https://doi.org/10.5194/egusphere-2026-1120-RC2 - AC2: 'Reply on RC2', Patrik Sleziak, 25 Jun 2026
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This is a potentially useful paper on regional evaluation of VIIRS snow cover products to replace MODIS, but it will require major revision. An uninformed reader could be left with the impression that the VIIRS snow cover is new and largely untested. In fact, a cursory search reveals more than twenty published evaluations going back to 2013. The information from Overall Agreement optimized for the entire dataset will be limited; is it possible to divide the data into periods or elevation bands with evaluation against observations that are not used in the optimisation?
Abstract
“MODIS” is not a dataset or a snow cover product (M*D10 is), and it is misleading to say that it is expected to be replaced by VIIRS in the near future; VIIRS snow cover products have been available since 2012.
Introduction
MODIS is on both Terra and Aqua, which are now in drifting orbits and data collection from both is expected to end in 2027.
Data
Smaller dots in Figure 1 would give a better impression of the distribution of stations. The open and forest sites with fortnightly measurements should be identified.
How dense is the canopy at the forest site?
The VIIRS Snow Cover Products User Guide lists eight products; the one used should be stated.
The VIIRS visible channel used for NDSI isn’t 0.55 microns and isn’t green.
NDSI does not distinguish snow from inland water; a water mask is used.
How many stations are there in each of the elevation groups? How representative are the point measurements of surrounding 375 m areas?
Methods
Numerator of Equation 2 should be A + D.
If T_SD = 0 cm, “Station snow depth >= T_SD (SNOW)” in Table 1 does not make sense.
Results
Fraction of measurements with snow cover would be a useful addition to figures. 100% accuracy for a 100% NDSI threshold in snow-free months is trivial.
Discussion and conclusions
Zhang et al. (2020) and Dietz and Rossler (2025) used different metrics to select thresholds; does this make a difference?
Minor corrections:
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“differences are found”
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“existing global snow cover products” here refers to MODIS products.
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“along an approximately”
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“each of the categories of the confusion matrix”
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“The optimal NDSI threshold”