the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Jul 2025
Analysis of Snow Cover Changes using MODIS and Google Earth Engine. A Tool for Measuring Climatic Change Effects on Snow in Italian Western Alps in the period 2000–2023
Abstract. Climate change (CC) is significantly impacting the snow cover of the European Alps, compromising winter tourism, hydrological cycles and water stock for agricultural and civil supply. This study investigates Snow Cover Changes (SCC) in the Western Italian Alps (Piemonte and Valle d'Aosta regions) from 2000 to 2023, using MODIS satellite data. In particular, MOD10A1 images were processed in Google Earth Engine to derive daily snow cover, integral snow cover area (iSCA), snow persistence (SP), and mean daily snowed area (MDSA). Ground data from 7 snowmeter stations were used to validate the satellite-derived SP. The analysis of SCC was performed by quantifying long-term trends of MDSA at-the-pixel-level. The normalized trend (nT) index represents the percentage change rate in snow-covered area per mean snow event, since 2000. It was mapped showing different spatial patterns of SCC in the study area. Results reveal an altitudinal gradient in nT, with the higher snow cover reduction occurring in lowland and within main valley areas, reaching -5 % below 1000 m a.s.l. and -1.8 % between 1000–1500 m a.s.l. These findings highlight the vulnerability of snow resources due to CC, impacting water availability, winter sports, and regional economies. This study can support adaptation strategies and sustainable resource management in the Western Alps by mapping critical areas where CC effects on snow must be mitigated.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-3028', Anonymous Referee #1, 14 Sep 2025
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RC2: 'Comment on egusphere-2025-3028', Anonymous Referee #2, 09 Oct 2025
The manuscript presents an analysis of snow cover changes in the Western Italian Alps (2000–2023) using MODIS data processed through Google Earth Engine (GEE). The topic is relevant, and the effort to quantify long-term snow dynamics at regional scale is commendable. However, the paper requires substantial revision to reach the scientific depth.
1. Conceptual framework and novelty
Although the introduction provides context on snow monitoring, it lacks a clear articulation of the research gap and the specific contribution of this study. Similar MODIS-based analyses have been published for Alpine regions, yet the manuscript does not sufficiently differentiate its approach or findings from prior work. The use of the nT index could represent a methodological contribution, but its conceptual basis and added value over standard trend analyses (e.g., Mann–Kendall or Sen’s slope) are not adequately explained.2. Methods and reproducibility
The methodological description is too general. The computation of iSCA, SP, MDSA, and nT indices should be fully explained, including equations, temporal aggregation steps (in data gap-filling), and cloud-masking criteria. The validation using seven snow stations might be insufficiently developed: statistical metrics (correlation coefficients, RMSE, bias) should be provided to demonstrate reliability. Without these details, the reproducibility and robustness of the findings remain uncertain. Also, GEE script should be provided. Data validation snow stations map should be added. A workflow figure is recommended.3. Results and interpretation
The results section mainly provides descriptive summaries. Trends are mentioned but not quantified in a way that allows meaningful comparison across elevation bands or subregions. The discussion of climatic controls (temperature, precipitation, topography) is superficial and largely speculative, lacking direct evidence from meteorological datasets. The authors should more explicitly link observed snow-cover trends to climate variability and recent warming patterns in the Western Alps.If these major revisions are addressed, the study could become a meaningful contribution to the understanding of snow-cover dynamics in this Western Alpine regions.
Citation: https://doi.org/10.5194/egusphere-2025-3028-RC2
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Review
The paper tries to evaluate snow cover change over 23 years in the Italian Western Alps using MODIS images. The topic, in general, can be a good analytical paper, but not a methodological one. In the results, showing and explanation, there are significant discussions as follows:
Title
I have a question: why did you title your paper “GEE,” though GEE was just used for downloading the data? The platform is not for downloading data, and all the data you used could be downloaded from other resources.
Introduction
In the introduction, you mention your goal is “to reconstruct the evolution of snow cover, from 2000 to 2023, using remote sensing technologies.” Please look at https://doi.org/10.3390/rs13152945 (which you also cite) as an example; there should be more as well. What makes your study different from theirs? You state that other studies do not use in situ data for validation, but in fact they do, for example, see https://doi.org/10.5194/hess-10-679-2006 and https://doi.org/10.1016/j.rse.2023.113877
Moreover, MODIS pixels are not directly comparable with in situ measurements because of the large difference in spatial resolution. For MODIS validation, researchers usually compare with higher-resolution satellite data such as Landsat.
Moreover, you mention your goal “using remote sensing” without giving a literature review on how RS is used for snow cover. Your literature review does not specify which data each study uses.
Area of Interest
In Fig. 1 you can see some “+” on the image. Please remove them, as outer latitude and longitude locations are enough.
Data
Why do you use MOD10A1, as we have gap-filled MODIS snow data MOD10A1F / MYD10A1F?
2.2.2 Ground Data
Please show the location of in situ data. As I understood from the abstract, there is only 7 station data. Do you think this is enough? Moreover, show them on the map so we can see the elevation of the stations. If the stations are mostly in low altitudes, it doesn’t give an accurate evaluation. Besides, 500 m vs in situ data is not a precise evaluation. How do you find which pixel represents the station?
Data Processing
Linear interpolation for filling the gap for a variable like snow is not suitable, as snow changes rapidly and also varies from one place to another. Use MOD10A1F / MYD10A1F, which is already gap-filled.
Snow Cover Trends Quantification
In general, you cannot consider a trend by evaluating some years and comparing them to one year (2000 in your case). As one year can be dry, wet, etc., you can do a Cumulative Frequency Distribution for example.
In Fig. 2, what does “m a.s.l” stand for?
In Fig. 4, I also have a question: in high elevations, climate change affects the duration and amount of snow. However, in 23 years, climate change will mostly affect low or mid-elevation. In high elevation, as we do not consider SWE and our goal is just snow cover, how are all the nT values negative, even in high elevation? Because even with climate change, we still have below-zero temperatures to have snow cover, even with lower SWE, for example.
The same question also applies to Fig. 5.
Compare with Landsat/Sentinel snow cover data.
Conclusion
The conclusion is too short. Discussion and conclusion can be combined.