A method for robust estimation of snow seasonality metrics from Landsat and Sentinel-2 time series data over Atlas Mountains scale using Google Earth Engine
AbstractSeasonal snow cover provides the majority of freshwater supplies for human society and natural ecosystems especially in semi-arid regions. For water resource managers, precise data regarding the spatiotemporal variability of snow cover and snow phenology is of paramount importance. Owing to...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-01-01
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| Series: | Geocarto International |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2024.2313001 |
| _version_ | 1797296094028234752 |
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| author | Youssra El jabiri Abdelghani Boudhar Abdelaziz Htitiou Eric A. Sproles Mostafa Bousbaa Hafsa Bouamri Abdelghani Chehbouni |
| author_facet | Youssra El jabiri Abdelghani Boudhar Abdelaziz Htitiou Eric A. Sproles Mostafa Bousbaa Hafsa Bouamri Abdelghani Chehbouni |
| author_sort | Youssra El jabiri |
| collection | DOAJ |
| description | AbstractSeasonal snow cover provides the majority of freshwater supplies for human society and natural ecosystems especially in semi-arid regions. For water resource managers, precise data regarding the spatiotemporal variability of snow cover and snow phenology is of paramount importance. Owing to the great spatial and temporal heterogeneity of the snowpack and the inaccessibility of high mountainous areas, gapless satellite remote sensing presents an unprecedented opportunity to monitor snow cover effectively and affordably on a fine scale, from different aspects, and with regular revisit time. This study derives the snow seasonality metrics (first day of snowfall, last day of snow melt; and snow cover duration) over a large semi-arid region in Morocco’s Atlas Mountains. We calculate these metrics by combining over 10,000 images from Landsat 8 and Sentinel 2 satellites for four hydrological years (2016–2021) to create a harmonized product with a time interval of about 3 days using the Google Earth Engine (GEE) platform. This dense and large time series facilitates a gap-filling method to minimize and overcome the effect of cloud cover, and its assessment shows a positive correlation between the masked pixels and the interpolated ones. These methods allowed us to realize a map of the snow cover area and extract a homogeneous Normalized Difference Snow Index (NDSI) profile over the four years whereby we were able to determine the optimal threshold to separate the presence of snow from its absence. The results showed that derived snow cover metrics provide considerable variation in both time and space, where an increase in snowpack measurement values at higher elevations can be noted. Overall, the snow duration ranges between November and April depending on the characteristics of each hydrological year. The retrieved Landsat 8 and Sentinel 2 snow dates had a high level of agreement with in-situ data observations with almost a day-and-a-half delay with an overall accuracy equal to 0.96. The analysis of snow cover dynamics via GEE has offered the ability to calculate the first day of snowfall, last day of snowmelt, and snow cover duration annually at a pixel level, providing the user with the ability to track the seasonal and interannual variability in the timing of snowmelt toward a better understanding of how the hydrological cycles of higher latitude and mountainous regions are responding to climate change. |
| first_indexed | 2024-03-07T21:58:23Z |
| format | Article |
| id | doaj.art-a998b08404f449aaa27b1af2405f2e46 |
| institution | Directory Open Access Journal |
| issn | 1010-6049 1752-0762 |
| language | English |
| last_indexed | 2024-03-07T21:58:23Z |
| publishDate | 2024-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj.art-a998b08404f449aaa27b1af2405f2e462024-02-24T08:22:17ZengTaylor & Francis GroupGeocarto International1010-60491752-07622024-01-0139110.1080/10106049.2024.2313001A method for robust estimation of snow seasonality metrics from Landsat and Sentinel-2 time series data over Atlas Mountains scale using Google Earth EngineYoussra El jabiri0Abdelghani Boudhar1Abdelaziz Htitiou2Eric A. Sproles3Mostafa Bousbaa4Hafsa Bouamri5Abdelghani Chehbouni6Data4Earth Laboratory, Sultan Moulay Slimane University, Beni Mellal, MoroccoData4Earth Laboratory, Sultan Moulay Slimane University, Beni Mellal, MoroccoData4Earth Laboratory, Sultan Moulay Slimane University, Beni Mellal, MoroccoDepartment of Earth Sciences, MT State University, Bozeman, MontanaCentre for Remote Sensing Applications, Mohammed VI Polytechnic University, Ben Guerir, MoroccoInternational Water Research Institue (IWRI), Mohammed VI Polytechnic University, Ben Guerir, MoroccoCentre for Remote Sensing Applications, Mohammed VI Polytechnic University, Ben Guerir, MoroccoAbstractSeasonal snow cover provides the majority of freshwater supplies for human society and natural ecosystems especially in semi-arid regions. For water resource managers, precise data regarding the spatiotemporal variability of snow cover and snow phenology is of paramount importance. Owing to the great spatial and temporal heterogeneity of the snowpack and the inaccessibility of high mountainous areas, gapless satellite remote sensing presents an unprecedented opportunity to monitor snow cover effectively and affordably on a fine scale, from different aspects, and with regular revisit time. This study derives the snow seasonality metrics (first day of snowfall, last day of snow melt; and snow cover duration) over a large semi-arid region in Morocco’s Atlas Mountains. We calculate these metrics by combining over 10,000 images from Landsat 8 and Sentinel 2 satellites for four hydrological years (2016–2021) to create a harmonized product with a time interval of about 3 days using the Google Earth Engine (GEE) platform. This dense and large time series facilitates a gap-filling method to minimize and overcome the effect of cloud cover, and its assessment shows a positive correlation between the masked pixels and the interpolated ones. These methods allowed us to realize a map of the snow cover area and extract a homogeneous Normalized Difference Snow Index (NDSI) profile over the four years whereby we were able to determine the optimal threshold to separate the presence of snow from its absence. The results showed that derived snow cover metrics provide considerable variation in both time and space, where an increase in snowpack measurement values at higher elevations can be noted. Overall, the snow duration ranges between November and April depending on the characteristics of each hydrological year. The retrieved Landsat 8 and Sentinel 2 snow dates had a high level of agreement with in-situ data observations with almost a day-and-a-half delay with an overall accuracy equal to 0.96. The analysis of snow cover dynamics via GEE has offered the ability to calculate the first day of snowfall, last day of snowmelt, and snow cover duration annually at a pixel level, providing the user with the ability to track the seasonal and interannual variability in the timing of snowmelt toward a better understanding of how the hydrological cycles of higher latitude and mountainous regions are responding to climate change.https://www.tandfonline.com/doi/10.1080/10106049.2024.2313001Snow metricsLandsat 8Sentinel 2Google Earth Enginecloud maskinggap filling |
| spellingShingle | Youssra El jabiri Abdelghani Boudhar Abdelaziz Htitiou Eric A. Sproles Mostafa Bousbaa Hafsa Bouamri Abdelghani Chehbouni A method for robust estimation of snow seasonality metrics from Landsat and Sentinel-2 time series data over Atlas Mountains scale using Google Earth Engine Geocarto International Snow metrics Landsat 8 Sentinel 2 Google Earth Engine cloud masking gap filling |
| title | A method for robust estimation of snow seasonality metrics from Landsat and Sentinel-2 time series data over Atlas Mountains scale using Google Earth Engine |
| title_full | A method for robust estimation of snow seasonality metrics from Landsat and Sentinel-2 time series data over Atlas Mountains scale using Google Earth Engine |
| title_fullStr | A method for robust estimation of snow seasonality metrics from Landsat and Sentinel-2 time series data over Atlas Mountains scale using Google Earth Engine |
| title_full_unstemmed | A method for robust estimation of snow seasonality metrics from Landsat and Sentinel-2 time series data over Atlas Mountains scale using Google Earth Engine |
| title_short | A method for robust estimation of snow seasonality metrics from Landsat and Sentinel-2 time series data over Atlas Mountains scale using Google Earth Engine |
| title_sort | method for robust estimation of snow seasonality metrics from landsat and sentinel 2 time series data over atlas mountains scale using google earth engine |
| topic | Snow metrics Landsat 8 Sentinel 2 Google Earth Engine cloud masking gap filling |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2024.2313001 |
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