An improvement in ICESat-2 ATL03 snow depth estimation: a case study in the Tuolumne Basin, USA
Most of the Earth’s snow mass is stored in the Northern Hemisphere mountains, making accurate quantitative estimates of mountainous snow mass crucial for understanding the global hydrological cycle. The ICESat-2 satellite with a high-resolution laser altimeter enables detailed snow distribution mapp...
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2505628 |
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| author | Tongrui Chen Lingmei Jiang Jianwei Yang Dan Kong Yong Pang |
| author_facet | Tongrui Chen Lingmei Jiang Jianwei Yang Dan Kong Yong Pang |
| author_sort | Tongrui Chen |
| collection | DOAJ |
| description | Most of the Earth’s snow mass is stored in the Northern Hemisphere mountains, making accurate quantitative estimates of mountainous snow mass crucial for understanding the global hydrological cycle. The ICESat-2 satellite with a high-resolution laser altimeter enables detailed snow distribution mapping but faces uncertainties in snow depth estimation below the 100-m scale in mountains. To address this challenge, we improved the ICESat-2 ATL03 snow depth estimation by applying filters for slope and photon coverage in the spatial aggregation from 20-m to 1-km scales, considering the influences of photon density, snow spatial heterogeneity, and ICESat-2 elevation errors in steep terrains. Validation in the Tuolumne River Basin, USA, demonstrated a significant improvement in the accuracy of snow depth estimation. The mean absolute error (MAE) was 0.31 m for all slopes and 0.17 m for slopes less than 10° at the 20-m scale, indicating a 40% increase in accuracy compared with previous studies. As the snow depth was aggregated to 1 km, the MAE respectively decreased to 0.21 and 0.13 m. This study examined the potential of using ICESat-2 for estimating snow depth in mountainous regions across various spatial scales, providing more reliable snow depth estimates in data-scarce mountainous regions. |
| format | Article |
| id | doaj-art-e9e7f0d3924c4a62bd8d21fc303c886f |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-e9e7f0d3924c4a62bd8d21fc303c886f2025-08-25T11:28:45ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2505628An improvement in ICESat-2 ATL03 snow depth estimation: a case study in the Tuolumne Basin, USATongrui Chen0Lingmei Jiang1Jianwei Yang2Dan Kong3Yong Pang4State Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing, People’s Republic of ChinaState Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing, People’s Republic of ChinaState Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing, People’s Republic of ChinaKey Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, People’s Republic of ChinaKey Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, People’s Republic of ChinaMost of the Earth’s snow mass is stored in the Northern Hemisphere mountains, making accurate quantitative estimates of mountainous snow mass crucial for understanding the global hydrological cycle. The ICESat-2 satellite with a high-resolution laser altimeter enables detailed snow distribution mapping but faces uncertainties in snow depth estimation below the 100-m scale in mountains. To address this challenge, we improved the ICESat-2 ATL03 snow depth estimation by applying filters for slope and photon coverage in the spatial aggregation from 20-m to 1-km scales, considering the influences of photon density, snow spatial heterogeneity, and ICESat-2 elevation errors in steep terrains. Validation in the Tuolumne River Basin, USA, demonstrated a significant improvement in the accuracy of snow depth estimation. The mean absolute error (MAE) was 0.31 m for all slopes and 0.17 m for slopes less than 10° at the 20-m scale, indicating a 40% increase in accuracy compared with previous studies. As the snow depth was aggregated to 1 km, the MAE respectively decreased to 0.21 and 0.13 m. This study examined the potential of using ICESat-2 for estimating snow depth in mountainous regions across various spatial scales, providing more reliable snow depth estimates in data-scarce mountainous regions.https://www.tandfonline.com/doi/10.1080/17538947.2025.2505628ICESat-2ATL03ATL06-SRATL08snow depth |
| spellingShingle | Tongrui Chen Lingmei Jiang Jianwei Yang Dan Kong Yong Pang An improvement in ICESat-2 ATL03 snow depth estimation: a case study in the Tuolumne Basin, USA International Journal of Digital Earth ICESat-2 ATL03 ATL06-SR ATL08 snow depth |
| title | An improvement in ICESat-2 ATL03 snow depth estimation: a case study in the Tuolumne Basin, USA |
| title_full | An improvement in ICESat-2 ATL03 snow depth estimation: a case study in the Tuolumne Basin, USA |
| title_fullStr | An improvement in ICESat-2 ATL03 snow depth estimation: a case study in the Tuolumne Basin, USA |
| title_full_unstemmed | An improvement in ICESat-2 ATL03 snow depth estimation: a case study in the Tuolumne Basin, USA |
| title_short | An improvement in ICESat-2 ATL03 snow depth estimation: a case study in the Tuolumne Basin, USA |
| title_sort | improvement in icesat 2 atl03 snow depth estimation a case study in the tuolumne basin usa |
| topic | ICESat-2 ATL03 ATL06-SR ATL08 snow depth |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2505628 |
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