A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning
ABSTRACTA high-quality snow depth product is very import for cryospheric science and its related disciplines. Current long time-series snow depth products covering the Northern Hemisphere can be divided into two categories: remote sensing snow depth products and reanalysis snow depth products. Howev...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-03-01
|
| Series: | Big Earth Data |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/20964471.2023.2177435 |
| _version_ | 1797871082998333440 |
|---|---|
| author | Yanxing Hu Tao Che Liyun Dai Yu Zhu Lin Xiao Jie Deng Xin Li |
| author_facet | Yanxing Hu Tao Che Liyun Dai Yu Zhu Lin Xiao Jie Deng Xin Li |
| author_sort | Yanxing Hu |
| collection | DOAJ |
| description | ABSTRACTA high-quality snow depth product is very import for cryospheric science and its related disciplines. Current long time-series snow depth products covering the Northern Hemisphere can be divided into two categories: remote sensing snow depth products and reanalysis snow depth products. However, existing gridded snow depth products have some shortcomings. Remote sensing-derived snow depth products are temporally and spatially discontinuous and tend to underestimate snow depth, while reanalysis snow depth products have coarse spatial resolutions and great uncertainties. To overcome these problems, in our previous work we proposed a novel data fusion framework based on Random Forest Regression of snow products from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Advanced Microwave Scanning Radiometer-2 (AMSR2), Global Snow Monitoring for Climate Research (GlobSnow), the Northern Hemisphere Snow Depth (NHSD), ERA-Interim, and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), incorporating geolocation (latitude and longitude), and topographic data (elevation), which were used as input independent variables. More than 30,000 ground observation sites were used as the dependent variable to train and validate the model in different time periods. This fusion framework resulted in a long time series of continuous daily snow depth product over the Northern Hemisphere with a spatial resolution of 0.25°. Here, we compared the fused snow depth and the original gridded snow depth products with 13,272 observation sites, showing an improved precision of our product. The evaluation indices of the fused (best original) dataset yielded a coefficient of determination R2 of 0.81 (0.23), Root Mean Squared Error (RMSE) of 7.69 (15.86) cm, and Mean Absolute Error (MAE) of 2.74 (6.14) cm. Most of the bias (88.31%) between the fused snow depth and in situ observations was in the range of −5 cm to 5 cm. The accuracy assessment of independent snow observation sites – Sodankylä (SOD), Old Aspen (OAS), Old Black Spruce (OBS), and Old Jack Pine (OJP) – showed that the fused snow depth dataset had high precision for snow depths of less than 100 cm with a relatively homogeneous surrounding environment. The results of random point selection and independent in situ site validation show that the accuracy of the fused snow depth product is not significantly improved in deep snow areas and areas with complex terrain. In the altitude range of 100 m to 2000 m, the fused snow depth had a higher precision, with R2 varying from 0.73 to 0.86. The fused snow depth had a decreasing trend based on the spatiotemporal analysis and Mann-Kendall trend test method. This fused snow depth product provides the basis for understanding the temporal and spatial characteristics of snow cover and their relation to climate change, hydrological and water cycle, water resource management, ecological environment, snow disaster and hazard prevention. |
| first_indexed | 2024-04-10T00:37:32Z |
| format | Article |
| id | doaj.art-841b053022924b6fb7477215ea06e0e4 |
| institution | Directory Open Access Journal |
| issn | 2096-4471 2574-5417 |
| language | English |
| last_indexed | 2024-04-10T00:37:32Z |
| publishDate | 2023-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Big Earth Data |
| spelling | doaj.art-841b053022924b6fb7477215ea06e0e42023-03-14T09:10:25ZengTaylor & Francis GroupBig Earth Data2096-44712574-54172023-03-0112810.1080/20964471.2023.2177435A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learningYanxing Hu0Tao Che1Liyun Dai2Yu Zhu3Lin Xiao4Jie Deng5Xin Li6Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, ChinaKey Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, ChinaKey Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, ChinaYunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Institute of International Rivers and Eco-Security, Yunnan University, Kunming, ChinaNational Forestry and Grassland Administration Key Laboratory of Forest Resource Conservation and Ecological Safety on the Upper Reaches of the Yangtze River, Sichuan Province Key Laboratory of Ecological Forestry Engineering on the Upper Reaches of the Yangtze River, College of Forestry, Sichuan Agricultural University, Chengdu, ChinaKey Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, ChinaState Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, ChinaABSTRACTA high-quality snow depth product is very import for cryospheric science and its related disciplines. Current long time-series snow depth products covering the Northern Hemisphere can be divided into two categories: remote sensing snow depth products and reanalysis snow depth products. However, existing gridded snow depth products have some shortcomings. Remote sensing-derived snow depth products are temporally and spatially discontinuous and tend to underestimate snow depth, while reanalysis snow depth products have coarse spatial resolutions and great uncertainties. To overcome these problems, in our previous work we proposed a novel data fusion framework based on Random Forest Regression of snow products from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Advanced Microwave Scanning Radiometer-2 (AMSR2), Global Snow Monitoring for Climate Research (GlobSnow), the Northern Hemisphere Snow Depth (NHSD), ERA-Interim, and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), incorporating geolocation (latitude and longitude), and topographic data (elevation), which were used as input independent variables. More than 30,000 ground observation sites were used as the dependent variable to train and validate the model in different time periods. This fusion framework resulted in a long time series of continuous daily snow depth product over the Northern Hemisphere with a spatial resolution of 0.25°. Here, we compared the fused snow depth and the original gridded snow depth products with 13,272 observation sites, showing an improved precision of our product. The evaluation indices of the fused (best original) dataset yielded a coefficient of determination R2 of 0.81 (0.23), Root Mean Squared Error (RMSE) of 7.69 (15.86) cm, and Mean Absolute Error (MAE) of 2.74 (6.14) cm. Most of the bias (88.31%) between the fused snow depth and in situ observations was in the range of −5 cm to 5 cm. The accuracy assessment of independent snow observation sites – Sodankylä (SOD), Old Aspen (OAS), Old Black Spruce (OBS), and Old Jack Pine (OJP) – showed that the fused snow depth dataset had high precision for snow depths of less than 100 cm with a relatively homogeneous surrounding environment. The results of random point selection and independent in situ site validation show that the accuracy of the fused snow depth product is not significantly improved in deep snow areas and areas with complex terrain. In the altitude range of 100 m to 2000 m, the fused snow depth had a higher precision, with R2 varying from 0.73 to 0.86. The fused snow depth had a decreasing trend based on the spatiotemporal analysis and Mann-Kendall trend test method. This fused snow depth product provides the basis for understanding the temporal and spatial characteristics of snow cover and their relation to climate change, hydrological and water cycle, water resource management, ecological environment, snow disaster and hazard prevention.https://www.tandfonline.com/doi/10.1080/20964471.2023.2177435Snow depth datasetsmachine learningdata fusionNorthern Hemisphere |
| spellingShingle | Yanxing Hu Tao Che Liyun Dai Yu Zhu Lin Xiao Jie Deng Xin Li A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning Big Earth Data Snow depth datasets machine learning data fusion Northern Hemisphere |
| title | A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning |
| title_full | A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning |
| title_fullStr | A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning |
| title_full_unstemmed | A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning |
| title_short | A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning |
| title_sort | long term daily gridded snow depth dataset for the northern hemisphere from 1980 to 2019 based on machine learning |
| topic | Snow depth datasets machine learning data fusion Northern Hemisphere |
| url | https://www.tandfonline.com/doi/10.1080/20964471.2023.2177435 |
| work_keys_str_mv | AT yanxinghu alongtermdailygriddedsnowdepthdatasetforthenorthernhemispherefrom1980to2019basedonmachinelearning AT taoche alongtermdailygriddedsnowdepthdatasetforthenorthernhemispherefrom1980to2019basedonmachinelearning AT liyundai alongtermdailygriddedsnowdepthdatasetforthenorthernhemispherefrom1980to2019basedonmachinelearning AT yuzhu alongtermdailygriddedsnowdepthdatasetforthenorthernhemispherefrom1980to2019basedonmachinelearning AT linxiao alongtermdailygriddedsnowdepthdatasetforthenorthernhemispherefrom1980to2019basedonmachinelearning AT jiedeng alongtermdailygriddedsnowdepthdatasetforthenorthernhemispherefrom1980to2019basedonmachinelearning AT xinli alongtermdailygriddedsnowdepthdatasetforthenorthernhemispherefrom1980to2019basedonmachinelearning AT yanxinghu longtermdailygriddedsnowdepthdatasetforthenorthernhemispherefrom1980to2019basedonmachinelearning AT taoche longtermdailygriddedsnowdepthdatasetforthenorthernhemispherefrom1980to2019basedonmachinelearning AT liyundai longtermdailygriddedsnowdepthdatasetforthenorthernhemispherefrom1980to2019basedonmachinelearning AT yuzhu longtermdailygriddedsnowdepthdatasetforthenorthernhemispherefrom1980to2019basedonmachinelearning AT linxiao longtermdailygriddedsnowdepthdatasetforthenorthernhemispherefrom1980to2019basedonmachinelearning AT jiedeng longtermdailygriddedsnowdepthdatasetforthenorthernhemispherefrom1980to2019basedonmachinelearning AT xinli longtermdailygriddedsnowdepthdatasetforthenorthernhemispherefrom1980to2019basedonmachinelearning |