MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area
To improve the poor accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) daily fractional snow cover product over the complex terrain of the Tibetan Plateau (RMSE = 0.30), unmanned aerial vehicle and machine learning technologies are employed to map the fractional snow cover based o...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/6/962 |
| _version_ | 1819278179947249664 |
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| author | Changyu Liu Xiaodong Huang Xubing Li Tiangang Liang |
| author_facet | Changyu Liu Xiaodong Huang Xubing Li Tiangang Liang |
| author_sort | Changyu Liu |
| collection | DOAJ |
| description | To improve the poor accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) daily fractional snow cover product over the complex terrain of the Tibetan Plateau (RMSE = 0.30), unmanned aerial vehicle and machine learning technologies are employed to map the fractional snow cover based on MODIS over this terrain. Three machine learning models, including random forest, support vector machine, and back-propagation artificial neural network models, are trained and compared in this study. The results indicate that compared with the MODIS daily fractional snow cover product, the introduction of a highly accurate snow map acquired by unmanned aerial vehicles as a reference into machine learning models can significantly improve the MODIS fractional snow cover mapping accuracy. The random forest model shows the best accuracy among the three machine learning models, with an RMSE (root-mean-square error) of 0.23, especially over forestland and shrubland, with RMSEs of 0.13 and 0.18, respectively. Although the accuracy of the support vector machine and back-propagation artificial neural network models are worse over forestland and shrubland, their average errors are still better than that of MOD10A1. Different fractional snow cover gradients also affect the accuracy of the machine learning algorithms. Nevertheless, the random forest model remains stable in different fractional snow cover gradients and is, therefore, the best machine learning algorithm for MODIS fractional snow cover mapping in Tibetan Plateau areas with complex terrain and severely fragmented snow cover. |
| first_indexed | 2024-12-24T00:07:54Z |
| format | Article |
| id | doaj.art-4819361ccfb94b89b06c66712436a169 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T00:07:54Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4819361ccfb94b89b06c66712436a1692022-12-21T17:24:57ZengMDPI AGRemote Sensing2072-42922020-03-0112696210.3390/rs12060962rs12060962MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous AreaChangyu Liu0Xiaodong Huang1Xubing Li2Tiangang Liang3State Key Laboratory of Grassland Agro–Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, ChinaSchool of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaState Key Laboratory of Grassland Agro–Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, ChinaTo improve the poor accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) daily fractional snow cover product over the complex terrain of the Tibetan Plateau (RMSE = 0.30), unmanned aerial vehicle and machine learning technologies are employed to map the fractional snow cover based on MODIS over this terrain. Three machine learning models, including random forest, support vector machine, and back-propagation artificial neural network models, are trained and compared in this study. The results indicate that compared with the MODIS daily fractional snow cover product, the introduction of a highly accurate snow map acquired by unmanned aerial vehicles as a reference into machine learning models can significantly improve the MODIS fractional snow cover mapping accuracy. The random forest model shows the best accuracy among the three machine learning models, with an RMSE (root-mean-square error) of 0.23, especially over forestland and shrubland, with RMSEs of 0.13 and 0.18, respectively. Although the accuracy of the support vector machine and back-propagation artificial neural network models are worse over forestland and shrubland, their average errors are still better than that of MOD10A1. Different fractional snow cover gradients also affect the accuracy of the machine learning algorithms. Nevertheless, the random forest model remains stable in different fractional snow cover gradients and is, therefore, the best machine learning algorithm for MODIS fractional snow cover mapping in Tibetan Plateau areas with complex terrain and severely fragmented snow cover.https://www.mdpi.com/2072-4292/12/6/962modisfractinal snow coveruavtibetan plateau |
| spellingShingle | Changyu Liu Xiaodong Huang Xubing Li Tiangang Liang MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area Remote Sensing modis fractinal snow cover uav tibetan plateau |
| title | MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area |
| title_full | MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area |
| title_fullStr | MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area |
| title_full_unstemmed | MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area |
| title_short | MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area |
| title_sort | modis fractional snow cover mapping using machine learning technology in a mountainous area |
| topic | modis fractinal snow cover uav tibetan plateau |
| url | https://www.mdpi.com/2072-4292/12/6/962 |
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