Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau
Moderate-resolution imaging spectroradiometer (MODIS) snow-cover products have relatively low accuracy over the Tibetan Plateau because of its complex terrain and shallow, fragmented snow cover. In this study, fractional snow-cover (FSC) mapping algorithms were developed using a linear regression mo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/9/12/1332 |
| _version_ | 1819003198307827712 |
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| author | Hui Liang Xiaodong Huang Yanhua Sun Yunlong Wang Tiangang Liang |
| author_facet | Hui Liang Xiaodong Huang Yanhua Sun Yunlong Wang Tiangang Liang |
| author_sort | Hui Liang |
| collection | DOAJ |
| description | Moderate-resolution imaging spectroradiometer (MODIS) snow-cover products have relatively low accuracy over the Tibetan Plateau because of its complex terrain and shallow, fragmented snow cover. In this study, fractional snow-cover (FSC) mapping algorithms were developed using a linear regression model (LR), a linear spectral mixture analysis model (LSMA) and a back-propagation artificial neural network model (BP-ANN) based on MODIS data (version 006) and unmanned aerial vehicle (UAV) data. The accuracies of the three models were validated against Landsat 8 Operational Land Imager (OLI) snow-cover maps (Landsat 8 FSC) and compared with the MODIS global FSC product (MOD10A1 FSC, version 005) for the purpose of finding the optimal algorithm for FSC extraction for the Tibetan Plateau. The results showed that (1) the overall retrieval results of the LR and BP-ANN models based on MODIS and UAV data were relatively similar to the OLI snow-cover maps; the accuracy and stability were greatly improved, with even some reduction in errors; compared to the Landsat 8 FSC, the correlation coefficients (r) were 0.8222 and 0.8445 respectively and the root-mean-square errors (RMSEs) were 0.2304 and 0.2201, respectively. (2) The accuracy and stability of the fully constrained LSMA model using the pixel purity index (PPI) endmember extraction method based only on MODIS data suffered the worst performance of the three models; r was only 0.7921 and the RMSE was as large as 0.3485. There were some serious omission phenomena in the study area, specifically for the largest mean absolute error (MAE = 0.2755) and positive mean error (PME = 0.3411). (3) The accuracy of the MOD10A1 FSC product was much lower than that of the LR and BP-ANN models, although its accuracy slightly better that of the LSMA based on comprehensive evaluation of six accuracy indices. (4) The optimal model was the BP-ANN model with combined inputs of surface reflectivity data (R1–R7), elevation (DEM) and temperature (LST), which can easily incorporate auxiliary information (DEM and LST) on the basis of (R1–R7) during the relationship training period and can effectively improve the accuracy of snow area monitoring—it is the ideal algorithm for retrieving FSC for the Tibetan Plateau. |
| first_indexed | 2024-12-20T23:17:11Z |
| format | Article |
| id | doaj.art-d21194107cae4e25a10d60ca315b7fb1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T23:17:11Z |
| publishDate | 2017-12-01 |
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| spelling | doaj.art-d21194107cae4e25a10d60ca315b7fb12022-12-21T19:23:37ZengMDPI AGRemote Sensing2072-42922017-12-01912133210.3390/rs9121332rs9121332Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan PlateauHui Liang0Xiaodong Huang1Yanhua Sun2Yunlong Wang3Tiangang Liang4State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, ChinaState Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, ChinaCollege of Earth and Environment Sciences, Lanzhou University, Lanzhou 730000, ChinaState Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, ChinaState Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, ChinaModerate-resolution imaging spectroradiometer (MODIS) snow-cover products have relatively low accuracy over the Tibetan Plateau because of its complex terrain and shallow, fragmented snow cover. In this study, fractional snow-cover (FSC) mapping algorithms were developed using a linear regression model (LR), a linear spectral mixture analysis model (LSMA) and a back-propagation artificial neural network model (BP-ANN) based on MODIS data (version 006) and unmanned aerial vehicle (UAV) data. The accuracies of the three models were validated against Landsat 8 Operational Land Imager (OLI) snow-cover maps (Landsat 8 FSC) and compared with the MODIS global FSC product (MOD10A1 FSC, version 005) for the purpose of finding the optimal algorithm for FSC extraction for the Tibetan Plateau. The results showed that (1) the overall retrieval results of the LR and BP-ANN models based on MODIS and UAV data were relatively similar to the OLI snow-cover maps; the accuracy and stability were greatly improved, with even some reduction in errors; compared to the Landsat 8 FSC, the correlation coefficients (r) were 0.8222 and 0.8445 respectively and the root-mean-square errors (RMSEs) were 0.2304 and 0.2201, respectively. (2) The accuracy and stability of the fully constrained LSMA model using the pixel purity index (PPI) endmember extraction method based only on MODIS data suffered the worst performance of the three models; r was only 0.7921 and the RMSE was as large as 0.3485. There were some serious omission phenomena in the study area, specifically for the largest mean absolute error (MAE = 0.2755) and positive mean error (PME = 0.3411). (3) The accuracy of the MOD10A1 FSC product was much lower than that of the LR and BP-ANN models, although its accuracy slightly better that of the LSMA based on comprehensive evaluation of six accuracy indices. (4) The optimal model was the BP-ANN model with combined inputs of surface reflectivity data (R1–R7), elevation (DEM) and temperature (LST), which can easily incorporate auxiliary information (DEM and LST) on the basis of (R1–R7) during the relationship training period and can effectively improve the accuracy of snow area monitoring—it is the ideal algorithm for retrieving FSC for the Tibetan Plateau.https://www.mdpi.com/2072-4292/9/12/1332fractional snow-coverMODISUAVTibetan Plateau |
| spellingShingle | Hui Liang Xiaodong Huang Yanhua Sun Yunlong Wang Tiangang Liang Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau Remote Sensing fractional snow-cover MODIS UAV Tibetan Plateau |
| title | Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau |
| title_full | Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau |
| title_fullStr | Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau |
| title_full_unstemmed | Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau |
| title_short | Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau |
| title_sort | fractional snow cover mapping based on modis and uav data over the tibetan plateau |
| topic | fractional snow-cover MODIS UAV Tibetan Plateau |
| url | https://www.mdpi.com/2072-4292/9/12/1332 |
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