Estimating Fractional Snow Cover in the Pan-Arctic Region Using Added Vegetation Extraction Algorithm
Snow cover is an essential indicator of global climate change. The composition of the underlying surface in the Pan-Arctic region is complex; forest and other areas with high vegetation coverage have a significant influence on the retrieval accuracy of fractional snow cover (FSC). Therefore, to expl...
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ftdoajarticles:oai:doaj.org/article:d982c80d84b2445eb83753a488580353 2023-05-15T15:00:56+02:00 Estimating Fractional Snow Cover in the Pan-Arctic Region Using Added Vegetation Extraction Algorithm Yuan Ma Donghang Shao Jian Wang Haojie Li Hongyu Zhao Wenzheng Ji 2023-01-01T00:00:00Z https://doi.org/10.3390/rs15030775 https://doaj.org/article/d982c80d84b2445eb83753a488580353 EN eng MDPI AG https://www.mdpi.com/2072-4292/15/3/775 https://doaj.org/toc/2072-4292 doi:10.3390/rs15030775 2072-4292 https://doaj.org/article/d982c80d84b2445eb83753a488580353 Remote Sensing, Vol 15, Iss 775, p 775 (2023) fractional snow cover BV-BLRM model vegetation classification of validation Science Q article 2023 ftdoajarticles https://doi.org/10.3390/rs15030775 2023-02-12T01:25:51Z Snow cover is an essential indicator of global climate change. The composition of the underlying surface in the Pan-Arctic region is complex; forest and other areas with high vegetation coverage have a significant influence on the retrieval accuracy of fractional snow cover (FSC). Therefore, to explore the impact of vegetation on the extraction of the FSC algorithm, this study developed the normalized difference vegetation index (NDVI)-based Bivariate Linear Regression Model (BV-BLRM) to calculate the FSC. Then, the overall accuracy of the model and its changes under different classification conditions were evaluated and the relationship between the accuracy improvement and different underlying surfaces and elevations was analyzed. The results show that the BV-BLRM model is more accurate than MODIS’s traditional univariate linear algorithm for FSC (MOD-FSC) in each underlying surface. Overall, regarding the accuracy of the BV-BLRM model, the RMSE is 0.2, MAE is 0.15, and accuracy is 28.6% higher than the MOD-FSC model. The newly developed BV-BLRM model has the most significant improvement in the accuracy of FSC retrieval when the underlying surface has high vegetation coverage. Under different classification accuracies, the accuracy of BV-BLRM model was higher than that of MOD-FSC model, with an average of 30.5%. The improvement of FSC extraction accuracy by the model is smaller when the underlying surface is perpetual snow zone, with an average of 12.2%. This study is applicable to the scale mapping of FSC in large areas and is helpful to improve the FSC accuracy in areas with high vegetation coverage. Article in Journal/Newspaper Arctic Climate change Directory of Open Access Journals: DOAJ Articles Arctic Remote Sensing 15 3 775 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
fractional snow cover BV-BLRM model vegetation classification of validation Science Q |
spellingShingle |
fractional snow cover BV-BLRM model vegetation classification of validation Science Q Yuan Ma Donghang Shao Jian Wang Haojie Li Hongyu Zhao Wenzheng Ji Estimating Fractional Snow Cover in the Pan-Arctic Region Using Added Vegetation Extraction Algorithm |
topic_facet |
fractional snow cover BV-BLRM model vegetation classification of validation Science Q |
description |
Snow cover is an essential indicator of global climate change. The composition of the underlying surface in the Pan-Arctic region is complex; forest and other areas with high vegetation coverage have a significant influence on the retrieval accuracy of fractional snow cover (FSC). Therefore, to explore the impact of vegetation on the extraction of the FSC algorithm, this study developed the normalized difference vegetation index (NDVI)-based Bivariate Linear Regression Model (BV-BLRM) to calculate the FSC. Then, the overall accuracy of the model and its changes under different classification conditions were evaluated and the relationship between the accuracy improvement and different underlying surfaces and elevations was analyzed. The results show that the BV-BLRM model is more accurate than MODIS’s traditional univariate linear algorithm for FSC (MOD-FSC) in each underlying surface. Overall, regarding the accuracy of the BV-BLRM model, the RMSE is 0.2, MAE is 0.15, and accuracy is 28.6% higher than the MOD-FSC model. The newly developed BV-BLRM model has the most significant improvement in the accuracy of FSC retrieval when the underlying surface has high vegetation coverage. Under different classification accuracies, the accuracy of BV-BLRM model was higher than that of MOD-FSC model, with an average of 30.5%. The improvement of FSC extraction accuracy by the model is smaller when the underlying surface is perpetual snow zone, with an average of 12.2%. This study is applicable to the scale mapping of FSC in large areas and is helpful to improve the FSC accuracy in areas with high vegetation coverage. |
format |
Article in Journal/Newspaper |
author |
Yuan Ma Donghang Shao Jian Wang Haojie Li Hongyu Zhao Wenzheng Ji |
author_facet |
Yuan Ma Donghang Shao Jian Wang Haojie Li Hongyu Zhao Wenzheng Ji |
author_sort |
Yuan Ma |
title |
Estimating Fractional Snow Cover in the Pan-Arctic Region Using Added Vegetation Extraction Algorithm |
title_short |
Estimating Fractional Snow Cover in the Pan-Arctic Region Using Added Vegetation Extraction Algorithm |
title_full |
Estimating Fractional Snow Cover in the Pan-Arctic Region Using Added Vegetation Extraction Algorithm |
title_fullStr |
Estimating Fractional Snow Cover in the Pan-Arctic Region Using Added Vegetation Extraction Algorithm |
title_full_unstemmed |
Estimating Fractional Snow Cover in the Pan-Arctic Region Using Added Vegetation Extraction Algorithm |
title_sort |
estimating fractional snow cover in the pan-arctic region using added vegetation extraction algorithm |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://doi.org/10.3390/rs15030775 https://doaj.org/article/d982c80d84b2445eb83753a488580353 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change |
genre_facet |
Arctic Climate change |
op_source |
Remote Sensing, Vol 15, Iss 775, p 775 (2023) |
op_relation |
https://www.mdpi.com/2072-4292/15/3/775 https://doaj.org/toc/2072-4292 doi:10.3390/rs15030775 2072-4292 https://doaj.org/article/d982c80d84b2445eb83753a488580353 |
op_doi |
https://doi.org/10.3390/rs15030775 |
container_title |
Remote Sensing |
container_volume |
15 |
container_issue |
3 |
container_start_page |
775 |
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1766332983156932608 |