Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas
Long-term monitoring of snow cover is crucial for climatic and hydrological studies. The utility of long-term snow-cover products lies in their ability to record the real states of the earth's surface. Although a long-term, consistent snow product derived from the ESA CCI+ (Climate Change Initi...
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Copernicus Publications
2021
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Online Access: | https://doi.org/10.5194/tc-15-4261-2021 https://tc.copernicus.org/articles/15/4261/2021/tc-15-4261-2021.pdf https://doaj.org/article/05479c7b901746ac990133a630e77629 |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:05479c7b901746ac990133a630e77629 2023-05-15T18:32:19+02:00 Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas X. Wu K. Naegeli V. Premier C. Marin D. Ma J. Wang S. Wunderle 2021-09-01 https://doi.org/10.5194/tc-15-4261-2021 https://tc.copernicus.org/articles/15/4261/2021/tc-15-4261-2021.pdf https://doaj.org/article/05479c7b901746ac990133a630e77629 en eng Copernicus Publications doi:10.5194/tc-15-4261-2021 1994-0416 1994-0424 https://tc.copernicus.org/articles/15/4261/2021/tc-15-4261-2021.pdf https://doaj.org/article/05479c7b901746ac990133a630e77629 undefined The Cryosphere, Vol 15, Pp 4261-4279 (2021) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2021 fttriple https://doi.org/10.5194/tc-15-4261-2021 2023-01-22T18:10:38Z Long-term monitoring of snow cover is crucial for climatic and hydrological studies. The utility of long-term snow-cover products lies in their ability to record the real states of the earth's surface. Although a long-term, consistent snow product derived from the ESA CCI+ (Climate Change Initiative) AVHRR GAC (Advanced Very High Resolution Radiometer global area coverage) dataset dating back to the 1980s has been generated and released, its accuracy and consistency have not been extensively evaluated. Here, we extensively validate the AVHRR GAC snow-cover extent dataset for the mountainous Hindu Kush Himalayan (HKH) region due to its high importance for climate change impact and adaptation studies. The sensor-to-sensor consistency was first investigated using a snow dataset based on long-term in situ stations (1982–2013). Also, this includes a study on the dependence of AVHRR snow-cover accuracy related to snow depth. Furthermore, in order to increase the spatial coverage of validation and explore the influences of land-cover type, elevation, slope, aspect, and topographical variability in the accuracy of AVHRR snow extent, a comparison with Landsat Thematic Mapper (TM) data was included. Finally, the performance of the AVHRR GAC snow-cover dataset was also compared to the MODIS (MOD10A1 V006) product. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006. Using a ±3 d temporal filter caused a slight decrease in accuracy (from 94 % to 92 %). Validation against Landsat TM data over the area with a wide range of conditions (i.e., elevation, topography, and land cover) indicated overall root mean square errors (RMSEs) of about 13.27 % and 16 % and overall biases of about −5.83 % and −7.13 % for the AVHRR GAC raw and gap-filled snow datasets, respectively. It can be concluded that the here validated AVHRR GAC snow-cover climatology is a highly valuable and powerful dataset to assess environmental changes in the HKH region due to its good quality, ... Article in Journal/Newspaper The Cryosphere Unknown The Cryosphere 15 9 4261 4279 |
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geo envir X. Wu K. Naegeli V. Premier C. Marin D. Ma J. Wang S. Wunderle Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas |
topic_facet |
geo envir |
description |
Long-term monitoring of snow cover is crucial for climatic and hydrological studies. The utility of long-term snow-cover products lies in their ability to record the real states of the earth's surface. Although a long-term, consistent snow product derived from the ESA CCI+ (Climate Change Initiative) AVHRR GAC (Advanced Very High Resolution Radiometer global area coverage) dataset dating back to the 1980s has been generated and released, its accuracy and consistency have not been extensively evaluated. Here, we extensively validate the AVHRR GAC snow-cover extent dataset for the mountainous Hindu Kush Himalayan (HKH) region due to its high importance for climate change impact and adaptation studies. The sensor-to-sensor consistency was first investigated using a snow dataset based on long-term in situ stations (1982–2013). Also, this includes a study on the dependence of AVHRR snow-cover accuracy related to snow depth. Furthermore, in order to increase the spatial coverage of validation and explore the influences of land-cover type, elevation, slope, aspect, and topographical variability in the accuracy of AVHRR snow extent, a comparison with Landsat Thematic Mapper (TM) data was included. Finally, the performance of the AVHRR GAC snow-cover dataset was also compared to the MODIS (MOD10A1 V006) product. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006. Using a ±3 d temporal filter caused a slight decrease in accuracy (from 94 % to 92 %). Validation against Landsat TM data over the area with a wide range of conditions (i.e., elevation, topography, and land cover) indicated overall root mean square errors (RMSEs) of about 13.27 % and 16 % and overall biases of about −5.83 % and −7.13 % for the AVHRR GAC raw and gap-filled snow datasets, respectively. It can be concluded that the here validated AVHRR GAC snow-cover climatology is a highly valuable and powerful dataset to assess environmental changes in the HKH region due to its good quality, ... |
format |
Article in Journal/Newspaper |
author |
X. Wu K. Naegeli V. Premier C. Marin D. Ma J. Wang S. Wunderle |
author_facet |
X. Wu K. Naegeli V. Premier C. Marin D. Ma J. Wang S. Wunderle |
author_sort |
X. Wu |
title |
Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas |
title_short |
Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas |
title_full |
Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas |
title_fullStr |
Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas |
title_full_unstemmed |
Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas |
title_sort |
evaluation of snow extent time series derived from advanced very high resolution radiometer global area coverage data (1982–2018) in the hindu kush himalayas |
publisher |
Copernicus Publications |
publishDate |
2021 |
url |
https://doi.org/10.5194/tc-15-4261-2021 https://tc.copernicus.org/articles/15/4261/2021/tc-15-4261-2021.pdf https://doaj.org/article/05479c7b901746ac990133a630e77629 |
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The Cryosphere |
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The Cryosphere |
op_source |
The Cryosphere, Vol 15, Pp 4261-4279 (2021) |
op_relation |
doi:10.5194/tc-15-4261-2021 1994-0416 1994-0424 https://tc.copernicus.org/articles/15/4261/2021/tc-15-4261-2021.pdf https://doaj.org/article/05479c7b901746ac990133a630e77629 |
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op_doi |
https://doi.org/10.5194/tc-15-4261-2021 |
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The Cryosphere |
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15 |
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9 |
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4261 |
op_container_end_page |
4279 |
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