Enhancing glacier monitoring through adaptive smoothing of MODIS NDSI time series
<p>Observation of glacier surface characteristics through remotely sensed time-series data is essential for understanding glacier seasonality, mass balance, and long-term trends. Yet, the reliability of these observations depends significantly on the quality of the time-series data. This study...
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| مؤلفون آخرون: | |
| منشور في: |
2024
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| _version_ | 1852026633568911360 |
|---|---|
| author | Chen Xin (704623) |
| author2 | Yongwei Sheng (837277) |
| author2_role | author |
| author_facet | Chen Xin (704623) Yongwei Sheng (837277) |
| author_role | author |
| dc.creator.none.fl_str_mv | Chen Xin (704623) Yongwei Sheng (837277) |
| dc.date.none.fl_str_mv | 2024-09-16T14:40:13Z |
| dc.identifier.none.fl_str_mv | 10.6084/m9.figshare.27037933.v1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Enhancing_glacier_monitoring_through_adaptive_smoothing_of_MODIS_NDSI_time_series/27037933 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Biotechnology Space Science showcasing significant improvements observations depends significantly cloud contamination reduction understanding glacier seasonality glacier surface condition glacier surface characteristics meticulous preprocessing scheme data gap handling remotely sensed time enhancing glacier monitoring glacier monitoring series data proposed scheme two glaciers term trends temporal resolution study presents seasonal fluctuations results affirm remote sensing reliable evaluation outlier removal methodology ’ median values mass balance challenges associated benchmark project adaptive smoothing achieve convergence |
| dc.title.none.fl_str_mv | Enhancing glacier monitoring through adaptive smoothing of MODIS NDSI time series |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Observation of glacier surface characteristics through remotely sensed time-series data is essential for understanding glacier seasonality, mass balance, and long-term trends. Yet, the reliability of these observations depends significantly on the quality of the time-series data. This study presents a meticulous preprocessing scheme to improve the quality of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Snow Index (NDSI) time-series data for glacier monitoring. We propose a three-step algorithm specifically crafted to overcome the challenges associated with cloud contamination reduction, outlier removal and data gap handling. This innovative approach iteratively compares the median values of automatically adjusted asymmetrical moving windows to achieve convergence, removing outliers using minimal window size to keep the temporal resolution as high as possible. The methodology’s effectiveness is demonstrated through its application to two glaciers from the United States Geological Survey (USGS) Benchmark Project, showcasing significant improvements in the quality of smoothed MODIS NDSI time series. These results affirm the efficacy of the proposed scheme in rendering a more reliable evaluation of glacier surface condition and seasonal fluctuations. Consequently, this study contributes significant methodological advancements to the fields of remote sensing and glaciology, enhancing the accuracy of glacier monitoring techniques.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_55e4a2b2cf82fc2ad3a53fcf4ee00e21 |
| identifier_str_mv | 10.6084/m9.figshare.27037933.v1 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27037933 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Enhancing glacier monitoring through adaptive smoothing of MODIS NDSI time seriesChen Xin (704623)Yongwei Sheng (837277)MedicineBiotechnologySpace Scienceshowcasing significant improvementsobservations depends significantlycloud contamination reductionunderstanding glacier seasonalityglacier surface conditionglacier surface characteristicsmeticulous preprocessing schemedata gap handlingremotely sensed timeenhancing glacier monitoringglacier monitoringseries dataproposed schemetwo glaciersterm trendstemporal resolutionstudy presentsseasonal fluctuationsresults affirmremote sensingreliable evaluationoutlier removalmethodology ’median valuesmass balancechallenges associatedbenchmark projectadaptive smoothingachieve convergence<p>Observation of glacier surface characteristics through remotely sensed time-series data is essential for understanding glacier seasonality, mass balance, and long-term trends. Yet, the reliability of these observations depends significantly on the quality of the time-series data. This study presents a meticulous preprocessing scheme to improve the quality of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Snow Index (NDSI) time-series data for glacier monitoring. We propose a three-step algorithm specifically crafted to overcome the challenges associated with cloud contamination reduction, outlier removal and data gap handling. This innovative approach iteratively compares the median values of automatically adjusted asymmetrical moving windows to achieve convergence, removing outliers using minimal window size to keep the temporal resolution as high as possible. The methodology’s effectiveness is demonstrated through its application to two glaciers from the United States Geological Survey (USGS) Benchmark Project, showcasing significant improvements in the quality of smoothed MODIS NDSI time series. These results affirm the efficacy of the proposed scheme in rendering a more reliable evaluation of glacier surface condition and seasonal fluctuations. Consequently, this study contributes significant methodological advancements to the fields of remote sensing and glaciology, enhancing the accuracy of glacier monitoring techniques.</p>2024-09-16T14:40:13ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.6084/m9.figshare.27037933.v1https://figshare.com/articles/figure/Enhancing_glacier_monitoring_through_adaptive_smoothing_of_MODIS_NDSI_time_series/27037933CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270379332024-09-16T14:40:13Z |
| spellingShingle | Enhancing glacier monitoring through adaptive smoothing of MODIS NDSI time series Chen Xin (704623) Medicine Biotechnology Space Science showcasing significant improvements observations depends significantly cloud contamination reduction understanding glacier seasonality glacier surface condition glacier surface characteristics meticulous preprocessing scheme data gap handling remotely sensed time enhancing glacier monitoring glacier monitoring series data proposed scheme two glaciers term trends temporal resolution study presents seasonal fluctuations results affirm remote sensing reliable evaluation outlier removal methodology ’ median values mass balance challenges associated benchmark project adaptive smoothing achieve convergence |
| status_str | publishedVersion |
| title | Enhancing glacier monitoring through adaptive smoothing of MODIS NDSI time series |
| title_full | Enhancing glacier monitoring through adaptive smoothing of MODIS NDSI time series |
| title_fullStr | Enhancing glacier monitoring through adaptive smoothing of MODIS NDSI time series |
| title_full_unstemmed | Enhancing glacier monitoring through adaptive smoothing of MODIS NDSI time series |
| title_short | Enhancing glacier monitoring through adaptive smoothing of MODIS NDSI time series |
| title_sort | Enhancing glacier monitoring through adaptive smoothing of MODIS NDSI time series |
| topic | Medicine Biotechnology Space Science showcasing significant improvements observations depends significantly cloud contamination reduction understanding glacier seasonality glacier surface condition glacier surface characteristics meticulous preprocessing scheme data gap handling remotely sensed time enhancing glacier monitoring glacier monitoring series data proposed scheme two glaciers term trends temporal resolution study presents seasonal fluctuations results affirm remote sensing reliable evaluation outlier removal methodology ’ median values mass balance challenges associated benchmark project adaptive smoothing achieve convergence |