TY  - Data
T1  - Monthly scale glacier surface velocity dataset in Gangrigab area in 2023
A1  - Wu Kunpeng
A1  - 
PY  - 2024
DA  - 2024-12-30
PB  - National Cryosphere Desert Data Center
AB  - The 2023 monthly scale glacier surface velocity dataset in Gangrigab area is a high spatiotemporal resolution data product generated by fusing Landsat, Sentinel-1, Sentinel-2 remote sensing images and drone images using weighted least squares method. This dataset covers the Gangrigab area, with a spatial range of 29 ° 00 ′ to 29 ° 30 ′ north latitude and 96 ° 20 ′ to 97 ° 00 ′ east longitude. The time range is from January to December 2023, with a spatial resolution of 30 meters. It contains 12 velocity grid files named after months, with a file naming format of "YYYYMM. tif". The dataset reflects the trend of glacier surface velocity changes on a monthly basis, providing important data support for studying regional climate change, glacier movement characteristics, and related cryosphere scientific issues. The generation of this dataset is based on multi-source remote sensing image fusion technology. Landsat imagery provides fundamental data due to its wide coverage and high optical quality; The SAR image of Sentinel-1 overcomes the limitations of cloud and fog interference and performs well in regional dynamic monitoring; Sentinel-2 provides optical data with high spatial resolution; As high-precision ground reference data, drone images provide a reliable basis for calculating image fusion weights and verifying accuracy. This dataset not only fills the gap in spatial resolution of existing global glacier datasets in the Gangrigab region, but also significantly improves the accuracy and reliability of glacier surface velocity monitoring through the combination of multi-source data. In addition, the data has undergone uncertainty assessment and accuracy verification, with an error of 0.0098 m/d. Comparison with unmanned aerial vehicle observation data shows a significant reduction in mean deviation and a stabilization of standard deviation. The fused data exhibits excellent performance in noise control, smoothing processing, and accuracy improvement. The results of t
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/12a6d873-a5fd-48d4-8921-84a6397eb110
ER  -