2023年岗日嘎布地区月尺度冰川表面运动速度数据集是基于Landsat、Sentinel-1、Sentinel-2遥感影像以及无人机影像，通过加权最小二乘法融合生成的高时空分辨率数据产品。本数据集覆盖岗日嘎布地区，空间范围为北纬29°00′至29°30′，东经96°20′至97°00′，时间范围为2023年1月至12月，空间分辨率为30米，包含12个以月份命名的速度栅格文件，文件命名格式为“YYYYMM.tif”。数据集以逐月形式反映冰川表面运动速度的变化趋势，为研究区域气候变化、冰川运动特征及相关冰冻圈科学问题提供了重要的数据支撑。
\n本数据集的生成基于多源遥感影像融合技术。Landsat影像以其覆盖范围广和光学质量高的优势提供了基础数据；Sentinel-1的SAR影像克服了云雾干扰的限制，在区域动态监测方面表现出色；Sentinel-2提供了高空间分辨率的光学数据；无人机影像作为高精度地面参考数据，为影像融合权重的计算和精度验证提供了可靠依据。本数据集不仅填补了现有全球冰川数据集在岗日嘎布地区空间分辨率不足的空白，同时通过多源数据的结合，显著提高了冰川表面运动速度监测的精度和可靠性。
\n此外，数据经过不确定性评估及精度验证，误差为0.0098 m/d，与无人机观测数据的对比表明其均值偏差显著降低，标准差趋于稳定。融合后的数据在噪声控制、平滑处理和精度提升方面表现优异。本数据集的成果为研究青藏高原东南部的气候变化和冰川动态提供了宝贵的数据支持，同时也为冰川遥感监测技术的发展提供了新的思路和方法。
", "ds_source": "本数据集的原始数据来源于多种遥感影像和观测数据，涵盖Landsat OLI、Sentinel-1 GRD、Sentinel-2 MSI影像以及无人机获取的高精度影像。Landsat OLI影像的第8波段数据空间分辨率为15米，主要从美国地质勘探局（USGS）的开放平台获取，选取的影像已经过系统几何校正（C2L1产品），确保其适合直接进行冰川运动速度计算。影像的时间选择以每月初和月底的影像对为主，尽量减少云雾干扰，必要时延长时间间隔以获取质量较高的影像对。
\nSentinel-1 GRD影像为雷达图像，采用IW模式地距探测数据（GRD），分辨率为5×20米，主要从欧空局哥白尼数据中心下载。这些影像克服了高海拔地区光学遥感常见的云雾干扰问题，为冰川表面运动速度的时间序列构建提供了重要支持。由于Sentinel-1卫星的重访周期为12天，本研究通过时间基线设置（24天或36天）生成月尺度的影像对，确保了数据的时序性和完整性。
\nSentinel-2 MSI影像的第8波段为近红外波段，空间分辨率为10米，从欧空局哥白尼计划中获取。选择影像的标准与Landsat类似，以每月的云量较低影像为主，尽可能提高数据质量。无人机影像采用大疆M300 RTK无人机搭载睿铂M6 Pros量测型相机获取，覆盖研究区的部分冰川末端区域，航区面积约为30平方公里。无人机影像用于计算融合权重以及验证融合结果的精度，具有极高的空间分辨率和几何精度。
\n同时，研究还参考了岗日嘎布地区冰川分布数据集和RGI 7.0全球冰川编目，作为研究区范围的参考依据，为影像数据的区域性筛选和结果解释提供支持。
", "ds_process_way": "本数据集采用多源数据融合技术，以生成高精度、高时空分辨率的冰川表面运动速度数据。数据加工分为影像预处理、运动速度计算、融合权重计算、数据融合及精度验证五个主要步骤。
\n影像预处理包括几何校正、影像配准和噪声去除等。对于Landsat和Sentinel-2影像，使用COSI-CORR软件提取冰川运动速度，采用频率域互相关算法，设置搜索窗口为32×32像素，步长分别为2像素和3像素，相关系数阈值为0.95。对于Sentinel-1影像，则采用SNAP软件中的offset-tracking功能，设置匹配窗口为128×128像素，生成空间分辨率为30米的速度图。无人机影像的运动速度提取使用ImGRAFT工具箱，通过图像配准和特征追踪算法获取结果，并重采样至30米分辨率。
\n融合权重计算基于加权最小二乘法。选取同时期的Landsat、Sentinel-1和Sentinel-2影像速度数据，对无人机影像速度结果进行拟合，计算各数据源的权重系数（WL8、WS1、WS2）。然后，对所有影像的速度数据进行加权平均融合。为处理像元空值问题，引入掩膜函数调整权重分配，确保融合过程的稳定性。
\n数据融合完成后，采用无冰区残余运动速度作为误差估计的基础，通过计算其均值和标准差量化数据的不确定性。此外，与无人机影像计算的运动速度结果作差，评估融合数据的精度，最终验证数据集的可靠性。
", "ds_quality": "数据质量评估贯穿本数据集的生产全过程，以确保其可靠性和适用性。融合数据的误差通过无冰区的残余运动速度进行估算。结果表明，无冰区的残余运动速度误差为0.0098 m/d，且误差值呈正态分布。这一误差显著低于冰川的实际运动速度，表明数据集具备较高的精度。
\n为了进一步验证数据质量，本研究将融合结果与无人机影像计算得到的冰川表面运动速度进行对比分析。融合结果与无人机影像的速度差值的均值和标准差显示，融合数据的偏差显著降低，标准差趋于稳定，表明融合过程有效减少了噪声。与单一数据源相比，融合后的数据在平滑处理和异常值控制方面表现优异。
\n此外，本数据集采用严格的质量控制流程，包括影像的几何精度校正、速度提取算法优化以及多源数据的权重计算方法，确保了数据的一致性和可信性。融合数据不仅成功捕捉了岗日嘎布地区冰川表面运动的空间差异性，还提供了连续的时间序列，为后续研究提供了坚实的数据基础。总体而言，本数据集在精度、可靠性和实用性方面均达到了较高水平。
", "ds_acq_start_time": "2023-01-01 00:00:00", "ds_acq_end_time": "2023-12-31 00:00:00", "ds_acq_place": "岗日嘎布地区位于青藏高原东南部，呈北西至南东走向，与波密、墨脱、察隅和八宿毗邻。该地区的经纬度范围大约在29°00′N至29°30′N，以及96°20′E至97°00′E之间。", "ds_acq_lon_east": 97.0, "ds_acq_lat_south": 29.0, "ds_acq_lon_west": 96.5, "ds_acq_lat_north": 29.5, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 53481470, "ds_files_count": 2, "ds_format": "数据命名格式为YYYYMM.tif。YYYY为年份，MM为月份", "ds_space_res": "30米", "ds_time_res": "month", "ds_coordinate": "WGS84", "ds_projection": "WGS_1984_UTM_Zone_47N", "ds_thumbnail": "b03c8c75-3ca2-41f0-a553-5358890914ad.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "9de89acc-5714-4927-aba3-ac88067dff8a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "lihongxing@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170" ], "quality_level": 3, "publish_time": "2024-12-30 15:16:38", "last_updated": "2026-01-13 09:14:52", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6869.2025", "i18n": { "en": { "title": "Monthly scale glacier surface velocity dataset in Gangrigab area in 2023", "ds_format": "*.tif", "ds_source": "The raw data of this dataset comes from various remote sensing images and observation data, including Landsat OLI, Sentinel-1 GRD, Sentinel-2 MSI images, as well as high-precision images obtained by drones. The spatial resolution of the 8th band data of Landsat OLI images is 15 meters, mainly obtained from the open platform of the United States Geological Survey (USGS). The selected images have undergone systematic geometric correction (C2L1 product) to ensure their suitability for direct glacier movement velocity calculation. The time selection of images is mainly based on the image pairs at the beginning and end of each month, minimizing cloud and fog interference. If necessary, the time interval can be extended to obtain high-quality image pairs.
\nThe Sentinel-1 GRD image is a radar image, using IW mode ground distance detection data (GRD) with a resolution of 5 × 20 meters, mainly downloaded from the European Space Agency's Copernicus data center. These images overcome the common problem of cloud and fog interference in optical remote sensing in high-altitude areas, providing important support for the construction of time series of glacier surface movement velocity. Due to the revisit period of Sentinel-1 satellite being 12 days, this study generated monthly scale image pairs through time baseline setting (24 or 36 days) to ensure the temporal and complete nature of the data.
\nThe eighth band of Sentinel-2 MSI image is the near-infrared band with a spatial resolution of 10 meters, obtained from the European Space Agency's Copernicus program. The criteria for selecting images are similar to Landsat, focusing on low cloud cover images per month to maximize data quality. The drone images were obtained using the DJI M300 RTK drone equipped with the Ruibo M6 Pros measurement camera, covering some of the glacier end areas in the study area, with a navigation area of approximately 30 square kilometers. Drone images are used to calculate fusion weights and verify the accuracy of fusion results, with extremely high spatial resolution and geometric precision.
\nAt the same time, the study also referred to the glacier distribution dataset in the Gangrigab area and the RGI 7.0 global glacier catalog as a reference for the study area, providing support for regional screening of image data and interpretation of results.
", "ds_quality": "Data quality assessment runs through the entire production process of this dataset to ensure its reliability and applicability. The error of fusion data is estimated by the residual motion velocity of the ice free area. The results indicate that the residual velocity error in the ice free area is 0.0098 m/d, and the error values are normally distributed. This error is significantly lower than the actual movement speed of glaciers, indicating that the dataset has high accuracy.
\nIn order to further verify the data quality, this study will compare and analyze the fusion results with the glacier surface motion velocity calculated from unmanned aerial vehicle images. The mean and standard deviation of the speed difference between the fusion result and the drone image show that the deviation of the fusion data is significantly reduced, and the standard deviation tends to stabilize, indicating that the fusion process effectively reduces noise. Compared with a single data source, the fused data performs well in smoothing and outlier control.
\nIn addition, this dataset adopts strict quality control processes, including geometric accuracy correction of images, optimization of velocity extraction algorithms, and weight calculation methods for multi-source data, ensuring the consistency and reliability of the data. The fusion data not only successfully captured the spatial differences in glacier surface movement in the Gangrigab area, but also provided a continuous time series, providing a solid data foundation for subsequent research. Overall, this dataset has achieved a high level of accuracy, reliability, and practicality.
", "ds_ref_way": "", "ds_abstract": "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.
\nThe 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.
\nIn 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 this dataset provide valuable data support for studying climate change and glacier dynamics in the southeastern part of the Qinghai Tibet Plateau, and also provide new ideas and methods for the development of glacier remote sensing monitoring technology.
", "ds_time_res": "month", "ds_acq_place": "The Gangrigab area is located in the southeastern part of the Qinghai Tibet Plateau, running northwest to southeast and adjacent to Bomi, Motuo, Chayu, and Basu. The latitude and longitude range of the region is approximately between 29 ° 00'N and 29 ° 30'N, as well as between 96 ° 20'E and 97 ° 00'E.", "ds_space_res": "30米", "ds_projection": "WGS_1984_UTM_Zone_47N", "ds_process_way": "This dataset adopts multi-source data fusion technology to generate high-precision and high spatiotemporal resolution glacier surface velocity data. Data processing consists of five main steps: image preprocessing, motion speed calculation, fusion weight calculation, data fusion, and accuracy verification.
\nImage preprocessing includes geometric correction, image registration, and noise removal. For Landsat and Sentinel-2 images, COSI-CORR software was used to extract glacier movement velocity, and frequency domain cross-correlation algorithm was adopted. The search window was set to 32 × 32 pixels, with step sizes of 2 pixels and 3 pixels, respectively, and a correlation coefficient threshold of 0.95. For Sentinel-1 images, the offset tracking function in SNAP software is used to set the matching window to 128 × 128 pixels and generate a velocity map with a spatial resolution of 30 meters. The motion speed extraction of drone images is performed using the ImGRAFT toolbox, and the results are obtained through image registration and feature tracking algorithms, and resampled to a resolution of 30 meters.
\nThe fusion weight calculation is based on weighted least squares method. Select Landsat, Sentinel-1, and Sentinel-2 image velocity data from the same period, fit the drone image velocity results, and calculate the weight coefficients (WL8, WS1, WS2) for each data source. Then, perform weighted average fusion on the velocity data of all images. To address the issue of pixel null values, a mask function is introduced to adjust weight allocation and ensure the stability of the fusion process.
\nAfter the data fusion is completed, the residual motion velocity of the ice free area is used as the basis for error estimation, and the uncertainty of the data is quantified by calculating its mean and standard deviation. In addition, the accuracy of the fused data is evaluated by subtracting the motion speed results calculated from the drone images, ultimately verifying the reliability of the dataset.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "冰川" ], "ds_subject_tags": [ "地球科学" ], "ds_class_tags": [], "ds_locus_tags": [ "西藏" ], "ds_time_tags": [ 2023 ], "ds_contributors": [ { "true_name": "吴坤鹏", "email": "wukunpeng@ynu.edu.cn", "work_for": "云南大学", "country": "中国" }, { "true_name": "高道勋", "email": "gdaoxun0108@163.com", "work_for": "云南大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "吴坤鹏", "email": "wukunpeng@ynu.edu.cn", "work_for": "云南大学", "country": "中国" }, { "true_name": "高道勋", "email": "gaodaoxun@stu.ynu.edu.cn", "work_for": "云南大学", "country": "中国" } ], "ds_managers": [ { "true_name": "吴坤鹏", "email": "wukunpeng@ynu.edu.cn", "work_for": "云南大学", "country": "中国" } ], "category": "冰川" }