该数据是利用Modis产品数据，通过重采样、拼接等处理，得到柴达木盆地2000-2018年每年的平均积雪覆被率数据，通过对每个像元进行回归分析，得到2000-2018年柴达木地区1km分辨率积雪变化率图\n
本数据集主要应用于冰冻圈科学研究。", "ds_source": "
MOD10A1数据来自美国国家航空航天局(https://www.nasa.gov/)。", "ds_process_way": "
日积雪覆被率数据是在MODIS产品数据 MOD10A1的基础上，通过重采样、拼接、剪裁等预处理，采用基于三次样条函数插值的去云算法进行去云处理后得到。利用arcgis计算年平均积雪率并选取DEM大于3500m的区域进行掩膜处理，得到柴木盆地的年积雪覆被率数据。基于Python对每个像元进行线性回归分析，最终生成2000-2018年柴达木地区1km分辨率积雪变化率数据。", "ds_quality": "
a. 原始资料数据精度\n
MOD10A1(500m)产品数据包含积雪覆被率和质量评估（QA）等数据。积雪覆盖数据是基于采用归一化差异积雪指数（NDSI）和其他标准测试的积雪制图算法制成。\n
b. 项目数据产生和汇集过程中的相关质量控制措施，包括完整的数据产生过程、使用的方法和标准规范、数据应用范围等内容。\n
(1)数据生产过程: 对原始影像进行重采样、 拼接、剪裁等预处理，最终采用基于三次样条函数插值的去云算法进行去云处理后得到，利用arcgis计算年平均积雪率并选取DEM大于3500m的区域进行掩膜处理，得到柴木盆地的年积雪覆被率数据。基于Python对每个像元进行线性回归分析，最终生成2000-2018年柴达木地区1km分辨率积雪变化率图。\n
(2)方法和标准规范:重采样、拼接、剪裁、线性回归分析。\n
c. 加工后数据精度\n
在加工生成数据时，保留部分原始数据。", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2018-12-31 00:00:00", "ds_acq_place": "柴达木盆地", "ds_acq_lon_east": 99.25, "ds_acq_lat_south": 35.0, "ds_acq_lon_west": 90.25, "ds_acq_lat_north": 39.31666666666667, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 3192347, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "1km", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "ce2a7f0d-a3e9-4a6c-a75f-a1d0877b2615.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "4851e874-eafc-4879-812b-ffbdd825e967", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.Hydro.db2442.2022", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2022-09-29 11:01:35", "last_updated": "2025-04-29 16:01:26", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.Hydro.db2442.2022", "i18n": { "en": { "title": "Distribution map of 1km resolution snow cover change rate in Qaidam region (2000-2018)", "ds_format": "TIF", "ds_source": "
Mod10a1 data from NASA（ https://www.nasa.gov/ )。", "ds_quality": "
a. Accuracy of original data\n
The product data of mod10a1 (500m) includes snow cover rate, quality assessment (QA) and other data. Snow cover data is based on the snow mapping algorithm tested using the normalized difference snow cover index (NDSI) and other standards.\n
b. Relevant quality control measures in the process of project data generation and collection, including the complete data generation process, the methods and standards used, and the scope of data application.\n
(1) Data production process: resampling, splicing, clipping and other preprocessing are carried out on the original image, and finally obtained after cloud removal by using the cloud removal algorithm based on cubic spline function interpolation. The annual average snow cover rate is calculated by using ArcGIS and the area with DEM greater than 3500m is selected for mask processing, so as to obtain the annual snow cover rate data of Chaimu basin. Linear regression analysis is performed on each pixel based on python, and finally a 1km resolution snow cover change rate map in Qaidam region from 2000 to 2018 is generated.\n
(2) Methods and standard specifications: resampling, splicing, tailoring, linear regression analysis.\n
c. Data accuracy after processing\n
When processing the generated data, part of the original data is retained.", "ds_ref_way": "", "ds_abstract": "
This data is based on MODIS product data. Through resampling, splicing and other processing, the annual average snow cover rate data of Qaidam Basin from 2000 to 2018 is obtained. Through regression analysis of each pixel, the 1km resolution snow change rate map of Qaidam region from 2000 to 2018 is obtained\n
This data set is mainly applied to the scientific research of Cryosphere.
", "ds_time_res": "年", "ds_acq_place": "Qaidam Basin", "ds_space_res": "1km", "ds_projection": "", "ds_process_way": "The daily snow cover rate data is obtained on the basis of MODIS product data mod10a1 through resampling, splicing, clipping and other preprocessing, and cloud removal algorithm based on cubic spline function interpolation. Using ArcGIS to calculate the annual average snow cover rate and selecting areas with DEM greater than 3500m for mask processing, the annual snow cover rate data of Chaimu basin are obtained. Linear regression analysis is performed on each pixel based on python, and the 1km resolution snow cover change rate data in Qaidam region from 2000 to 2018 is finally generated.", "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": [ "柴达木盆地", "积雪变化率", "归一化差异积雪指数" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "柴达木盆地" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018 ], "ds_contributors": [ { "true_name": "朱高峰", "email": "zhugf@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李锴基", "email": "Likj19@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_managers": [ { "true_name": "朱高峰", "email": "zhugf@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "category": "积雪" }