本数据为新疆逐日降尺度500m积雪深度数据。其生产主要基于随机森林算法，利用中国长时间序列雪深数据集、地形数据、空间位置数据、积雪覆盖日数等制备了2010-2020年期间空间分辨率为500m的逐日积雪深度数据集。该数据集以TIF文件格式存储，命名规则为：“SNDP+year+DOY.tif”，DOY为年积日。本数据集以期为积雪的深入研究与准确分析、气候变化、雪灾预警等提供有效的数据支撑。
", "ds_source": "“中国长时间序列雪深数据集”来源于国家青藏高原科学数据中心；MOD13A1归一化植被指数数据来源于Earthdata；SRTM地形数据来源于通过地理空间数据云平台，包括海拔、坡度、坡向、地表粗糙度数据。
", "ds_process_way": "本数据集以新疆气象站点数据、中国长时间序列雪深数据集、经度、纬度、SRTM海拔、坡度、坡向、地表粗糙度、MOD13A1归一化植被指数数据、积雪覆盖日数数据为基础，建立以随机森林算法为基础的积雪深度降尺度模型，根据此模型生产500m降尺度积雪深度产品。
", "ds_quality": "该数据的原始数据为中国长时间序列雪深数据集，以新疆地区50个实测站台点提供的2013年至2020年的雪深实测数据对降尺度前后雪深数据集进行精度评估与对比，结果表明通过降尺度降尺度后雪深数据的精度有所提升，平均决定系数R2为0.61，数据综合评价指标KEG’为0.64，均方根误差RMSE为4.59cm，平均绝对误差MAE为1.36cm，相关系数为0.81。
", "ds_acq_start_time": "2010-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "新疆维吾尔自治区", "ds_acq_lon_east": 97.08, "ds_acq_lat_south": 33.37, "ds_acq_lon_west": 70.25, "ds_acq_lat_north": 49.93, "ds_acq_alt_low": -158.0, "ds_acq_alt_high": 7804.0, "ds_share_type": "apply-access", "ds_total_size": 287595633758, "ds_files_count": 12059, "ds_format": "*.tif", "ds_space_res": "500m", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "经纬", "ds_thumbnail": "50fa2bae-9034-4eb4-aa97-f8fc5fc7c38f.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "c6ac5c3c-eb4f-4119-86fb-c4534a9bd7fb", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.15", "170.45" ], "quality_level": 3, "publish_time": "2023-05-05 17:56:07", "last_updated": "2026-05-20 17:26:56", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB2841.2023", "i18n": { "en": { "title": "500m Daily Snow Depth Dataset in Xinjiang (2010-2020)", "ds_format": "*.tif", "ds_source": "The 'China Long term Snow Depth Dataset' is sourced from the National Qinghai Tibet Plateau Science Data Center; The MOD13A1 normalized vegetation index data is sourced from Earthdata; SRTM terrain data comes from a geographic spatial data cloud platform, including altitude, slope, aspect, and surface roughness data.
", "ds_quality": "The original data for this dataset is a long-term series snow depth dataset from China. The accuracy of the snow depth dataset before and after downscaling was evaluated and compared using snow depth measurement data from 50 measured stations in Xinjiang from 2013 to 2020. The results showed that the accuracy of the snow depth data was improved after downscaling, with an average coefficient of determination R2 of 0.61, a comprehensive evaluation index KEG 'of 0.64, a root mean square error RMSE of 4.59cm, an average absolute error MAE of 1.36cm, and a correlation coefficient of 0.81.
", "ds_ref_way": "", "ds_abstract": "This data is daily downscaled 500m snow depth data for Xinjiang. Its production is mainly based on the random forest algorithm, using the Chinese long-term series snow depth dataset, terrain data, spatial position data, snow cover days, etc. to prepare a daily snow depth dataset with a spatial resolution of 500m from 2010 to 2020. This dataset is stored in TIF file format, with the naming convention of \"SNDP+year+DOY. tif\" and DOY being the product of year and day. This dataset aims to provide effective data support for in-depth research and accurate analysis of snow cover, climate change, and snow disaster warning.
", "ds_time_res": "", "ds_acq_place": "Xinjiang Uyghur Autonomous Region", "ds_space_res": "", "ds_projection": "", "ds_process_way": "This dataset is based on data from Xinjiang meteorological stations, China's long-term series snow depth dataset, longitude, latitude, SRTM altitude, slope, aspect, surface roughness, MOD13A1 normalized vegetation index data, and snow cover days data. A snow depth downscaling model based on random forest algorithm is established to produce 500m downscaled snow depth products.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/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, "belong_to_nieer": false, "ds_topic_tags": [ "积雪", "雪深", "积雪深度" ], "ds_subject_tags": [ "大气科学", "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆" ], "ds_time_tags": [ 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "李海星", "email": "leehaixing@126.com", "work_for": "南京工业大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李海星", "email": "leehaixing@126.com", "work_for": "南京工业大学", "country": "中国" } ], "ds_managers": [ { "true_name": "李海星", "email": "leehaixing@126.com", "work_for": "南京工业大学", "country": "中国" } ], "category": "积雪" }