青藏高原内积雪的动态变化对区域能量平衡以及水文、气候和生态环境具有重要的影响。由于青藏高原内季节性积雪多呈现出雪层普遍偏浅且斑块状的分布特征，因此积雪日数将能更好的体现时空分布特征。本数据集以空间分辨率为500m的MODIS-NDSI数据为基础，通过逐日积雪覆盖范围提取、积雪日数合成和站点数据校正等步骤，制作了青海省积雪日数遥感监测数据集（2002-2021年），并经青海省51个地面气象台站观测资料同期对比，数据具有较高精度。该数据集将为积雪动态变化监测、雪灾评估和气候变化等研究工作提供数据支撑。数据集共包括1个文件夹，包括有24个格式为tif的积雪日数数据
", "ds_source": "本文选用空间分辨率为500 m的每日积雪遥感产品（产品编号为MOD10A1），MOD10A1数据在美国冰雪数据中心进行下载，其地址为：https://nsidc.org/，经数据检索后覆盖青海省的产品行列号分别为h25v05和h26v05，
", "ds_process_way": "青海省积雪日数数据集的制作流程主要分为4个步骤：数据获取与预处理、逐日积雪覆盖范围提取、积雪日数合成与站点校正和精度验证与输出。其中输入的原始数据为2003年10月至2021年5月的MOD10A1 V6版数据，精度验证采用地面气象台站的人工观测数据，最终输出为2003-2021积雪季内青海省的积雪日数数据。
", "ds_quality": "以地面台站人工观测的积雪日数作为“真值”，逐积雪季对本文制作的积雪日数数据集进行精度检验，在各个雪季内两者间的积雪日数相关程度较高，其在0.65-0.86，相对误差保持在±15%。图2展示了2017和2019雪季内青海省积雪日数的空间分布状况，
", "ds_acq_start_time": "2002-01-01 00:00:00", "ds_acq_end_time": "2021-02-28 00:00:00", "ds_acq_place": "青海", "ds_acq_lon_east": 103.06666666666666, "ds_acq_lat_south": 31.116666666666667, "ds_acq_lon_west": 89.56666666666666, "ds_acq_lat_north": 31.316666666666666, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 588334998, "ds_files_count": 21, "ds_format": "TIF", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "f4625268-69d4-4210-be1f-c0d1ded375e4.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "aba68fe5-65d3-41b1-b036-bc274a834b5e", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2021-11-25 15:53:26", "last_updated": "2023-05-12 19:57:49", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2517.2022", "i18n": { "en": { "title": "Remote sensing monitoring data set of snow days in Qinghai Province from 2002 to 2021", "ds_format": "TIF", "ds_source": " <pre><code> <pre><code> &lt;pre&gt;&lt;code&gt;\nIn this paper, the daily snow remote sensing product with spatial resolution of 500 m (product No.: mod10a1) is selected. The mod10a1 data is downloaded from the U.S. snow and Ice Data Center at the address: https://nsidc.org/ After data retrieval, the product line numbers covering Qinghai Province are h25v05 and h26v05 respectively,
", "ds_quality": " <pre><code> <pre><code> &lt;pre&gt;&lt;code&gt;\nTaking the snow days manually observed by ground stations as the \"true value\", the accuracy of the snow days data set made in this paper is tested one by one. In each snow season, the correlation degree of snow days between the two is high, which is 0.65-0.86, and the relative error is maintained at ± 15%. Figure 2 shows the spatial distribution of snow days in Qinghai Province during the 2017 and 2019 snow seasons,
", "ds_ref_way": "", "ds_abstract": " <pre><code> <pre><code> &lt;p&gt;The dynamic change of snow cover in the Qinghai Tibet Plateau has an important impact on regional energy balance, hydrology, climate and ecological environment. Because the seasonal snow in the Qinghai Tibet Plateau mostly presents the distribution characteristics of shallow snow layer and patch shape, the number of snow days will better reflect the temporal and spatial distribution characteristics. Based on the modis-ndsi data with a spatial resolution of 500m, the remote sensing monitoring data set of snow days in Qinghai Province (2002-2021) is prepared through the steps of daily snow cover extraction, snow days synthesis and station data correction. After the comparison of the observation data of 51 surface meteorological stations in Qinghai Province over the same period, the data has high accuracy. The data set will provide data support for snow dynamic change monitoring, snow disaster assessment and climate change research. The data set includes 1 folder, including 24 snow days in TIF format&lt;/p&gt;\n <pre><code> <pre><code> &lt;pre&gt;&lt;code&gt;\nThe production process of snow days data set in Qinghai Province is mainly divided into four steps: data acquisition and preprocessing, daily snow coverage extraction, snow days synthesis and station correction, accuracy verification and output. The input original data is the mod10a1 V6 version data from October 2003 to may 2021. The accuracy verification adopts the manual observation data of ground meteorological stations, and the final output is the snow days data of Qinghai Province in the snow season from 2003 to 2021.
", "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": [ "青藏高原", "积雪日数", "气象站点", "NDSI" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏高原", "青海省" ], "ds_time_tags": [ 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 ], "ds_contributors": [ { "true_name": "李晓东", "email": "17016325@qq.com", "work_for": "青海省气象科学研究所", "country": "中国" }, { "true_name": "李林", "email": "1652330742@qq.com", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "史飞飞", "email": "shifeifei1203@126.com", "work_for": "青海省气象科学研究所", "country": "中国" }, { "true_name": "苏文将", "email": "", "work_for": "青海省气象科学研究所", "country": "中国" }, { "true_name": "肖建设", "email": "", "work_for": "青海省气象科学研究所", "country": "中国" }, { "true_name": "李红梅", "email": "qhlhm163@163.com", "work_for": "青海省气象科学研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李晓东", "email": "17016325@qq.com", "work_for": "青海省气象科学研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "李晓东", "email": "17016325@qq.com", "work_for": "青海省气象科学研究所", "country": "中国" } ], "category": "积雪" }