针对“一带一路”地区现有的积雪面积产品在山区及林地低估等问题，基于多源遥感数据，采用MARS模型结合地类特征的方法进行积雪自动识别，生成了一套“一带一路”区域的积雪面积数据。借助机器学习在解决非线性拟合问题上的优势，避免传统积雪遥感识别在复杂地表和地形下的误判，产品在山区及林区的精度较已有MODIS积雪产品显著提高。在该产品基础上计算得到积雪终日数据集，积雪终日定义为一个水文年中最后连续5天是雪的终日对应日期。", "ds_source": "
MOD09GA 来源于美国地质调查局网站。
", "ds_process_way": "以Landsat-8的FSC影像作为真值数据，并结合CGLS-LC100数据集，提取详细的地类覆盖信息。通过结合地类特征，构建了基于MOD09GA反射率的MARS模型，以实现高效的积雪识别。整个过程包括数据预处理、特征优化、模型训练及验证，最终形成了一套高效的自动识别算法。该算法在积雪面积识别方面表现出快速性和高精度，能够有效应对现有产品面临的挑战，为区域环境监测与气候变化研究提供了重要的技术支持。
", "ds_quality": "", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2024-12-31 00:00:00", "ds_acq_place": "“一带一路”地区", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": 23.0, "ds_acq_lon_west": 180.0, "ds_acq_lat_north": 63.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 3368926649, "ds_files_count": 25, "ds_format": "*.tif", "ds_space_res": "500m", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "WGS_1984_Albers", "ds_thumbnail": "8a0f1cb0-bc9c-470b-9d73-49d92bdc9be1.png", "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": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.15", "170.45" ], "quality_level": 3, "publish_time": "2025-11-20 09:11:09", "last_updated": "2026-05-20 11:27:35", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6899.2025", "i18n": { "en": { "title": "\"The Belt and Road\" snow day data set (2000-2024)", "ds_format": "*.tif", "ds_source": "MOD09GA is sourced from the website of the United States Geological Survey.
", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "Aiming at the problem that the existing snow cover products in the \"the Belt and Road\" area are underestimated in mountain areas and woodlands, based on multi-source remote sensing data, a set of snow cover data in the \"the Belt and Road\" area was generated by using the MARS model combined with the method of land type characteristics to automatically identify snow cover. By leveraging the advantages of machine learning in solving nonlinear fitting problems, traditional snow remote sensing recognition can avoid misjudgment in complex terrain and terrain, and the accuracy of the product in mountainous and forested areas is significantly improved compared to existing MODIS snow products. On the basis of this product, a dataset of daily snow accumulation is calculated, which is defined as the corresponding date of the last 5 consecutive days of a hydrological year when snow accumulates.", "ds_time_res": "", "ds_acq_place": "\"The Belt and Road\" region", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
Extract detailed land cover information using FSC images from Landsat-8 as ground truth data, combined with the CGLS-LC100 dataset. A MARS model based on MOD09GA reflectance was constructed by combining land features to achieve efficient snow recognition. The entire process includes data preprocessing, feature optimization, model training, and validation, ultimately forming an efficient automatic recognition algorithm. This algorithm demonstrates speed and high accuracy in snow area recognition, effectively addressing the challenges faced by existing products and providing important technical support for regional environmental monitoring and climate change research.
", "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, "ds_topic_tags": [ "一带一路", "积雪范围", "积雪面积比例" ], "ds_subject_tags": [ "大气科学", "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "一带一路" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024 ], "ds_contributors": [ { "true_name": "郝晓华", "email": "haoxh@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "赵琴", "email": "zhaoqin21@mails.ucas.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "纪文政", "email": "jiwenzheng@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "高伟强", "email": "weiqiang_97@163.com", "work_for": "太钢集团岚县矿业有限公司", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "赵琴", "email": "zhaoqin21@mails.ucas.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "赵琴", "email": "zhaoqin21@mails.ucas.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "积雪" }