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，本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了积雪初日数据集，积雪初日的定义为一个水文年中第一次出现连续5天是雪的首日对应日期。", "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。
", "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": 2259020969, "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": "3ee02200-083c-475a-a82b-b26f07fac78d.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": "2025-11-19 15:32:03", "last_updated": "2026-05-20 11:27:10", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6900.2025", "i18n": { "en": { "title": "\"The Belt and Road\" Snow First 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": "Based on multi-source remote sensing data, a set of snow area data of the \"the Belt and Road\" region was generated by using the MARS model combined with the method of land type features for automatic snow recognition. By leveraging the advantages of machine learning in solving nonlinear fitting problems and avoiding the misjudgment of traditional snow remote sensing recognition in complex terrain and terrain, the accuracy of this product in mountainous and forested areas has significantly improved compared to existing MODIS snow products. On the basis of this product, a dataset of the first day of snow accumulation was calculated. The definition of the first day of snow accumulation is the corresponding date of the first consecutive 5-day occurrence of snow in a hydrological year.", "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.
", "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”地区" ], "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" }