针对现有雪水当量遥感产品精度不稳定，且数据时空不连续等问题，基于深度学习理论，构建了一套多种深度学习模型嵌套的雪水当量产品融合算法，并基于已有的雪水当量产品形成了一套高质量时空序列连续的北半球雪水当量数据产品。
", "ds_source": "本数据集基于已有的雪水当量数据融合而来，分别包括AMSR-E/AMSR2 SWE、GLDAS SWE、GlobSnow SWE、AMSR-E SWE、GLDAS SWE。
", "ds_process_way": "首先是构建雪水当量训练数据集，本研究使用的雪水当量数据包括AMSR-E/AMSR2 SWE、ERA-Interim SWE、MERRA-2 SWE、GLDAS SWE、GlobSnow SWE、ERA5_Land SWE。为了提供更高空间分辨率的辅助信息，将500 m分辨率的 MODIS 积雪面积数据 、地理数据（经纬度）、地形数据（海拔、坡度、坡向）等作为辅助训练数据，地面观测的雪水当量数据作为参考真值。在训练数据集基础上，构建基于岭回归模型结合LSTM的雪水当量回归计算模型，利用岭回归深度学习算法进行首次学习，将学习结果区分为精度提高明显和不明显两部分。针对精度提高不明显的部分，采用LSTM算法在时序依赖上的优势，进行进一步学习。在此基础上，获取高质量时空连续的雪水当量数据。
", "ds_quality": "", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2025-01-31 00:00:00", "ds_acq_place": "北半球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": 0.0, "ds_acq_lon_west": 180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 476790153455, "ds_files_count": 9164, "ds_format": "HDF", "ds_space_res": "5000m", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "WGS_1984_Albers", "ds_thumbnail": "dc8e410f-f71c-445c-8d6d-6def9634fc26.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "Donghang Shao, Hongyi Li, Jian Wang, Xiaohua Hao, Tao Che, Wenzheng Ji. Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach, Earth System Science Data, 2022, 14(2): 795-809.", "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": [ "170.45" ], "quality_level": 3, "publish_time": "2024-06-14 09:15:46", "last_updated": "2026-05-20 15:28:49", "protected": true, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6494.2024", "i18n": { "en": { "title": "Daily data product of snow water equivalent in the Northern Hemisphere (2000-2025)", "ds_format": "HDF", "ds_source": "This dataset is based on the fusion of existing snow water equivalent data, including AMSR-E/AMSR2 SWE, GLDAS SWE, GlobSnow SWE, AMSR-E SWE, and GLDAS SWE.
", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "Aiming at the problems of unstable accuracy and spatiotemporal discontinuity of existing snow water equivalent remote sensing products, a set of nested snow water equivalent product fusion algorithms based on deep learning theory was constructed, and a high-quality spatiotemporal sequence continuous Northern Hemisphere snow water equivalent data product was formed based on existing snow water equivalent products.
", "ds_time_res": "", "ds_acq_place": "Northern Hemisphere", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The first step is to construct a snow water equivalent training dataset, which includes the snow water equivalent data used in this study AMSR-E/AMSR2 SWE、ERA-Interim SWE、MERRA-2 SWE、GLDAS SWE、GlobSnow SWE、ERA5_Land SWE。 In order to provide higher spatial resolution auxiliary information, MODIS snow cover area data with a resolution of 500 meters, geographic data (latitude and longitude), terrain data (altitude, slope, aspect), etc. are used as auxiliary training data, and ground observation snow water equivalent data is used as reference truth. On the basis of the training dataset, a snow water equivalent regression calculation model based on ridge regression model combined with LSTM is constructed. The ridge regression deep learning algorithm is used for the first learning, and the learning results are divided into two parts: significant and insignificant accuracy improvement. For the parts where the accuracy improvement is not significant, the advantages of LSTM algorithm in temporal dependence are adopted for further learning. On this basis, obtain high-quality spatiotemporal continuous snow water equivalent data.
", "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": [ "雪水当量", "深度学习", "融合算法" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "北半球" ], "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, 2025 ], "ds_contributors": [ { "true_name": "邵东航", "email": "shaodonghang@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "邵东航", "email": "shaodonghang@lzb.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": "邵东航", "email": "shaodonghang@lzb.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" }