海洋化学时间序列产品包括总碱度、无机碳、硝酸盐、磷酸盐、硅酸盐和 pH 值，是持续监测海洋化学变化的基础支持机制。这些产品在促进以海洋生态系统动态监测为重点的研究和促进海洋可持续发展方面发挥着至关重要的作用。在重建 2000 年至 2022 年期间澳大利亚专属经济区海面上这些海洋化学物质在1公里范围内的浓度时，对 CANYON-B 和随机森林回归方法进行了新颖的整合探索。该方法涉及将多源原位海洋化学时间序列观测数据与 MODIS Terra 海洋反射率图像和海洋水色产品分布合并。这项研究凸显了机器学习在大规模重建海洋化学数据方面的巨大能力，为利用原位测量和光学图像重建海洋化学元素引入了一种新的可行方法，从而大大提高了我们监测大尺度海洋动态的能力。", "ds_source": "
数据来源于科学数据银行（https://www.scidb.cn/en/detail?dataSetId=4991142399124803a68a67633cbec7d3）。", "ds_process_way": "
将 CANYON-B 和随机森林回归方法进行了新颖的整合探索，该方法涉及将多源原位海洋化学时间序列观测数据与 MODIS Terra 海洋反射率图像和海洋水色产品分布合并。", "ds_quality": "
数据质量良好。", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "澳大利亚", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 303090706043, "ds_files_count": 7, "ds_format": "HDF5", "ds_space_res": "1000m", "ds_time_res": "月", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "c9810152-8613-4bbd-b6fa-a872038091a5.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "d2c052ce-d283-4a48-8962-6a3dbcb03b8e", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.6015" ], "quality_level": 3, "publish_time": "2024-12-27 15:57:56", "last_updated": "2026-01-14 10:55:45", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.SCIDB.DB6698.2024", "i18n": { "en": { "title": "Monthly Product of Marine Chemical Data in Australian Waters from 2000 to 2022", "ds_format": "HDF5", "ds_source": "
The data is sourced from the Scientific Data Bank（ https://www.scidb.cn/en/detail?dataSetId=4991142399124803a68a67633cbec7d3 ）.", "ds_quality": "
The data quality is good.", "ds_ref_way": "", "ds_abstract": "
The marine chemical time-series products, which include total alkalinity, inorganic carbon, nitrate, phosphate, silicate, and pH, constitute a foundational support mechanism for the ongoing surveillance of oceanic chemical changes. These products play a critical role in facilitating research focused on dynamic monitoring of marine ecosystems and fostering sustainable oceanic development. The interpolation methods frequently prove low-effective on a large scale, resulting in data with extensive temporal and spatial expanses that are difficulty for applications aimed at monitoring large-scale ocean dynamics. A novel integration of the CANYON-B and random forest regression methods was explored in reconstructing the concentrations of these marine chemicals at the sea surface within Australia's Exclusive Economic Zone over the period from 2000 to 2022 on a 1-kilometre scale. The approach involves the amalgamation of multi-source in-situ ocean chemistry time-series observations with MODIS Terra ocean reflectance imagery and ocean water colour product distributions. This research highlights the substantial capabilities of machine learning for the large-scale reconstruction of ocean chemistry data, introducing a new, viable method for utilising in-situ measurements and optical imagery in reconstructing marine chemical elements, thereby significantly enhancing our abil
", "ds_time_res": "月", "ds_acq_place": "Australia", "ds_space_res": "1000m", "ds_projection": "", "ds_process_way": "A novel integration exploration was conducted between CANYON-B and random forest regression methods, which involves merging multi-source in-situ ocean chemistry time series observation data with MODIS Terra ocean reflectance images and ocean color product distributions.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "海洋化学", "碱度", "无机碳" ], "ds_subject_tags": [ "海洋化学" ], "ds_class_tags": [], "ds_locus_tags": [ "澳大利亚" ], "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 ], "ds_contributors": [ { "true_name": "王力哲", "email": "lizhe.wang@foxmail.com", "work_for": "中国地质大学（武汉）", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "王力哲", "email": "lizhe.wang@foxmail.com", "work_for": "中国地质大学（武汉）", "country": "中国" } ], "ds_managers": [ { "true_name": "王力哲", "email": "lizhe.wang@foxmail.com", "work_for": "中国地质大学（武汉）", "country": "中国" } ], "category": "水文" }