{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T06:36:54Z","timestamp":1769841414470,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,6]],"date-time":"2024-01-06T00:00:00Z","timestamp":1704499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["CBAS2022IRP05"],"award-info":[{"award-number":["CBAS2022IRP05"]}]},{"name":"Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["42376193"],"award-info":[{"award-number":["42376193"]}]},{"name":"National Natural Science Foundation of China","award":["CBAS2022IRP05"],"award-info":[{"award-number":["CBAS2022IRP05"]}]},{"name":"National Natural Science Foundation of China","award":["42376193"],"award-info":[{"award-number":["42376193"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Dissolved oxygen (DO) is essential for assessing and monitoring the health of marine ecosystems. The phenomenon of ocean deoxygenation is widely recognized. Nevertheless, the limited availability of observations poses a challenge in achieving a comprehensive understanding of global ocean DO dynamics and trends. The study addresses the challenge of unevenly distributed Argo DO data by developing time\u2013space\u2013depth machine learning (TSD-ML), a novel machine learning-based model designed to enhance reconstruction accuracy in data-sparse regions. TSD-ML partitions Argo data into segments based on time, depth, and spatial dimensions, and conducts model training for each segment. This research contrasts the effectiveness of partitioned and non-partitioned modeling approaches using three distinct ML regression methods. The results reveal that TSD-ML significantly enhances reconstruction accuracy in areas with uneven DO data distribution, achieving a 30% reduction in root mean square error (RMSE) and a 20% decrease in mean absolute error (MAE). In addition, a comparison with WOA18 and GLODAPv2 ship survey data confirms the high accuracy of the reconstructions. Analysis of the reconstructed global ocean DO trends over the past two decades indicates an alarming expansion of anoxic zones.<\/jats:p>","DOI":"10.3390\/rs16020228","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T05:21:38Z","timestamp":1704691298000},"page":"228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Reconstructing Model Based on Time\u2013Space\u2013Depth Partitioning for Global Ocean Dissolved Oxygen Concentration"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8663-1279","authenticated-orcid":false,"given":"Zhenguo","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3605-6578","authenticated-orcid":false,"given":"Cunjin","family":"Xue","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2854-2261","authenticated-orcid":false,"given":"Bo","family":"Ping","sequence":"additional","affiliation":[{"name":"School of Earth System Science, Institute of Surface-Earth System Science, Tianjin University, Tianjin 300072, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"115417","DOI":"10.1016\/j.envpol.2020.115417","article-title":"Use of water quality index and multivariate statistical methods for the evaluation of water quality of a stream affected by multiple stressors: A case study","volume":"266","author":"Varol","year":"2020","journal-title":"Environ. 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