2026-10-12T05:29:40Z https://www.scidb.cn/oai

10.11922/sciencedb.01154 2022-07-07T18:01:45Z

2022-07-07 OPEN-LSTM: A Global 1º×1º Monthly Ocean Heat Content Dataset from Remote Sensing Data Based on a Long Short-Term Memory (LSTM) Method (1993-2020) doi:10.11922/sciencedb.01154 en The OPEN-LSTM dataset estimates Ocean Heat Content (OHC) for upper 2000 m over six different depths (0-100，0-300，0-700，0-1000，0-1500，0-2000 m) at a global scale from 1993 to 2020, based on a Long Short-Term Memory (LSTM) method, via multisource remote sensing observations (SSH, SST, and Winds) combined with Argo-gridded OHC as training data. The resolution of OPEN-LSTM is monthly and one degree, and the time span is from 1993 to 2020. The LSTM neural network method considers long temporal dependence of ocean process to reconstruct a new long time-series OHC dataset (1993-2020) and fill the pre-Argo data gaps from satellite remote sensing observations. The OPEN dataset has been cited by the IPCC AR6 report, and adopted by Big Earth Data in Support of the Sustainable Development Goals (2021) report. ocean heat content (OHC); long short-term memory (LSTM); remote sensing data; global climate change; time-series reconstruction; global ocean warming; deep learning Hua Su Tian Qin An Wang Wenfang Lu Xiao-Hai Yan PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/10.3390/rs13193799 Science Data Bank