Earth Syst. Sci. Data, 13, 1–31, 2021 https://doi.org/10.5194/essd-13-1-2021 © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018 Yongzhe Chen1,2, Xiaoming Feng1, and Bojie Fu1,2 1State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China 2College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China Correspondence: Xiaoming Feng (
[email protected]) Received: 9 March 2020 – Discussion started: 8 May 2020 Revised: 13 November 2020 – Accepted: 18 November 2020 – Published: 5 January 2021 Abstract. Soil moisture is an important variable linking the atmosphere and terrestrial ecosystems. However, long-term satellite monitoring of surface soil moisture at the global scale needs improvement. In this study, we conducted data calibration and data fusion of 11 well-acknowledged microwave remote-sensing soil moisture products since 2003 through a neural network approach, with Soil Moisture Active Passive (SMAP) soil moisture data applied as the primary training target. The training efficiency was high (R2 D 0:95) due to the selection of nine quality impact factors of microwave soil moisture products and the complicated organizational structure of multiple neural networks (five rounds of iterative simulations, eight substeps, 67 independent neural networks, and more than 1 million localized subnetworks). Then, we developed the global remote-sensing-based surface soil moisture dataset (RSSSM) covering 2003–2018 at 0.1◦ resolution. The temporal resolution is approximately 10 d, meaning that three data records are obtained within a month, for days 1–10, 11–20, and from the 21st to the last day of that month.