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High-resolution European daily soil moisture derived with machine learning (2003-2020)
- Title
- High-resolution European daily soil moisture derived with machine learning (2003-2020)
- Authors
- Sungmin, O.; Orth, Rene; Weber, Ulrich; Park, Seon Ki
- Ewha Authors
- 박선기; 오승민
- SCOPUS Author ID
- 박선기; 오승민
- Issue Date
- 2022
- Journal Title
- SCIENTIFIC DATA
- ISSN
- 2052-4463
- Citation
- SCIENTIFIC DATA vol. 9, no. 1
- Publisher
- NATURE PORTFOLIO
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
Data Paper
- Abstract
- Machine learning (ML) has emerged as a novel tool for generating large-scale land surface data in recent years. ML can learn the relationship between input and target, e.g. meteorological variables and in-situ soil moisture, and then estimate soil moisture across space and time, independently of prior physics-based knowledge. Here we develop a high-resolution (0.1 degrees) daily soil moisture dataset in Europe (SoMo.ml-EU) using Long Short-Term Memory trained with in-situ measurements. The resulting dataset covers three vertical layers and the period 2003-2020. Compared to its previous version with a lower spatial resolution (0.25 degrees), it shows a closer agreement with independent in-situ data in terms of temporal variation, demonstrating the enhanced usefulness of in-situ observations when processed jointly with high-resolution meteorological data. Regional comparison with other gridded datasets also demonstrates the ability of SoMo.ml-EU in describing the variability of soil moisture, including drought conditions. As a result, our new dataset will benefit regional studies requiring high-resolution observation-based soil moisture, such as hydrological and agricultural analyses.
- DOI
- 10.1038/s41597-022-01785-6
- Appears in Collections:
- 공과대학 > 기후에너지시스템공학과 > Journal papers
- Files in This Item:
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s41597-022-01785-6.pdf(3.96 MB)
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