Global soil moisture from in-situ measurements using machine learning -- SoMo.ml
| Title: | Global soil moisture from in-situ measurements using machine learning -- SoMo.ml |
|---|---|
| Authors: | O, Sungmin, Orth, Rene |
| Publication Year: | 2020 |
| Collection: | Computer Science Physics (Other) |
| Subject Terms: | Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning |
| More Details: | While soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture generated from in-situ measurements using machine learning, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0-10 cm, 10-30 cm, and 30-50 cm) at 0.25{\deg} spatial and daily temporal resolution over the period 2000-2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its independent and novel derivation, to support large-scale hydrological, meteorological, and ecological analyses. |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/2010.02374 |
| Accession Number: | edsarx.2010.02374 |
| Database: | arXiv |
| FullText | Text: Availability: 0 CustomLinks: – Url: http://arxiv.org/abs/2010.02374 Name: EDS - Arxiv Category: fullText Text: View this record from Arxiv MouseOverText: View this record from Arxiv – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:edsarx&genre=article&issn=&ISBN=&volume=&issue=&date=20201005&spage=&pages=&title=Global soil moisture from in-situ measurements using machine learning -- SoMo.ml&atitle=Global%20soil%20moisture%20from%20in-situ%20measurements%20using%20machine%20learning%20--%20SoMo.ml&aulast=O%2C%20Sungmin&id=DOI: Name: Full Text Finder (for New FTF UI) (s8985755) Category: fullText Text: Find It @ SCU Libraries MouseOverText: Find It @ SCU Libraries |
|---|---|
| Header | DbId: edsarx DbLabel: arXiv An: edsarx.2010.02374 RelevancyScore: 1008 AccessLevel: 3 PubType: Report PubTypeId: report PreciseRelevancyScore: 1007.73657226563 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Global soil moisture from in-situ measurements using machine learning -- SoMo.ml – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22O%2C+Sungmin%22">O, Sungmin</searchLink><br /><searchLink fieldCode="AR" term="%22Orth%2C+Rene%22">Orth, Rene</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2020 – Name: Subset Label: Collection Group: HoldingsInfo Data: Computer Science<br />Physics (Other) – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Physics+-+Atmospheric+and+Oceanic+Physics%22">Physics - Atmospheric and Oceanic Physics</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+-+Machine+Learning%22">Computer Science - Machine Learning</searchLink> – Name: Abstract Label: Description Group: Ab Data: While soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture generated from in-situ measurements using machine learning, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0-10 cm, 10-30 cm, and 30-50 cm) at 0.25{\deg} spatial and daily temporal resolution over the period 2000-2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its independent and novel derivation, to support large-scale hydrological, meteorological, and ecological analyses. – Name: TypeDocument Label: Document Type Group: TypDoc Data: Working Paper – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="http://arxiv.org/abs/2010.02374" linkWindow="_blank">http://arxiv.org/abs/2010.02374</link> – Name: AN Label: Accession Number Group: ID Data: edsarx.2010.02374 |
| PLink | https://login.libproxy.scu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsarx&AN=edsarx.2010.02374 |
| RecordInfo | BibRecord: BibEntity: Subjects: – SubjectFull: Physics - Atmospheric and Oceanic Physics Type: general – SubjectFull: Computer Science - Machine Learning Type: general Titles: – TitleFull: Global soil moisture from in-situ measurements using machine learning -- SoMo.ml Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: O, Sungmin – PersonEntity: Name: NameFull: Orth, Rene IsPartOfRelationships: – BibEntity: Dates: – D: 05 M: 10 Type: published Y: 2020 |
| ResultId | 1 |