1-km soil moisture predictions in the United States

Authors:

Ricardo Llamas Leobardo Valera Paula Olaya Michela Taufer Rodrigo Vargas

Owners:

Ricardo Llamas

Type:

Resource

Storage:

The size of this resource is 126.9 MB

Created:

Dec 14, 2021 at 4:48 p.m.

Last updated:

Feb 02, 2024 at 2:21 p.m. (Metadata update)

Published date:

Jan 20, 2022 at 5:31 p.m.

DOI:

10.4211/hs.5c7a4f1c16c34079b8e3583a1497cb95

Citation:

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Abstract

Monthly and weekly soil moisture predictions in 2010 at 1-km spatial resolution using four different Machine Learning Methods integrated in the Satellite Soil Moisture based on a modular SOil Moisture SPatial Inference Engine (SOMOSPIE- Rorabaugh et al. 2019) (kernel-weighted k-nearest neighbors <KKNN>, Random Forests <RF>, Surrogate-Based Model <SBM> and a Hybrid Piecewise Polynomial Modeling Technique <HYPPO>). Data were acquired from the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product version 6.1, 0.25-degrees spatial resolution. Modeled soil moisture layers are delivered for two regions in the conterminous United States. Each region encompasses a polygon of 7.5° x 3.75° (n = 450 pixels with 30 columns and 15 rows in the native resolution of the ESA CCI Soil moisture product). Region 1 <so called West Region> comprises an area of 275,516 km2. Region 2 <so called Midwest region> comprises an area of 283,499 km2. Predicted soil moisture values were validated by means two approaches, cross-validation using the ESA CCI estimates and independent ground-truth records from the North American Soil Moisture Database (currently known as the National Soil Moisture Network). Detailed methods and results of this dataset are described in: Llamas, R.M; Valera, Leobardo; Olaya, Paula; Taufer, Michela; Vargas, Rodrigo “Downscaling Satellite Soil Moisture based on a modular SOil Moisture SPatial Inference Engine (SOMOSPIE)”, Remote Sensing (submitted).

Subject Keywords

Coverage

Spatial

Coordinate System/Geographic Projection:

WGS 84 EPSG:4326

Coordinate Units:

Decimal degrees

North Latitude

37.3886°

East Longitude

-93.9977°

South Latitude

32.9879°

West Longitude

-116.7667°

Temporal

Start Date:	01/01/2010
End Date:	12/31/2010

Streets Terrain Satelite

Spatial Extent

Content

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Data Services

The following web services are available for data contained in this resource. Geospatial Feature and Raster data are made available via Open Geospatial Consortium Web Services. The provided links can be copied and pasted into GIS software to access these data. Multidimensional NetCDF data are made available via a THREDDS Data Server using remote data access protocols such as OPeNDAP. Other data services may be made available in the future to support additional data types.

Web Map Service

https://geoserver.hydroshare.org/geoserver/HS-5c7a4f1c16c34079b8e3583a1497cb95/wms?request=GetCapabilities

Web Coverage Service

https://geoserver.hydroshare.org/geoserver/HS-5c7a4f1c16c34079b8e3583a1497cb95/wcs?request=GetCapabilities

Related Resources

This resource is referenced by	Llamas, R.M; Valera, Leobardo; Olaya, Paula; Taufer, Michela; Vargas, Rodrigo. "Downscaling Satellite Soil Moisture based on a modular SOil Moisture SPatial Inference Engine (SOMOSPIE)", Remote Sensing (submitted)
The content of this resource is derived from	https://www.esa-soilmoisture-cci.org/v06.1_release

Credits

Funding Agencies

This resource was created using funding from the following sources:

Agency Name	Award Title	Award Number
National Science Foundation	Collaborative Research: Elements: SENSORY: Software Ecosystem for kNowledge diScOveRY - a data-driven framework for soil moisture applications	2103836

How to Cite

Llamas, R., L. Valera, P. Olaya, M. Taufer, R. Vargas (2022). 1-km soil moisture predictions in the United States, HydroShare, https://doi.org/10.4211/hs.5c7a4f1c16c34079b8e3583a1497cb95

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/

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