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1-km soil moisture predictions in the United States with SOMOSPIE

A newer version of this resource https://doi.org/10.4211/hs.96eeb0d796a64b578f24e8154c166988 is available that replaces this version.

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Type:	Resource
Storage:	The size of this resource is 281.0 MB
Created:	Mar 08, 2022 at 1:45 a.m. (UTC)
Last updated:	Mar 08, 2022 at 3:20 a.m. (UTC) (Metadata update)
Published date:	Mar 08, 2022 at 3:19 a.m. (UTC)
DOI:	10.4211/hs.79162a679f8a4273be582561a5504f68
Citation:	See how to cite this resource
Content types:	Geographic Raster Content

Sharing Status:	Published
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Abstract

Monthly and weekly soil moisture predictions in 2010 at 1-km spatial resolution using two different modeling methods integrated in the modular SOil Moisture SPatial Inference Engine (SOMOSPIE- Rorabaugh et al. 2019) (kernel-weighted k-nearest neighbors <KKNN>, Random Forests <RF>). 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> consists of an area of 275,516 km2. Region 2 <so called Midwest region> consists of an area of 283,499 km2. Predicted soil moisture values were validated by means of 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:
End Date:

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-79162a679f8a4273be582561a5504f68/wms?request=GetCapabilities

Web Coverage Service

https://geoserver.hydroshare.org/geoserver/HS-79162a679f8a4273be582561a5504f68/wcs?request=GetCapabilities

Related Resources

The content of this resource is derived from	https://www.esa-soilmoisture-cci.org/v06.1_release
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)
This resource has been replaced by a newer version	Llamas, R., L. Valera, P. Olaya, M. Taufer, R. Vargas (2022). 1-km soil moisture predictions in the United States with SOMOSPIE framework, HydroShare, https://doi.org/10.4211/hs.96eeb0d796a64b578f24e8154c166988
The content of this resource was created by a related App or software program	1. Rorabaugh, D.; Guevara, M.; Llamas, R.; Kitson, J.; Vargas, R.; Taufer, M. SOMOSPIE: A Modular SOil MOisture SPatial Inference Engine Based on Data-Driven Decisions. In Proceedings of the 2019 15th International Conference on eScience (eScience); IEEE: San Diego, CA, USA, 2019; pp. 1–10.

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., Valera, L., Olaya, P., Taufer, M., Vargas, R. (2022). 1-km soil moisture predictions in the United States with SOMOSPIE, HydroShare, https://doi.org/10.4211/hs.79162a679f8a4273be582561a5504f68

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

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

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