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| 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: | See how to cite this resource | |
| Content types: | Geographic Raster Content |
| Sharing Status: | Published |
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| Views: | 989 |
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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
Temporal
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Content
Data Services
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 |
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| National Science Foundation | Collaborative Research: Elements: SENSORY: Software Ecosystem for kNowledge diScOveRY - a data-driven framework for soil moisture applications | 2103836 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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