Please wait for the process to complete.
Checking for non-preferred file/folder path names (may take a long time depending on the number of files/folders) ...
This resource contains some files/folders that have non-preferred characters in their name. Show non-conforming files/folders.
||This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (firstname.lastname@example.org) for information on this resource.|
|Storage:||The size of this resource is 248.3 MB|
|Created:||Mar 08, 2022 at 1:45 a.m.|
|Last updated:|| Mar 08, 2022 at 3:20 a.m.
|Citation:||See how to cite this resource|
|Content types:||Geographic Raster Content|
|+1 Votes:||Be the first one to this.|
|Comments:||No comments (yet)|
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).
Web Map Service
Web Coverage Service
|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.|
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
This resource is shared under the Creative Commons Attribution CC BY.http://creativecommons.org/licenses/by/4.0/