SOMOSPIE: A modular
SOil MOisture SPatial Inference Engine
based on data-driven decisions

Funded by the National Science Foundation (NSF) under grant number 1724843

About SOMOSPIE

SOMOSPIE is a modular SOil MOisture SPatial Inference Engine that allows address the coarse-grained resolution and spatial information gaps associated with satellite data. The modular components of SOMOSPIE consists of:
Input of available satellite data at its native spatial resolution.
Selection of a geographic region of interest.
Prediction, of missing values across the entire region of interest (i.e., gap-filling) and at finer-grained resolution.
Analysis and visualization of generated predictions.

Installing Somospie

SOMOSPIE can be installed on different operating systems .

Download SOMOSPIE Datasets

From Hydroshare: Gap-Free Global Annual Soil Moisture: 15km Grids for 1991-2018. Two soil moisture annual predictions (1991-2018) are provided there. The first model predictions are based on terrain parameters (sm_kknn_terrain.tar.xz) and the second model predictions include bioclimatic data (sm_kknn_eco_swc_terrain_15km..tar.xz). Both are xz-compressed files that can be uncompressed on windows via winzip or similar and on linux using the command: "tar -xf backup.tar.xz".

Publication

D. Rorabaugh (#), M. Guevara, R. Llamas, J. Kitson, R. Vargas, and M. Taufer. SOMOSPIE: A modular SOil MOisture SPatial Inference Engine based on data-driven decisions. 2019 15th International Conference on eScience (eScience) (2019). [link]

@inproceedings{rorabaugh2019somospie,
title={SOMOSPIE: A modular SOil MOisture SPatial Inference Engine based on data-driven decisions},
author={Rorabaugh, Danny and Guevara, Mario and Llamas, Ricardo and Kitson, Joy and Vargas, Rodrigo and Taufer, Michela},
booktitle={2019 15th International Conference on eScience (eScience)},
pages={1--10},
year={2019},
organization={IEEE}
}

R. Llamas (#), M. Guevara, M. Taufer, and R. Vargas. Spatial Gap-Filling of ESA CCI Satellite-Derived Soil Moisture based on Linear Geostatistics. Remote Sensing 12(4):665 (2020)10.3390/rs12040665. [link]

@article{llamas2020spatial,
title={Spatial gap-filling of ESA CCI satellite-derived soil moisture based on geostatistical techniques and multiple regression},
author={Llamas, Ricardo M and Guevara, Mario and Rorabaugh, Danny and Taufer, Michela and Vargas, Rodrigo},
journal={Remote Sensing},
volume={12},
number={4},
pages={665},
year={2020},
publisher={Multidisciplinary Digital Publishing Institute}
}

T. Kitson, P. Olaya, E. Racca, Michael R. Wyatt II, M. Guevara, R. Vargas, and M. Taufer. Data Analytics for Modeling Soil Moisture Patterns across United States Ecoclimatic Domains. In Proceedings of the 2017 IEEE International Conference on Big Data.pp 1-3. Boston, MA, USA. December 2017. [link]

@inproceedings{kitson2017data,
title={Data analytics for modeling soil moisture patterns across united states ecoclimatic domains},
author={Kitson, Thomas and Olaya, Paula and Racca, Elizabeth and Wyatt, Michael R and Guevara, Mario and Vargas, Rodrigo and Taufer, Michela}
, booktitle={2017 IEEE International Conference on Big Data (Big Data)}, pages={4768--4770},
year={2017},
organization={IEEE}
}

R. McKenna, V. Pallipuram, R. Vargas, and M. Taufer. From HPC Performance to Weather Modeling: Transforming Methods for HPC Predictions into Models of Extreme Climate Conditions. In Proceedings of the Tenth IEEE International Conference on e-Science and Grid Technologies (eScience), pp. 108 – 117. Munich, Germany. August 31 – September 4, 2015. (2020)10.3390/rs12040665

@inproceedings{mckinney2015hpc,
title={From HPC performance to climate modeling: Transforming methods for HPC predictions into models of extreme climate conditions},
author={McKinney, Ryan and Pallipuram, Vivek K and Vargas, Rodrigo and Taufer, Michela},
booktitle={2015 IEEE 11th International Conference on e-Science},
pages={108--117},
year={2015},
organization={IEEE}
}

E. Stell, M. Guevara, and R. Vargas. Soil swelling potential across Colorado: A digital soil mapping assessment. Landscape and Urban Planning.v.190, 2019. [link]

@article{stell2019soil,
title={Soil swelling potential across Colorado: A digital soil mapping assessment},
author={Stell, Emma and Guevara, Mario and Vargas, Rodrigo},
journal={Landscape and Urban Planning},
volume={190},
pages={103599},
year={2019},
publisher={Elsevier}
}

M Guevara and R. Vargas. Downscaling satellite soil moisture using geomorphometry and machine learning, PLOS ONE, v.14, 2019 [link]

@article{guevara2019downscaling,
title={Downscaling satellite soil moisture using geomorphometry and machine learning},
author={Guevara, Mario and Vargas, Rodrigo},
journal={PloS one},
volume={14},
number={9},
pages={e0219639},
year={2019},
publisher={Public Library of Science San Francisco, CA USA}
}

T. Kitson, P. Olaya, E. Racca, M. Wyatt, M. Guevara, R. Vargas, a M. Taufer Data analytics for modeling soil moisture patterns across united states ecoclimatic domains. 2017 IEEE International Conference on Big Data (Big Data).2017. [link]

@inproceedings{kitson2017data,
title={Data analytics for modeling soil moisture patterns across united states ecoclimatic domains},
author={Kitson, Thomas and Olaya, Paula and Racca, Elizabeth and Wyatt, Michael R and Guevara, Mario and Vargas, Rodrigo and Taufer, Michela},
booktitle={2017 IEEE International Conference on Big Data (Big Data)},
pages={4768--4770},
year={2017},
organization={IEEE}
}

D. Warner,M Guevara, S. Inamdar, and R. Vargas. Upscaling soil-atmosphere CO2 and CH4 fluxes across a topographically complex forested landscape, Agricultural and Forest Meteorology, v.264, 2019 [link]

@article{warner2019upscaling,
title={Upscaling soil-atmosphere CO2 and CH4 fluxes across a topographically complex forested landscape},
author={Warner, Daniel L and Guevara, Mario and Inamdar, Shreeram and Vargas, Rodrigo},
journal={Agricultural and forest meteorology},
volume={264},
pages={80--91},
year={2019},
publisher={Elsevier}
}

Jimmy Landmesser

Graduate Student, University of Tennessee Knoxville

Paula Olaya

Doctoral Student, University of Tennessee Knoxville

Fred L. Martin

Collaborator