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Annual soil moisture predictions across conterminous United States using remote sensing and terrain analysis across 1 km grids (1991-2016)


Authors: Mario Guevara Rodrigo Vargas
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Resource type: Composite Resource
Storage: The size of this resource is 2.7 GB
Created: Jun 20, 2019 at 4:01 p.m.
Last updated: Aug 10, 2019 at 2:03 p.m.
DOI: 10.4211/hs.b8f6eae9d89241cf8b5904033460af61
Citation: See how to cite this resource
Content types: File Set Content  Single File Content  Geographic Raster Content 
Sharing Status: Published
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Abstract

We provide 26 annual soil moisture predictions across conterminous United States for the years 1991-2016. These predictions are provided in raster files with a geographical (lat, long) projection system and a spatial resolution of 1 x 1 km grids (folder: soil_moisture_annual_grids_1991_2016). These raster files were populated with soil moisture data based on multiple kernel based machine learning models for coupling hydrologically meaningful terrain parameters (the explanatory variables) with soil moisture microwave records (the response variable) from the European Space Agency Climate Change Initiative. We provide a raster stack with the annual training data from satellite soil moisture estimates (file: annual_means_of _ESA_CCI_soil_moiture_1991_2016.tif) and the explanatory variables (terrain) calculated on SAGA GIS (System of Automated Geoscientific Analysis) using digital terrain analysis (folder: explanatory_variables_dem). The explained variance for all models-years was >70% (10-fold cross-validation). The 1 km soil moisture grids (compared to the original satellite soil moisture estimates) had higher correlations with field soil moisture observations from the North American Soil Moisture Database (n=668 locations with available data between 1991-2013; 0-5 cm depth) than soil moisture microwave records. For further information refer to our preprint in bioRxiv: https://www.biorxiv.org/content/biorxiv/early/2019/07/01/688846.full.pdf

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Resource Level Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
North Latitude
52.6083°
East Longitude
-59.6667°
South Latitude
24.3333°
West Longitude
-130.2333°

Temporal

Start Date:
End Date:

Content

README.txt

This folder contains the information required to reproduce the soil moisture predictions and the cross valdiation results presented in Guevara and Vargas 2019, Downscaling satellite soil moisture using geomorphometry and machine learning (in review Plos one).

The folder soil_moisture_annual_grids_1991_2016 contains the annual predictions of soil moisture across conterminous United States in 1x1 km grids. These files are provided in generic *.tif raster files.

The folder explanatory_variables_dem contains the explanatory variables (predictors) used for generating the soil moisture 1 km grids. These files represent multiple terrain features and they were calculated in SAGA GIS. These files are provided in a raster native format of SAGA GIS (*.sgrd). These files are described in a document table with the file in the same folder (explanatory_variables_description_SAGA_GIS_1km.docx). 

The values of soil moisture estimates from the European Space Agency Climate Change Initiative were averaged in an annual basis. This information was used as training data for the machine learning model using the aforementioned terrain parameters as prediction factors. This file has a spatial resolution of >25 km grids.  We provide a raster stack containing the annual soil moisture estimates used for trainig the models in a generic raster stack *.tif file (annual_means_of _ESA_CCI_soil_moiture_1991_2016.tif).

All analysis were performed in R. We also provide the R code for generating and validating the soil moisture predictions across CONUS (guevara_vargas_soil_moisture_geomorphometry_kknn.R). 

References

Sources

Derived From: For soil moisture: https://www.esa-soilmoisture-cci.org/node/137
Derived From: For terrain parameters: http://www.saga-gis.org/
Derived From: The source DEM: https://topex.ucsd.edu/sandwell/publications/124_MG_Becker.pdf
Derived From: For statistical computing: https://www.r-project.org/
Derived From: bioRxiv preprint: https://www.biorxiv.org/content/biorxiv/early/2019/07/01/688846.full.pdf

Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
National Science Foundation CIF21 DIBBs: PD: Cyberinfrastructure Tools for Precision Agriculture in the 21st Century 1724847
Mexican National Council for Science and Technology (CONACyT) PhD Fellowship 382790

Contributors

People that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name Organization Address Phone Author Identifiers
Michela Taufer University of Tennessee Min H. Kao Building, Room 620 1520 Middle Drive Knoxville, TN 37996-2250 865-974-9952 GoogleScholarID

How to Cite

Guevara, M., R. Vargas (2019). Annual soil moisture predictions across conterminous United States using remote sensing and terrain analysis across 1 km grids (1991-2016), HydroShare, https://doi.org/10.4211/hs.b8f6eae9d89241cf8b5904033460af61

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

 http://creativecommons.org/licenses/by/4.0/
CC-BY

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