A spread sheet documenting the wetness conditions for various fields in the study site. Additionally, this sheet documents mean spectral responses for Landsat and Worldview 3 bands as well as tillage and wetness indices, and the estimated percentage of residue and raw water content.

A spread sheet documenting the wetness conditions for various fields in the study site. Additionally, this sheet documents mean spectral responses for Landsat and Worldview 3 bands as well as tillage and wetness indices, and the estimated percentage of residue and raw water content.

A spread sheet documenting the wetness conditions for various fields in the study site. Additionally, this sheet documents mean spectral responses for Landsat and Worldview 3 bands as well as tillage and wetness indices, and the estimated percentage of residue and raw water content.

These datasets are continuous parameter grids (CPG) of irrigated agriculture data (percent of basin classified as irrigated) for the years 2002, 2007, and 2012 in the Pacific Northwest. Source data was the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the United States (MIrAD-US), produced by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center.

These datasets are continuous parameter grids (CPG) of irrigated agriculture data (percent of basin classified as irrigated) for the years 2002, 2007, and 2012 in the Pacific Northwest. Source data was the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the United States (MIrAD-US), produced by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center.

,This dataset contains all data and code necessary to reproduce the analysis presented in the manuscript:,Winzeler, H.E., Owens, P.R., Read Q.D.., Libohova, Z., Ashworth, A., Sauer, T. 2022. 2022. Topographic wetness index as a proxy for soil moisture in a hillslope catena: flow algorithms and map generalization. Land 11:2018. DOI: 10.3390/land11112018.,There are several steps to this analysis. The relevant scripts for each are listed below. The first step is to use the raw digital elevation data (DEM) to produce different versions of the topographic wetness index (TWI) for the study region (Calculating TWI). Then, these TWI output files are processed, along with soil moisture (volumetric water content or VWC) time series data from a number of sensors located within the study region, to create analysis-ready data objects (Processing TWI and VWC). Next, models are fit relating TWI to soil moisture (Model fitting) and results are plotted (Visualizing main results). A number of additional analyses were also done (Additional analyses).,The DEM of the study region is archived in this dataset as SourceDem.zip. This contains the DEM of the study region (DEM1.sgrd) and associated auxiliary files all called DEM1.* with different extensions. In addition, the DEM is provided as a .tif file called USGS_one_meter_x39y400_AR_R6_WashingtonCO_2015.tif.,The remaining data and code files are archived in the repository created with a GitHub release on 2022-10-11, twi-moisture-0.1.zip. The data are found in a subfolder called data.,To reproduce the analysis in the manuscript run these scripts in the following order. The scripts are all found in the root directory of the repository. See the manuscript for more details on the methods.,Models were fit regressing soil moisture (average VWC for a certain time period) against a TWI index, with and without soil depth as a covariate. In each case, for both the model without depth and the model with depth, prediction performance was calculated with and without spatially-blocked cross-validation. Where cross validation wasn't used, we simply used the predictions from the model fit to all the data.,Preliminary visualization of results was done in a series of RMarkdown notebooks. All the notebooks follow the same general format, plotting model performance (observed-predicted correlation) across different combinations of time period and characteristics of the TWI indexes being compared. The indexes are grouped by SWI versus TWI, DEM filter used, flow algorithm, and any other parameters that varied.,The notebooks show the model performance metrics with and without the soil depth covariate, and with and without spatially-blocked cross-validation. Crossing those two factors, there are four values for model performance for each combination of time period and TWI index presented.,Some additional analyses were done that may not be published in the final manuscript but which are included here for completeness.,,

,The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The core of the 1997 experiment involves the deployment of the L-band Electronically Scanned Thinned Array Radiometer (ESTAR) for daily mapping of surface soil moisture. The region selected for investigation is the best instrumented site for surface soil moisture, hydrology and meteorology in the world. This includes the USDA/ARS Little Washita Watershed, the USDA/ARS facility at El Reno, Oklahoma, the ARM/CART central facility, as well as the Oklahoma Mesonet. The temporal coverage for this dataset is as follows: Begin datetime: 1995-10-01 00:00:00, End datetime: 2001-03-31 23:59:59. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Soil Texture Data Set is one of the various sub-surface data sets developed for the ARM/GCIP (Global Energy and Water Cycle Experiment (GEWEX) Continental-scale International Project) 1996 Near-Surface Observation (NESOB-96) Data Set. This data set contains a summary table of the percentages of sand, silt, and clay fractions in each soil layer at each of the ARM SWATS (Soil Water and Temperature System) sites at the SGP site. Also included is the corresponding USDA texture class as determined from the "soil triangle". The soil characterizations were perfomed by Oklahoma State University.,

This data set is comprised of several parameters from in situ measurements collected for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).

This data set is comprised of several parameters from in situ measurements collected for the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07).

This data set contains soil texture data that were extracted from a multi-layer soil characteristics database for the conterminous United States and generated for each regional study area. Data are representative of the conditions present in the regional study areas during the general timeline of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08) campaign.