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.

,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.,,

This dataset contains summary tables of land cover from the Cropland Data Layer (CDL) for individual stream catchments of the conterminous United States from the National Hydrography Dataset Plus Version 2.1 (United States Department of Agriculture National Agricultural Statistics Service, 2024; McKay and others, 2012). These data were summarized from primarily 30 meter grid cell raster data for the years 2000 through 2022. This data release contains 23 parquet tables that can be linked to the NHD Plus v2 dataset using the COMID unique identifier and a column for each CDL land cover class. From 2008 onwards, these data are available for the conterminous United States. From 2000 to 2007, the CDL is only available for select states. For convenience, an additional parquet table is included with a column for COMID and columns containing a flag indicating whether CDL data exist for each year from 2000 through 2022. Parquet tables can be accessed using the "arrow" package in RStudio. An example command to open a file is: arrow::read_parquet(filepath, "cdl_2000_table.parquet"))

This dataset contains summary tables of land cover from the Cropland Data Layer (CDL) for individual stream catchments of the conterminous United States from the National Hydrography Dataset Plus Version 2.1 (United States Department of Agriculture National Agricultural Statistics Service, 2024; McKay and others, 2012). These data were summarized from primarily 30 meter grid cell raster data for the years 2000 through 2022. This data release contains 23 parquet tables that can be linked to the NHD Plus v2 dataset using the COMID unique identifier and a column for each CDL land cover class. From 2008 onwards, these data are available for the conterminous United States. From 2000 to 2007, the CDL is only available for select states. For convenience, an additional parquet table is included with a column for COMID and columns containing a flag indicating whether CDL data exist for each year from 2000 through 2022. Parquet tables can be accessed using the "arrow" package in RStudio. An example command to open a file is: arrow::read_parquet(filepath, "cdl_2000_table.parquet"))

,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 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 High Plains Climate Network (HPCN) dataset is one of various datasets provided for the Southern Great Plains 1997 (SGP97) project. This dataset contains HPCN data from 15 stations in the SGP97 domain. This dataset covers the complete SGP97 time period (18 June 1997 through 18 July 1997) and for the SGP97 domain. The SGP97 domain is approximately 97W to 99W longitude and 34.5N to 37N latitude. The HPCN dataset contains different parameters depending upon the reporting station. Each station provides Station Name, State, and Identification Number preceding that station's data within the dataset. Each parameter column has a self explanatory title indicating the data available for that station and parameter units.,

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.

■경사농지의 지형·이용 특성과 위치 정보를 담은 농지 현황 데이터셋■지번, 지목, 면적, 고도, 경사, 공시지가, 좌표 등 주요 항목 포함 1. 농업용지 개발 및 보전 전략 수립2. 스마트팜 부지 적합성 분석3. 재해 취약 농지 모니터링