ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)

**When using the GIS data included in these map packages, please cite all of the following:

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. http://doi.org/10.1371/journal.pone.0220457

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. https://doi.org/10.5281/zenodo.2572018

OVERVIEW OF CONTENTS

This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:

Raw DEM and Soils data

Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the National Elevation Dataset)

DEM_Individual_Tiles: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.

DEMs_Merged: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM.

Soils Data (Map services and data available from Natural Resources Conservation Service Web Soil Survey, U.S. Department of Agriculture)

Animas-Dolores_Area_Soils: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).

Cortez_Area_Soils: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).

ArcGIS Map Packages

Goodman_Watershed_Full_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).

Goodman_Watershed_Mesa-Only_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.

Crow_Canyon_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).

Coffey_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).

For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."

LICENSES

Code: MIT year: 2019 Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton

CONTACT

Andrew Gillreath-Brown, PhD Candidate, RPA Department of Anthropology, Washington State University andrew.brown1234@gmail.com – Email andrewgillreathbrown.wordpress.com – Web

This data product was created based on the hypothesis that a variety of ground truth observations of soil moisture could be combined to estimate equal soil moisture contours across a large area (e.g., the FIFE area). A second stage required interpolating from these contours, using the methods of spatial statistics, to average soil moisture values at the nodes of a uniform grid. The grid node values in this product represent the average soil moisture of a 0.5 km x 0.5 km area centered at the node location. The correlation area method (CAM) was used to combine in situ measurements and airborne gamma remote sensing estimates to obtain areal averages of soil moisture. Information on biomass and the spatial distribution of vegetation in a model was also used to estimate soil moisture from PBMR measurements. Another simple method, using only ground soil moisture data, was also used to compute soil moisture from the PBMR measurements. All soil moisture data collected from the aircraft platforms and ground measurements were entered into the ARC/INFO GIS along with the UTM coordinates of each observation. All available and usable measurements of soil moisture were considered in an analysis that produced isolines of soil moisture.

This data series represents the volumetric soil moisture (percent saturated soil) for the surface layer (<5 cm). The data is created daily and is averaged for the ISO standard week and month. The data is produced from passive microwave satellite data collected by the Soil Moisture and Ocean Salinity (SMOS) satellite and converted to soil moisture using version 6.20 of the SMOS soil moisture processor. The data are produced by the European Space Agency and obtained under a Category 1 proposal for Level 2 soil moisture data. The data are gridded to a resolution of 0.25 degrees. Data quality flags have been applied to remove areas where rainfall is present during the acquisition, where snow cover is detected and when Radio Frequency Interference (RFI) is above an acceptable threshold.

This data product was created based on the hypothesis that a variety of ground truth observations of soil moisture could be combined to estimate equal soil moisture contours across a large area (e.g., the FIFE area). A second stage required interpolating from these contours, using the methods of spatial statistics, to average soil moisture values at the nodes of a uniform grid. The grid node values in this product represent the average soil moisture of a 0.5 km x 0.5 km area centered at the node location. The correlation area method (CAM) was used to combine in situ measurements and airborne gamma remote sensing estimates to obtain areal averages of soil moisture. Information on biomass and the spatial distribution of vegetation in a model was also used to estimate soil moisture from PBMR measurements. Another simple method, using only ground soil moisture data, was also used to compute soil moisture from the PBMR measurements. All soil moisture data collected from the aircraft platforms and ground measurements were entered into the ARC/INFO GIS along with the UTM coordinates of each observation. All available and usable measurements of soil moisture were considered in an analysis that produced isolines of soil moisture.

Geospatial data about Data quality info for soil moisture and drought. Export to CAD, GIS, PDF, CSV and access via API.

The Global Forecast System (GFS) forecast 0-10cm soil-moisture data at 37.5km resolution is created at the NOAA Climate Prediction Center for the purpose of near real-time usage by the national and international relief agencies and the general public. The users of this data include the U.S. Geological Survey (USGS), the U.S. Agency for International Development (USAID), the Joint Agricultural Weather Facility (JAWF) and the national Meteorological Centers in Africa, Asia and South America. The data is disseminated in the binary format as well as in the form of shape and tiff files for use by the GIS community. The soil moisture data in the GIS format can be accessed at the online linkage provided above.

Soil moisture is important for plant growth. A lack of moisture content over a growing season is a good indicator of drought, which can have social, environmental, and economic impacts. Increasing temperatures and changes in rainfall patterns are expected to increase the frequency and intensity of drought in many regions. Growing season soil moisture deficits are estimated by the potential evapotranspiration deficit, the difference between rainfall and evapotranspiration.

This dataset shows annual average soil moisture (potential evapotranspiration deficit (PED)) across New Zealand for years 1972 to 2014.

Evapotranspiration is the loss of water by evaporation and plant transpiration. PED is the difference between estimated evapotranspiration and rainfall.

We produced maps of the annual PED total (in millimetres) for every growing season (calculated as July–June years) from 1972 to 2013. Care should be taken when comparing maps from year to year – days may be missing from the PED GIS data, and data may have been interpolated to complete the dataset. The interpolation accuracy is lowest in areas of high elevation, where there are fewer climate stations and complex terrain affects accuracy. Climate stations may also open and close, affecting the accuracy of the data provided.

This dataset relates to the "Soil moisture and drought" measure on the Environmental Indicators, Te taiao Aotearoa website.

Geometry: raster catalogue Unit: mm/yr

This service is built by processing the analysis assimilation output of the National Water Model land based data. All output of the land dataset have the same geospatial extent covering all of CONUS with partial coverage into Canada and Mexico. Currently, the only layer within the service is near-surface soil moisture saturation. The near-surface soil moisture saturation layer shows moisture saturation of the top 40cm of the soil. Model Output Version: v2.1See https://water.noaa.gov/about/nwm for further details about this data.Link to graphical web page: https://water.noaa.gov/mapLink to data download: https://nomads.ncep.noaa.gov/pub/data/nccf/com/nwm/prod for raw data files in netcdf format.

This dataset contains detailed USDA SSURGO soil information and mapped soil extents for the soils of Accomack and Northampton Counties on Virginia's Eastern Shore, including those areas of focused study by the Virginia Coast Reserve LTER project. This USDA soil data is collected and combined here to make it more accessible to VCRLTER researchers and students, in a more GIS-friendly format, and to supersede previous digitized versions of more generalized soil maps created by the VCRLTER and included as part of the 1995 VCRLTER-Northampton County GIS data archive (dataset VCR14219). Data was downloaded in Jan. 2014 and tabular data for both counties was imported into a MS Access database using the provided standard SSURGO US 2003 template. Spatial data for the two counties was merged together into a single ArcGIS shapefile and selected fields from the MAPUNIT and MUAGGATT tables were joined to the final shapefile's attribute table. Each polygon represents all or part of a SSURGO "mapunit", which may contain multiple component soils; usually very similar soils that grade together or else so heterogeneously mixed together at fine spatial scales to make mapping the component soils individually impractical. Also, each soil typically has multiple vertical soil horizons, each with its own distinct composition (mineral, textural, etc.) and other characteristics. Detailed information about component soils (including typical soil moisture, dry albedo, erodibility indices, taxonomic nomenclature, flooding and ponding characteristics, engineering, crop, forest, and habitat suitability indices and yield tables, and geomorphic descriptions) and component horizons (including horizon depths, grain size distributions, sand/silt/clay fractions, mineral and organic content, and pore space characteristics) is included in the MS Access database but NOT in the combined ArcGIS shapefile. Users interested in exploring or displaying component or horizon information may use the report and query forms within the MS Access database, or they may join selected database tables to the shapefile using the appropriate mukey, cokey, and chkey indices in a one-to-many join within a chosen GIS software.

This dataset defines the sample locations for various abiotic data collected on Konza Prairie (rain gauges, soil moisture, and stream data). Included in this are locations for 11 rain gauges (GIS300) on Konza Prairie. The Konza headquarters weather station consists of two gauges which are operated year-round. The remaining Konza-operated gauges run from April 1 to November 1. These data are to be used in conjunction with the APT01 (precipitation) dataset. GIS305 contains the locations where measurements of soil moisture (%volume) are taken on Konza Prairie. These data are to be used in conjunction with the ASM01 (soil moisture) dataset. GIS315 defines the locations of stream gauges (5 including one operated by the USGS*) in the Kings Creek watershed. (*http://waterdata.usgs.gov/nwis/nwisman/?site_no=06879650)

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

Observed and projected changes in extreme values of key climate variables.

Data source: http://www.eea.europa.eu/data-and-maps/data/external/ensembles-fp6-project, http://www.eea.europa.eu/data-and-maps/data/external/euro-cordex

The World Inventory of Soil Emission Potentials (WISE) database currently contains data for over 4300 soil profiles collected mostly between 1950 and 1995. This database has been used to generate a series of uniform data sets of derived soil properties for each of the 106 soil units considered in the Soil Map of the World (FAO-UNESCO, 1974). These data sets were then linked to a 1/2 degree longitude by 1/2 degree latitude version of the edited and digital Soil Map of the World (FAO, 1995) to generate GIS raster image files for the following variables: Total available water capacity (mm water per 1 m soil depth) soil organic carbon density (kg C/m**2 for 0-30cm depth range) soil organic carbon density (kg C/m**2 for 0-100cm depth range) soil carbonate carbon density (kg C/m**2 for 0-100cm depth range) soil pH (0-30 cm depth range) soil pH (30-100 cm depth range) Data Citation: The data set should be cited as follows: Batjes, N. H. (ed). 2000. Global Data Set of Derived Soil Properties, 0.5-Degree Grid (ISRIC-WISE). Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.

Hydrologic data, primarily precipitation and runoff, have been collected on experimental watersheds operated by the U.S. Department of Agriculture Agricultural Research Service (USDA-ARS) and on other lands in southeastern Arizona since the 1950s. These data are of national and international importance and make up one of the most comprehensive semiarid watershed data sets in the world. The USDA-ARS Southwest Watershed Research Center has recently developed an electronic data processing system that includes an online interface (https://tucson.ars.ag.gov/dap) to provide public access to the data. The goal of the system is to promote analyses and interpretations of historic and current data by improving data access. The publicly accessible part of the system consists of an interactive Web site, which provides an interface to the data, and a relational database, which is used to process, store, and manage data. In addition, DAP was expanded to put sediment, meteorological, soil moisture and temperature, vegetation, CO2 and water flux, geographic information system (GIS) and aircraft and satellite spectral imagery data on line and to publish metadata for all WGEW long-term measurements. Resources in this dataset:Resource Title: PDF File. File Name: WGEW Soil Survey.pdf, url: https://www.tucson.ars.ag.gov/dap/Files/WGEW Soil Survey.pdf

ABSTRACT Soil erosion is currently one of the main concerns in agriculture, water resources, soil management and natural hazards studies, mainly due to its economic, environmental and human impacts. This concern is accentuated in developing countries where the hydrological monitoring and proper soil surveys are scarce. Therefore, the use of indirect estimates of soil loss by means of empirical equations stands out. In this context, the present study proposed the assessment of the Revised Universal Soil Loss Equation (RUSLE) with the aid of Geographical Information Systems (GIS) and remote sensing for two agricultural watersheds in southern Rio Grande do Sul - Brazil. Among all RUSLE factors, LS showed the closest patterns to the local when compared to the total annual soil loss, thus being a good indicator t of risk areas. The total annual soil loss varied from 0 to more than 100 t ha-1 yr-1, with the vast majority (about 65% of the total area) classified from slight to moderate rates of soil loss. The results estimated according to RUSLE indicated that over 10% of the study area presented very high to extremely high soil loss rates, thus requiring immediate soil conservation practices. The present study stands out as an important scientific and technical support for practitioners and decision-makers, being probably the first of its nature applied to extreme southern Brazil.

Geospatial data about Growing season soil moisture deficit 1977 1978. Export to CAD, GIS, PDF, CSV and access via API.

Passive Microwave Soil Moisture Map Market Outlook

According to our latest research, the global passive microwave soil moisture map market size reached USD 1.09 billion in 2024, driven by increasing demand for precision agriculture, climate monitoring, and disaster management applications. The market is experiencing robust growth, with a recorded CAGR of 7.6% between 2025 and 2033. Based on this growth trajectory, the market is forecasted to reach USD 2.11 billion by 2033. Key growth factors include technological advancements in remote sensing, rising awareness about sustainable land and water usage, and the integration of satellite-based data into real-time monitoring platforms.

The expansion of the passive microwave soil moisture map market is significantly propelled by the increasing adoption of precision agriculture techniques worldwide. As food security becomes a top priority for governments and private entities, there is a growing emphasis on optimizing irrigation and crop management practices. Passive microwave soil moisture mapping provides highly accurate, near-real-time data that enables farmers and agribusinesses to make data-driven decisions, minimize water wastage, and enhance crop yields. The integration of these maps with smart farming solutions and IoT devices further amplifies their utility, making them indispensable tools in modern agricultural operations. Additionally, the proliferation of affordable satellite-based and ground-based sensing technologies is making these solutions accessible to a broader range of stakeholders, from smallholder farmers to large agricultural conglomerates.

Another critical driver of market growth is the rising need for effective hydrological and climate monitoring systems. As climate change intensifies, the frequency and severity of droughts, floods, and other extreme weather events are increasing globally. Passive microwave soil moisture maps play a vital role in monitoring soil moisture dynamics, detecting anomalies, and forecasting hydrological risks. These maps provide essential data for water resource management, flood prediction, and drought assessment, enabling governments and disaster management agencies to implement timely mitigation strategies. The integration of passive microwave data with advanced climate models and geographic information systems (GIS) enhances the accuracy of environmental forecasts, supporting proactive decision-making and resource allocation.

Furthermore, the market is benefiting from growing investments in research and development, particularly in the fields of environmental monitoring and disaster management. Governments, international organizations, and private sector players are increasingly funding projects aimed at improving soil moisture mapping accuracy and expanding the spatial and temporal coverage of data. The deployment of new-generation satellites equipped with advanced passive microwave sensors is enhancing the resolution and reliability of soil moisture maps. Additionally, collaborations between research institutes, commercial enterprises, and government agencies are fostering innovation in data analytics, machine learning, and cloud-based platforms, driving the adoption of passive microwave soil moisture mapping solutions across diverse end-user segments.

From a regional perspective, North America and Europe are leading the market, driven by strong government support for climate and environmental monitoring initiatives, advanced technological infrastructure, and high awareness levels among end-users. The Asia Pacific region is emerging as a high-growth market, fueled by rapid agricultural modernization, increasing investments in satellite technology, and growing concerns about water scarcity and food security. Latin America and the Middle East & Africa are also witnessing steady growth, supported by expanding applications in agriculture and disaster management. As market penetration deepens across developing regions, the passive microwave soil moisture map market is poised for sustained expansion in the coming years.

Pro

The Soil and Terrain database for South Africa primary data (version 1.0), at scale 1:1 million (SOTER_South_Africa), was compiled of enhanced soil information within the framework of the FAO's program Land Degradation Assessment in Drylands (LADA). Primary soil and terrain data for South Africa were obtained from the SOTERSAF database (ver. 1) at scale 1:2 million. This version of SOTER_South_Africa includes some changes in the GIS file, based on the SRTM-DEM derived data and a changes of the attributes database.

SOTER forms a part of the ongoing activities of ISRIC, FAO and UNEP to update the world's baseline information on natural resources.The project involved collaboration with national soil institutes from the countries in the region as well as individual experts.

Version 3.1 of the ISRIC-WISE database (WISE3) was compiled from a wide range of soil profile data collected by many soil professionals worldwide. All profiles have been harmonized with respect to the original Legend (1974) and Revised Legend (1988) of FAO-Unesco. Thereby, the primary soil data ─ and any secondary data derived from them ─ can be linked using GIS to the spatial units of the digitized Soil Map of the World as well as more recent digital Soil and Terrain (SOTER) databases through the soil legend code.

WISE3 holds selected attribute data for some 10,250 soil profiles, with some 47,800 horizons, from 149 countries. Individual profiles have been sampled, described, and analyzed according to methods and standards in use in the originating countries. There is no uniform set of properties for which all profiles have analytical data, generally because only selected measurements were planned during the original surveys. Methods used for laboratory determinations of specific soil properties vary between laboratories and over time; sometimes, results for the same property cannot be compared directly. WISE3 will inevitably include gaps, being a compilation of legacy soil data derived from traditional soil survey, which can be of a taxonomic, geographic, and soil analytical nature. As a result, the amount of data available for modelling is sometimes much less than expected. Adroit use of the data, however, will permit a wide range of agricultural and environmental applications at a global and continental scale (1:500 000 and broader).

Preferred citation: Batjes NH 2009. Harmonized soil profile data for applications at global and continental scales: updates to the WISE database. Soil Use and Management 5:124–127, http://dx.doi.org/10.1111/j.1475-2743.2009.00202.x

This dataset contains the geographic boundaries of the Alaska Soil and Water Conservation Districts. Districts are formed under the provisions of Alaska Statute 41.10.130 and self-prescribe their district boundaries. The boundaries contained here-in were approved in a review process conducted with the individual soil and water conservation districts in 2010-2011 and in subsequent district boundary expansions and will be updated as necessary. For more information about soil and water conservation districts in Alaska please contact the Natural Resource Conservation and Development Board at http://dnr.alaska.gov/commis/nrcdb/index.html.