DOI: 10.7910/dvn/t8wbsw
title: A Raster of Remotely Sensed Agricultural Suitability (S) in Utah, U.S.A.

description: S is a probability of cultivation based on a series of environmental conditions on a global scale. Here, S is created to compare settlement locations throughout Utah to explain initial Euro-American settlement of the region. S is one of two proxies created specifically for Utah for comparison of environmental productivity throughout the state. The data are presented as a raster file where any one pixel represents the probability of cultivation from zero to one, normalized on a global scale (Ramankutty et al., 2002). Because S is normalized on a global scale, the range of values of S for Utah U.S.A does not cover the global spectrum of S, thus the highest S value in the data is 0.51.
&lt;p&gt;
&lt;p&gt;
S was originally created by Ramankutty et al. (2002) on a global scale to understand probability of cultivation based on a series of environmental factors. The Ramankutty et al. (2002) methods were used to build a regional proxy of agricultural suitability for the state of Utah. Adapting the methods in Ramankutty et al. (2002), we created a higher resolution dataset of S specific to the state of Utah. S is composed of actual and potential evapotranspiration rates from 2000-2013, growing degree days, soil carbon density, and soil pH.
&lt;p&gt;
The Moisture Index is calculated as:
&lt;p&gt;
&lt;i&gt;MI = ETact /PET&lt;/i&gt;
&lt;p&gt;
Where ETact is the actual evapotranspiration and PET is the potential evapotranspiration. This calculation results in a zero to one index representing global variation in moisture. MI was calculated for the study area (Utah) using a raster of annual actual ETact and PET evapotranspiration data from 2000 to 2013 derived from the MODIS instrumentation (Mu, Zhao, & Running, 2011; Mu, Zhao, & Running, 2013; Numerical Terradynamic Simulation Group, 2013). Using ArcMap 10.3.1 Raster Calculator (Spatial Analyst), a raster dataset is created at a resolution of 2.6 kilometers.containing values representative of the average Moisture Index for Utah over a period of fourteen years (ESRI, 2015). The data were collected remotely by satellite (MODIS) and represents reflective surfaces (urban areas, lakes, and the Utah Salt Flats) as null values in the dataset. Areas of null values that were not bodies of water were interpolated using Inverse Distance Weighting (3d Analyst) in ArcMap 10.3.1 (ESRI, 2015).
&lt;p&gt;
The probability of cultivation (S) is calculated as a normalized product of growing degree days (GDD), available moisture (MI), soil carbon density (Csoil), and soil pH (pHsoil). The equation is divided into two general components: 
&lt;p&gt;
&lt;i&gt;S = Sclim * Ssoil&lt;/i&gt;
&lt;p&gt;
where
&lt;p&gt;
&lt;i&gt;Sclim = f1(GDD) f2(MI)&lt;/i&gt;
&lt;p&gt;
and
&lt;p&gt;
&lt;i&gt;Ssoil = g1(Csoil) g2(pHsoil)&lt;/i&gt;
&lt;p&gt;
Climate suitability (Sclim) is calculated as a normalized probability density function of cropland area to Growing Degree-days (f1[GDD]) and probability density function of cropland area to Moisture Index (f2[MI]) (Ramankutty et al. 2002). Soil suitability (Ssoil) is calculated using a sigmoidal function of the soil carbon density and soil acidity/alkalinity. The optimum soil carbon range is from 4 to 8 kg of C/m2 and the optimum range of soil pH is from 6 to 7 (Ramankutty et al. 2002). The resulting S value varies from zero to one indicating the probability of agricultural on a global scale. 
&lt;p&gt;
To implement the equation for S, growing degree-days (GDD) are calculated using usmapmaker.pl Growing Degree-days calculator and PRISM climate maps with a minimum temperature threshold of 50 degrees Fahrenheit (Coop, 2010; Daly, Gibson, Taylor, Johnson, & Pasteris, 2002; Willmott & Robeson, 1995; “US Degree-Day Map Maker,” n.d.). Moisture Index data is calculated as described above. To calculate the overall climate suitability (Sclim), the resulting raster datasets of Growing Degree-days and Moisture Index are combined in ArcMap 10.3.1 using the Raster Calculator (Spatial Analyst) to create climate suitability (Sclim) raster dataset with a resolution of 2.6 kilometers sq. To calculate soil suitability, the functions provided by Ramankutty et al. (2002) are applied to soil data derived from the SSURGO soil dataset compiled using NRCS Soil Data Viewer 6.1 to create thematic maps of average soil pH within the top 30 centimeters and average carbon density within the top 30 centimeters ( Soil Survey Staff, 2015; NRCS Soils, n.d.). However, there are missing values in the SSURGO soil dataset for the state of Utah, resulting in datasets using soil pH to have null values in portions of the state (Soil Survey Staff, 2015). The resulting raster datasets of soil pH and carbon density are combined in ArcMap 10.3.1 using the Raster Calculator (Spatial Analyst) to create a soil suitability (Ssoil) raster dataset with a resolution of 9.2 kilometers sq (ESRI, 2015). The climate suitability raster dataset and soil suitability raster dataset are combined in ArcMap 10.3.1 using the Raster Calculator (Spatial Analyst) generating a S raster dataset with a resolution of 9.2 kilometers (ESRI, 2015).
&lt;p&gt;
Projection: GCS_WGS_1984
&lt;p&gt;
&lt;b&gt;Citations&lt;/b&gt;
&lt;p&gt;
Coop, L. B. (2010). U. S. degree-day mapping calculator and publisher – Documentation Version 4.0. Oregon State University Integrated Plant Protection Center Web Site Publication E.10-04-2:http://uspest.org/wea/mapmkrdoc.html
&lt;p&gt;
Daly, C., Gibson, W. P., Taylor, G. H., Johnson, G. L., &amp; Pasteris, P. (2002). A knowledge-based approach to the statistical mapping of climate. Climate Research, 22(2), 99–113.
&lt;p&gt;
ESRI. (2015). ArcGIS Desktop: Release (Version 10.3.1). Redlands, CA: Environmental Systems Research Institute.
&lt;p&gt;
Mu, Q., Zhao, M., & Running, S. W. (2013). MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA MOD16A2/A3). Algorithm Theoretical Basis Document, Collection, 5. Retrieved from http://www.ntsg.umt.edu/sites/ntsg.umt.edu/files/MOD16_ATBD.pdf
&lt;p&gt;
Mu, Q., Zhao, M., &amp; Running, S. W. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8), 1781–1800.
&lt;p&gt;
NRCS Soils. (2015). Soil Data Viewer (Version 6.1). Natural Resources Conservation Service. Retrieved from http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcs142p2_053619
&lt;p&gt;
Numerical Terradynamic Simulation Group. (2014, July 29). MODIS Global Evapotranspiration Project (MOD16). University of Montana.
&lt;p&gt;
Ramankutty, N., Foley, J. A., Norman, J., & Mcsweeney, K. (2002). The global distribution of cultivable lands: current patterns and sensitivity to possible climate change. Global Ecology and Biogeography, 11(5), 377–392. http://doi.org/10.1046/j.1466-822x.2002.00294.x
&lt;p&gt;
Soil Survey Staff. (n.d.). Web Soil Survey. United States Department of Agriculture. Retrieved from http://websoilsurvey.nrcs.usda.gov/
&lt;p&gt;
U.S. Degree-Day Map Maker. (n.d.). Retrieved August 12, 2014, from http://pnwpest.org/cgi-bin/usmapmaker.pl\
&lt;p&gt;
Willmott, C. J., & Robeson, S. M. (1995). Climatologically aided interpolation (CAI) of terrestrial air temperature. International Journal of Climatology, 15(2), 221–229. http://doi.org/10.1002/joc.3370150207
description: Download the probability of cultivation (S) data below. If you have any questions or concerns, please contact me at PYaworsky89@gmail.com.