Soil Survey Geographic Data Research Articles

Usability of soil survey soil texture data for soil health indicator scoring

ABSTRACTSoil textural information is an important component underlying other soil health indicators. Soil texture analysis is a common procedure, but it can be labor intensive and expensive. Soil texture data typically are available from the Soil Survey Geographic (SSURGO) database, which may be an option for determining soil health texture groups (SHTG). The SSURGO database provides soil texture information in the soil map unit (SMU) name, taxonomic class category (family), and detailed values (≤ 2 mm soil fraction) of percent sand, silt and clay by soil horizon. The objective of this study was to examine the possibility of using SSURGO data for SHTG at the 147-ha Cornell University Willsboro Research Farm in New York state as an alternative for soil texture data determined manually on collected soil core samples. Comparative results revealed that representative values for soil texture from the SSURGO database generally matched measured mean values for all SMUs.

Comparing SSURGO Data with Geospatial Field Measurements to Estimate Soil Texture and Infiltration Rate Classes in Glaciated Soils

ABSTRACTThe infiltration rate (IR) of water is a key soil property related to hydrological processes, soil health, and ecosystem services. However, detailed measurements of IR in the field and/or laboratory are labor-intensive and expensive to perform. Soil judging in the field provides a rapid and inexpensive method to estimate IR classes based on soil texture, soil organic carbon/matter, and soil structure. The objectives of this study were to classify and compare soil texture and IR for the A horizon across the 147 ha Cornell University Willsboro Research Farm using the Soil Survey Geographic (SSURGO) database and field-based measurements. Soil texture was the dominating factor to explain the general trend of Entisols > Inceptisols > Alfisols with regard to IR in the A horizon. In general, the variability in soil texture observed in field measurements was consistent with the variability reported in the SSURGO database, although the SSURGO representative values for soil texture did not completely match measured mean values for all soil map units. With the exception of one soil map unit, estimates of IR classes utilizing soil judging in the field criteria also were consistent when using either SSURGO or field-based data. Estimating infiltration rate classes for ecosystem services frameworks using geospatial analysis of field and/or SSURGO data can be enhanced with emerging technologies (e.g., sensors) and/or easily measured conventional soil properties.

Comparing soil carbon estimates in glaciated soils at a farm scale using geospatial analysis of field and SSURGO data

Soil carbon is a key soil property related to ecosystem services and it is often used in soil carbon content estimates at various scales. Uncertainties in soil carbon estimates often arise from variability in field, laboratory, and/or geospatial data at a farm scale. The objectives of this study were to quantify and compare levels of soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TC) for a 147-hectare field site in upstate New York based on three alternative analysis procedures: a) using carbon concentrations reported by the Soil Survey Geographic (SSURGO) spatial databases for each soil map unit (SMU) present at the field site and applying that value across each SMU; b) averaging the carbon contents of soil cores collected within a specific SMU boundary and applying the averaged value across each SMU; and c) interpolating carbon contents across the field site based on the individual soil cores. Maps of SOC, SIC, and TC contents based on the interpolated core samples were different from maps created by applying averaged core results or SSURGO values across the SMUs. Differences in the magnitudes and spatial distributions of carbon can be attributed to several factors. For example, SSURGO soil carbon values are frequently measured for a selected pedon(s) from a “type location” and not from the actual study location. These “type locations” can be located far from study sites and even in different states. Also, SSURGO soil carbon values may overestimate the actual contents when compared to systematic field measurements because the SSURGO values at lower depths are often extrapolated from upper soil horizons. Such extrapolations affect inorganic carbon to a much greater extent than organic carbon, therefore better agreement is observed in the present study between SOC estimates from the SSURGO database and field measurements. Because regional and/or global carbon estimates are rarely made with detailed field data due to the high costs of field and laboratory measurements, additional field sampling is needed to constrain and improve these estimates and also to assess the potential variability present.

Capturing LiDAR‐Derived Hydrologic Spatial Parameters to Evaluate Playa Wetlands

AbstractThe digital elevation model data from traditional stereo photogrammetric methods are inadequate in providing accurate vertical parameters to feed hydrologic models for low‐lying, extremely flat areas. High‐resolution light detection and ranging (LiDAR) data provide the robust capability of capturing small variations in low‐relief playa wetlands. The Rainwater Basin in south‐central Nebraska includes a complex of seasonally shallow playa wetlands that attract millions of migratory waterfowl every spring and fall. This research focuses on the development of a procedure with applicable protocols to produce LiDAR‐derived three‐dimensional wetland maps and to extract the critical surface parameters (i.e., watershed boundaries, flow direction, flow accumulation, and drainage lines) for playa wetlands. The topo‐hydrologic conditions of playa wetlands were evaluated at the watershed level. The results show that in the Rainwater Basin, 70.7% of the historic hydric soil footprints identified in the Soil Survey Geographic (SSURGO) database were not functioning as topographically depressional wetlands. This finding was confirmed by a recent five‐year Annual Habit Survey showing that 69.8% of the historic hydric soil footprints did not function during the spring migratory bird seasons between 2004 and 2009. The majority of playa wetlands' topographic conditions have been substantially changed and the SSURGO data cannot fully reflect current topographic reality in the Rainwater Basin.

Semi-automated disaggregation of conventional soil maps using knowledge driven data mining and classification trees

Disaggregation of conventional soil surveys has been identified as a potential source for much of the next generation of model-ready digital soil spatial data. This process aims to apportion vector soil surveys into raster (gridded) representations of the component soils that are often aggregated together in map unit designs. Most soil surveys are published with some description of the soil–landscape relationships that distinguish component soils within map units. We used these descriptions found in the Soil Survey Geographic (SSURGO) database of Webster and Pocahontas Counties in West Virginia, USA, to build a set of representative training areas for all soil components by using 1-arc second digital elevation data and derived geomorphic indices. These training areas were then used in classification tree ensembles with a more extensive environmental database to transform the original SSURGO map into a gridded soil series map. We created underlying prediction frequency surfaces from the models that can be used for creating continuous representations of soil class and property distributions.Disaggregation models matched training sets in 71%–74% of pixels and matched components in original SSURGO map units in 56%–65% of the study area. We evaluated both the original SSURGO data and our models using 87 independent pedons not used in model building. Validation pedons matched components in SSURGO map units at 39% of sites, but in map units that only included one named component (as opposed to multiple soils that could be matched to validation pedons) only 27% of the sites matched. Disaggregation predictions matched validation pedon classes 22–24% of the time using nearest neighbor spatial matches, and these rates increased to 39–44% for correct predictions within a 60-meter radius of the pedon. To characterize uncertainty, we compared relative ensemble prediction frequency (probability) of final hardened model classes at validation sites. Sites with correct predictions had generally higher prediction frequencies; which lead us to use them to create an uncertainty model. Uncertainty was calculated by determining the rate of correct predictions at validation sites within different intervals of prediction frequencies using nearest neighbor validation results. We were able to discern four uncertainty classes with values of 7%, 18%, 20% and 43%, which we called “ground truth probabilities”. We present the methods to create these models as a specific example of how disaggregation techniques may be used to aid in updating national soil survey inventories.

Aggregation Strategies for SSURGO Data: Effects on SWAT Soil Inputs and Hydrologic Outputs

The USDA-NRCS Soil Survey Geographic (SSURGO) dataset provides the most comprehensive, detailed soil data coverage across the United States. Correct usage of these data within a hydrologic model depends on assumptions that define how soil property data are aggregated. To reduce data intensity and improve model efficiency, most hydrologic modeling studies using SSURGO assume that soil property data are adequately grouped into some notion of a “soil type” which is represented by the map unit, denoted as map unit key (MUkey), within SSURGO. However, the map unit is not intended for this purpose as continuity in map unit design or composition is not guaranteed between adjacent surveys of different vintages. This causes problems when several survey areas are used together, because similar soils are assigned a different map unit across the boundaries of soil survey maps. We present a methodology for aggregating soil data among multiple soil survey areas according to soil taxonomic information available in SSURGO. Results indicate that the aggregation method provides an acceptable representation of soil parameter values and distributions while eliminating the reliance on an arbitrary map unit for soil type identification. The results of the hydrologic modeling using the Soil and Water Assessment Tool (SWAT) in the San Joaquin River Watershed indicate that the commonly used aggregation method and the newly developed method satisfactorily estimated soil and surface hydrologic processes as compared to using a non-aggregated soil dataset. For the soil hydrologic processes, the SWAT model output from our aggregation method accurately estimated soil water content (mean difference compared to the non-aggregated soil dataset output of −4 mm for western San Joaquin River Watershed subbasins and 15 mm for the eastern San Joaquin River watershed subbasins) and lateral flow (3 mm for the western subbasins and 0.2 mm for the eastern subbasins) as compared to using a non-aggregated soil dataset. For the surface hydrologic processes, the SWAT model under predicted surface runoff (−0.5 mm for the western subbasins and −0.1 mm for the eastern subbasins) and sediment yield (−0.02 t/ha for the western subbasins and −9×10−4 t/ha for the eastern subbasins) as compared to using a non-aggregated soil dataset. While some variations were statistically significant, the differences were numerically small. The results show that soil taxonomy provides a robust framework for grouping soils.

Effect of Soil Data Resolution on Identification of Critical Source Areas of Sediment

Identification of critical source areas (CSAs) of pollution in a watershed is important for effective implementation of best man- agement practices (BMPs). Process-based watershed models are often used for this purpose. One of the main inputs to these models is the spatially explicit soils data. The objective of this study was to evaluate whether the use of two commonly used soil data sets, the State Soil Geographic (STATSGO) and the Soil Survey Geographic (SSURGO) data, can lead to differences in location of CSAs of sediment. A watershed model, Soil and Water Assessment Tool (SWAT), in combination with the Tukey-Kramer test was used for locating CSAs in the Fish River watershed located in coastal Alabama. The model was calibrated and validated using flow data from a U.S. Geological Survey (USGS) gauging station located within the watershed. The locations of the CSAs of sediment were analyzed at subwatershed and hydrologic response unit (HRU) levels. Results show that the locations of the CSAs were different for the two soil data sets. The locations of the CSAs varied at both subwatershed and HRU levels. The use of STATSGO soil data resulted in higher soil erodibility factor and surface runoff. As a result, higher sediment yield was obtained from the use of the STATSGO data as compared with the sediment yield obtained from the use of the SSURGO data. Therefore, for accurate identification of CSAs of sediment (and potentially other pollutants) and for effective imple- mentation of economically feasible BMPs, it is important to use the most detailed spatial data set available. DOI: 10.1061/(ASCE)HE .1943-5584.0000318. © 2011 American Society of Civil Engineers.

An ArcGIS Toolkit to Assist in Using Digital Soil Data for BAER Erosion Hazard Ratings

After a wildfire, understanding a landscape's response to precipitation is essential. Land that has been stripped of its vegetation and soils that inhibit infiltration create a situation that can have disastrous effects, including severe erosion and landslides. Increased sediment in streams from soil erosion can result in fish kills and degraded water quality. Soil scientists play a key role in post-wildfire assessments and rehabilitation. The formal team assigned to assess post-fire landscape damage is known as a BAER (Burned Area Emergency Response) team. BAER teams must complete their assessment within 7 d of a fire's containment, and having soil survey data available makes the job much easier. One of the tasks for soil scientists on BAER teams is to assess the potential for erosion in areas damaged by the fire. The Smokey BEHR (BAER Erosion Hazard Rating) Toolkit for GIS isolates soil survey data within the fire perimeter and distills some of the most important soil attributes from SSURGO (Soil Survey Geographic) data into a readily usable format. Combined with burn severity information from a Burned Area Reflectance Classification (BARC) raster layer, areas with high erosion hazard can be quickly identified and targeted for treatment.

Applications of GIS to Citriculture in South Texas

The South Texas citrus industry needs an inventory of soil properties within existing citrus (Citrus spp.) orchards, wants data at the county level showing soils that are suitable for citrus production, and would value any information related to the establishment of citrus orchards. This study discusses integration of citrus, soil survey geographic data (SSURGO), and U.S. Census spatial and tabular data with geographical information system (GIS) technology for citriculture. For this study, Hidalgo County Texas was evaluated because it is the major citrus producing county in South Texas. The spatial and tabular data and commercial GIS software were used to inventory selected soil chemical and physical properties within citrus groves, to identify orchards that may be affected by urban expansion, and to select potential sites for establishing new citrus orchards. Results indicated that citrus, SSURGO, and U.S. census spatial and tabular data integrated with GIS technology can be a powerful tool for citriculture. The information provided in this study should appeal to producers, extension agents, scientists, and government agencies within the U.S. and abroad.

EFFECTS OF STATSGO AND SSURGO AS INPUTS ON SWAT MODEL'S SNOWMELT SIMULATION1

ABSTRACT: Soil data comprise a basic input of SWAT (Soil and Water Assessment Tool) for a watershed application. For watersheds where site specific soil data are unavailable, the two U.S. Department of Agriculture (USDA) soil databases, the State Soil Geographic (STATSGO) and Soil Survey Geographic (SSURGO) databases, may be the best alternatives. Although it has been noted that SWAT models using the STATSGO and SSURGO data may give different simulation results for water, sediment, and agricultural chemical yields, information is scarce on the effects of using these two databases in predicting streamflows that are predominantly generated from melting snow in spring. The objective of this study was to assess the effects of using STATSGO versus SSURGO as an input for the SWAT model's simulation of the streamflows in the upper 45 percent of the Elm River watershed in eastern North Dakota. Designating the model as SWAT‐STATSGO when the STATSGO data were used and SWAT‐SSURGO when the SSURGO data were used, SWAT‐STATSGO and SWAT‐SSURGO were separately calibrated and validated using the observed daily streamflows. The results indicated that SWAT‐SSURGO provided an overall better prediction of the discharges than SWAT‐STATSGO, although both did a good and comparable job of predicting the high streamflows. However, SWAT‐STATSGO predicted the low streamflows more accurately and had a slightly better performance during the validation period. In addition, the discrepancies between the discharges predicted by these two SWAT models tended to be larger at upstream locations than at those farther downstream within the study area.

Field-Truthing of USDA-Natural Resources Conservation Service Soil Survey Geographic Data on Hunewell Ranch, Erath County, Texas

Samples from the surface 10 cm of soils of Tarleton State University's Hunewell Ranch in Erath County, Texas were collected and subjected to physical and chemical laboratory analyses. The samples were collected within map unit polygons that represented 12 soil series. Results of the laboratory analysis were compared with the ranges of Soil Survey Geographic (SSURGO) data to determine the relationship between datasets. Parameters evaluated were soil color, texture, and reaction (pH). Soil texture and reaction showed weak relationships between SSURGO data and collected samples, while color exhibited stronger relationships. Areas displaying the greatest amount of variation from SSURGO data were erosional areas and transitional areas between map unit boundaries. Further studies are planned to investigate the extent of transitional areas.