The Need to Continue Improving Soil Survey Maps

Peer Reviewed Papers

The Need to Continue Improving Soil Survey Maps Bradley A. Miller Soil Survey maps are the preeminent data set collected about our environment. Although there are other impressive data sets that are regularly used for studying and utilizing the environment, none match the wide utility and potential of soil maps. Today, practically every hectare of soil that can be reached in the United States has been mapped by the Natural Resource Conservation Service. Because the Soil Survey is a natural resource inventory it is tempting to consider the finished product the end product. However, many users would benefit from the availability of more accurate and precise soil maps. Also, new demands are being put on the Soil Survey data through the use of GIS and integration of spatial soil data into environmental models. Recent innovations create opportunities to increase both the resolution and the efficiency at which Soil Survey maps are made. However, new technologies do not replace the need for field observation and validation. Unfortunately, the momentum for improving Soil Survey maps appears to be waning, as budget and personnel cuts continually hit the Natural Resource Conservation Service. With the increasing need to manage our limited resources wisely, now is not the time to slow down our pursuit to better understand and represent our environment in maps.

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oil Survey maps are the preeminent data set collected about our environment. Although there are other impressive data sets that are regularly used for studying and utilizing the environment, none match the wide utility and potential of soil maps. The USGS stream flow gauging stations, NOAA meteorological data network, and USGS topographic maps are excellent examples of environmental data that add value to public funding through their utility to industry and public well-being. Both stream gauges and weather stations continuously gather data, which make them important environmental monitoring systems. Topographic and soil maps, by contrast, are natural resource inventories. As they are “inventories,” it is tempting to consider the finished product the end product. However, that has not been the case with topographic data; investment in elevation data is advancing the creation of higher resolution elevation maps, e.g., LiDAR data. Soil maps also have new opportunities emerging from recent innovations. Unfortunately, the momentum for improving Soil Survey maps appears to be waning, with the impact of budget and personnel cuts at the Natural Resource Conservation Service.

Bradley A. Miller, Dep. of Geography, Michigan State Univ., East Lansing, MI 48824 ([email protected]). doi:10.2136/sh12-02-0005 Published in Soil Horizons (2012). © Soil Science Society of America 5585 Guilford Rd., Madison, WI 53711 USA. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.

After millions of work hours, the staff of the Soil Conservation Service and then the Natural Resource Conservation Service has mapped practically every hectare of soil that can be reached in the United States. So now what? Is the work of the soil classifier done? A wide variety of people and industries depend on accurate information about the spatial distribution of soil properties. Plus, the quality of Soil Survey maps has steadily increased over the past century, and the soil maps can be made better at lower costs with recent developments in technologies, such as GIS. Soil geography and soil maps have much more to offer society. I argue that there exists no environmental data set with a broader impact than the maps and data created by the U.S. Soil Survey. We should continue to strive for improvement in this invaluable resource.

The Value of Soil Survey Maps The information found in Soil Survey maps is utilized by a wide variety of users. The most critical users are, of course, farmers. Soil maps support our food production industry, which explains why the U.S. Soil Survey is within the USDA. The survey is also of great importance to geologists. The second director of the USGS, Major John Wesley Powell, was a strong supporter for the concept of a soil survey (Tandarich et al., 1988). In fact, Powell first advocated for the creation of a soil survey as part of the USGS (Amundson and Yaalon, 1995). To think of other users of the Soil Survey maps, one only needs to flip through the pages of a Soil Survey publication. Use and management information, as well as physical and chemical property estimates, within those pages are depended on—daily—by engineers, hydrologists, biologists, and anyone else intending to use the soil as a material or study its role in the environment.

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In addition to the above list of Soil Survey map users, there are many others who adapt the Soil Survey maps to meet their need for information about the physical landscape. Many people use the interpretive ratings from the Soil Survey in their calculations of land value and taxation (Chavas and Shumway, 1982; Huddleston, 1984; Davidson, 2002). Anthropologists use Soil Survey maps to help locate sites likely to exhibit evidence of past occupations (Mandel and Bettis, 1995; Artz, 2005). Geomorphologists use soil maps to guide their investigations into landscape processes, which then inform us about past climates and environments (Ruhe et al., 1967, Schaetzl et al., 2000). Perhaps the most obvious users of Soil Survey maps, not explicitly recognized in the Soil Survey documentation, are the plethora of environmental modelers. Environmental models need information about the spatial distribution of both physical and chemical properties of soils to estimate processes on the landscape; see, for example, Groffman et al. (1992), Wilson et al. (1996), Wang and Melesse (2006), Gassman et al. (2007), Tang et al. (2010), and Frankenberger et al. (2011). The need for spatial soil data for environmental models will only increase as issues of environmental quality become more pressing. Soil plays a role in the key processes of virtually every environmental system. For example, soil properties affect hydrologic pathways, fate of potential contaminants, and the transport of the soil itself (e.g., erosion, creep, and landslides). In addition to issues of land and water quality, we are just beginning to study soil as a sink for carbon, which is now becoming an area of strong interest as we contemplate the implications of climate change caused by carbon emissions to the atmosphere (Post et al., 1982; Jenkinson et al., 1991; Lal, 2004). The spatial distribution of soil properties is an important part in understanding and predicting where all of these processes may operate, and more. Therefore, better maps of soil properties are crucial. The wealth of information in a Soil Survey map benefits many audiences, even if they need to convert the original data to suit their needs. By providing base geographic data to all of these endeavors, the work of the Soil Survey is invaluable to Americans on a daily basis. These data strengthen our economy and help us make informed decisions about sustaining a quality environment. The staffers who worked so hard to create the soil maps we have today deserve our thanks and admiration.

And Yet, There’s More to be Done Unfortunately, although the job of the Soil Survey may appear to be largely done, we must continue to use every opportunity for improvement and advancement. Why? Users can always utilize higher resolution and more accurate soil maps. For example, a common practice for people who use soil information in their models is to convert Soil Survey map polygons into a raster grid format (e.g., Merwade, 2010). These raster grids are inputs for many different types of models and are often created with a resolution of square cells, 30 m on a side, or smaller (e.g., Anderson

et al., 2006; Breiby, 2006; Mednick, 2010). In effect, the 30-m resolution grid is a digital map with a resolution of 900 m2 (0.09 ha), inferred from a map with a minimum delineation area of 0.6 ha (6000 m2), assuming optimistically that the source was a secondorder, 1:12,000 scale soil map (Soil Survey Division Staff, 1993). Even if the entirety of the converted raster soil map was based on second-order soil delineations, there is still great variation in the quality of soil maps that need to be merged together to cover an area larger than a county. The different survey areas could have been last mapped anytime during the last 40 yr. Despite the fact that soil mapping quality has vastly improved over the past 40 yr, survey areas mapped at different times are used as if the uncertainty was the same across the merged map. Also, there are often discontinuities in the classification of soils between survey areas. When adjacent soil maps are merged together, the resulting soil map can have attribute boundaries that reflect survey boundaries more than a realistic distribution of soil properties (Miller et al., 2008). The consequences of basing management decisions on environmental models that rely on inferring higher resolution data from soil maps of larger scale and variable quality are unclear. Providing better input for these models will certainly improve our understanding of the environment and our attempts to model it. Currently, researchers have to calibrate their models assuming that the input soil data are correct at the resolution that is being provided to the model. There is no measure of uncertainty for soil data input, which also means that there is no real measure of uncertainty for the results of the model. Questions about how the models would be calibrated differently and how management decisions would be made differently if the spatial soil data were more accurate remain unanswered. The high proportion of environmental models using information from Soil Survey maps in some form highlights the impact of uncertainties in soil maps on environmental management decisions (Goodchild et al., 1996; Heuvelink, 1998; Mednick, 2010). Some might think that the existing soil maps are good enough for most uses, and that to continue to work on them would be a misguided priority in these lean economic times. They may argue that soil properties are often fairly uniform within the ≈0.6 to 6000 ha delineations of a current map unit polygon. However, studies of soil property variability have found the ranges of spatial correlations commonly end at distances between ≈4 and 486 m (Cambardella et al., 1994; Gallardo, 2003; Iqbal et al., 2005). In some areas, such as in dry and flat landscapes, a 0.6-ha delineation may be quite homogeneous. However, in many areas the examination of aerial photographs demonstrates the fi ne-scale variability of soil properties that 0.6-ha delineations simply cannot capture. In Fig. 1, for example, the darker toned areas of the landscape indicate patterns of wetness and organic carbon storage. And yet, aerial photography only scratches at the surface of variability in soil properties

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across the landscape. Soil is a three-dimensional entity, and its spatial variability continues below the surface (Kravchenko and Robertson, 2011).

Game-Changing Innovations The use of GIS with soil maps has altered the setting in which we work with spatial soil data. One of the ways that GIS has changed how we interact with soil maps is that resolution is no longer necessarily associated with scale. The GIS user can freely change the scale of the map on the computer screen, but that does not change the resolution of the data. The minimum delineation on the most common Soil Survey maps (second order) is 0.6 ha because 0.6 ha is the smallest practical area for legibly drawing map delineations on 1:12,000 to 1:31,860 scale aerial photos (Soil Survey Division Staff, 1993). Today, however, many people view Soil Survey map delineations through GIS, rather than on a paper map in a Soil Survey publication. Instead of delineations being limited by adequate physical space to draw a line manually and include a label, GIS maps are limited by availability of information and data storage capacity. This different model for representing spatial information demands that we provide better data for those who depend on, and use, soil maps—especially those who use them digitally. In addition to changing how we view maps, GIS in combination with other modern technologies is creating new opportunities for how we make soil maps. Today’s mappers can use GPS technologies, in combination with spatial data layers on tablet computers, to incorporate observations as they are in the field. Use of new data, like satellite imagery and LiDAR, plus new methods of computationally analyzing preexisting spatial data make it possible for Soil Survey staffers to fix problematic map delineations without leaving the office. When field reconnaissance is needed, these technologies can inform soil mappers where the problems are most likely to be, increasing the efficiency of the required data collection. For example, a simple comparison of Soil Survey map delineations with elevation data available from the USGS reveals the potential for many line placement (i.e., map unit boundary) improvements (Fig. 2). The utility of the higher resolution elevation data, now freely available, can further assist in the improvement of Soil Survey maps through digital terrain analysis procedures. Digital terrain analyses that leverage digital elevation models for enhancing soil maps include calculations of slope, aspect, curvature, and topographic convergence (Moore et al., 1993; Gessler et al., 2000; McBratney et al., 2003). These innovations are already being applied in creative ways by

Fig. 1. Screenshot of an area in Palo Alto County, Iowa using the SoilWeb interface (California Soil Resource Lab, 2012) for SSURGO map unit delineations (Soil Survey Staff, 2012). Note the distribution of soil properties evident in the variation of land surface tone in the aerial image, as compared with the Soil Survey map lines (yellow). Google Earth (accessed 24 Jan. 2012).

Fig. 2. Example from Ingham County, Michigan of how elevation information available today could help improve both the placement of delineations and the level of detail included in Soil Survey maps. Shown are the current Soil Survey delineations (Soil Survey Staff, 2012) overlain onto a USGS 10-m resolution elevation raster grid, available for all of the conterminous United States (USGS, 2012). In this image, the elevation data are enhanced by a hillshade model.

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soil scientists. The potential exists to do more with these technologies for the benefit of increasing the precision and accuracy of the Soil Survey maps. Soil scientists are also working to meet the increased demand for soil data through advancements in predictive soil mapping. Pedotransfer functions use correlations among basic soil properties to estimate soil properties that are difficult or expensive to measure (e.g., Borggaard et al., 2004; Zinn et al., 2005; Pachepsky et al., 2006). Geostatistics provide quantitative methods for predicting soil properties between points of observation (Trangmar et al., 1986; Goovaerts, 1999). Other computational approaches, such as artificial neural networks (Zhu, 2000), predict soil properties with more complex correlations among multiple environmental variables. For a more complete review of digital soil modeling techniques, see McBratney et al. (2003). These new approaches for assembling information about the soil landscape greatly improve the efficiency and resolution of soil mapping. However, the invaluable step of field observation and validation is still needed.

Conclusions Undoubtedly, seeking to improve our soil maps—something that we can all agree would be advantageous to society—will have costs. But with the increasing need to manage our limited resources wisely, now is not the time to slow down our pursuit to better understand and represent our environment in maps. The United States has much to gain when more accurate and precise soils data are available to farmers, agronomists, engineers, biologists, city planners, hydrologists, and environmental scientists. Providing these users with improved soil maps can be done more cost efficiently than in the past with the spatial technologies available today. Until more detailed maps with better placed lines are drawn, however, users of Soil Survey maps will be forced to continue to make do with the soil data available to them. Imagine all of the time and money that would be saved by having a more dependable soil map. Creating these improved soil maps should not be postponed; it should be a priority.

Acknowledgments My thanks to the anonymous reviewer and editors for their suggestions. Also, my special appreciation to Dr. Randall Schaetzl and Michael Luehmann of the Michigan State University, Department of Geography for their encouragement and critiques on previous drafts.

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