SSSAJ: Volume 75: Number 5 • September–October 2011

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Soil Sci. Soc. Am. J. 75:2011 Posted online doi:10.2136/sssaj2010.0260 Received 2 July 2010. *Corresponding author (bkgelder@iastate.edu). © 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. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.

Estimating Soil Organic Carbon in Central Iowa Using Aerial Imagery and Soil Surveys

Soil & Water Management & Conservation

Site-specific management, also known as precision agriculture or prescription farm-ing, has been an agricultural goal for a very long time, but it gained new empha-

sis with the development of global positioning systems (GPS) in the 1980s. Global positioning system technologies allow a farmer to accurately navigate a field, tailor-ing soil and crop management to optimize conditions at every location in the field (National Research Council, 1997). Adoption of variable-rate fertilizer and herbicide application has been slow, however, for several reasons, including the availability of suitable application technologies and equipment (Akridge and Whipker, 2000) and the high cost of site-specific information (Bullock and Bullock, 2000; Bullock and Lowenberg-DeBoer, 2007; Bullock et al., 2009). Applicator technologies and equip-ment availability are no longer limiting because most custom applicators and many farmers have site-specific application capabilities (Whipker and Akridge, 2008), but the high cost of site-specific information remains an obstacle to the implementation of precision agriculture. Fortunately, the combination of georeferenced soil data and digital aerial imagery in a geographical information system (GIS) has the potential to greatly reduce the costs of information acquisition and interpretation.

Soil organic C content is a common indicator of soil quality (Karlen et al., 1997) and is positively correlated with high plant yield (Kravchenko and Bullock, 2000; Kaspar et al., 2004). Increasing quantities of SOC can increase soil water holding capacity and cation exchange capacity while decreasing soil bulk density (Liu et al., 2006). Soil organic C also stores and supplies nutrients and herbicides and is thus an important factor in developing appropriate site-specific nutrient (Blackmer and White, 1998) and herbicide application recommendations (Locke

B. K. Gelder*Dep. of Agricultural and Biosystems EngineeringIowa State University3165 NSRICAmes, IA 50011

R. P. AnexDep. of Biological Systems EngineeringUniversity of Wisconsin460 Henry MallMadison, WI 53706

T. C. KasparT. J. SauerD. L. Karlen

USDA-ARSNational Lab. for Agriculture and the Environment2110 University Blvd.Ames, IA 50011

Widespread implementation of precision agriculture practices requires low-cost, high-quality data such as soil organic C (SOC) content, but SOC mapping currently requires expensive sample collection and analysis tech-niques. Soils higher in organic C appear darker than surrounding soils in aerial imagery after tillage, although this difference is only relative without knowledge of the range of SOC. This range could be estimated from Soil Survey Geographic (SSURGO) database. To verify this, the SSURGO database was used to estimate the SOC range at three sites in central Iowa. Soil organic C content across each field was then linearly interpolated within the SSURGO-estimated range for each field using the brightness values at each pixel in the aerial photograph as a scaling factor. Measured SOC data from the three sites ranged from 3.4 to 50 g kg−1 and the R2 and RMSE values between the measured and estimated SOC concentrations ranged from 0.60 to 0.82 and 3.5 to 7.6 g kg−1, respectively. Limited thresholding of the brightest and darkest pixels improved the accuracy and precision of SOC estimates over raw imagery. These results imply that aerial imagery supplemented by SSURGO-estimated SOC ranges can provide georeferenced SOC estimates suitable for site-specific recommendations and analysis without field sampling.

Abbreviations: GIS, geographic information system; GPS, global positioning system; SOC, soil organic carbon; SSURGO, soil survey geographic.

and Bryson, 1997). As a result, some states, such as Nebraska and Missouri, now include soil organic matter as a factor in develop-ing N fertilizer recommendations. Soil organic C, however, var-ies spatially within a field and sampling its distribution is time intensive and costly.

Darker soils generally contain more organic matter (Brown and O’Neal, 1923); this knowledge is used by surveyors to de-lineate soil map units, and the development of quantitative re-lationships between bare soil brightness and SOC continues. Chen et al. (2000) documented a significant (90%) reduction in the number of samples required to map discrete organic mat-ter classes within agricultural fields using high-resolution color aerial imagery and a logarithmic linear regression equation. Later, Chen et al. (2005) used aerial photography and soil sampling to produce continuous estimates of organic C concentrations at the field scale and Chen et al. (2008) advanced this technique by us-ing image similarity analysis to predict SOC in surrounding fields without additional sample collection. Alternative techniques for modeling SOC involving topographic attributes have been dem-onstrated by Mueller and Pierce (2003), who found that soil sam-pling, along with relative elevation from a detailed topographic survey, could estimate soil C in Michigan. Both of these advanced SOC mapping techniques, however, still require high-cost SOC field sampling and analysis to calibrate for specific locations.

Soil color can also vary due to changes in properties such as soil texture (Henderson et al., 1989) and surface roughness and moisture content (Matthias et al., 2000). The relationships between soil color, texture, and organic matter are usually well correlated. Konen et al. (2003) found significant correlations between SOC and soil texture as well as between SOC and Munsell soil color on Des Moines lobe soils. Fernandez et al. (1988) found that the color–organic matter relationship was stronger within a toposequence than across larger geographic ar-eas. Thus, even though color changes may be due to both soil tex-ture and SOC changes, color changes should be well correlated with SOC changes, especially across small areas without dramat-ic changes in texture. Changes in surface roughness, moisture, and residue cover are more problematic for estimating SOC. To effectively mitigate these effects, imagery must be screened for uniform surface roughness, moisture, and residue conditions.

One source of soil property information that is available without sampling is the Soil Survey Geographic (SSURGO) da-tabase (soildatamart.nrcs.usda.gov/, verified 20 Apr. 2011). The SSURGO database is a nationally standardized digital database of soil map units delineated at scales ranging from 1:12,000 to 1:63,360 and represents the most detailed level of soil mapping (Level 2) done by the NRCS. A soil map unit is a geographically contiguous area that has a unique combination of soil properties, contains one to three named soils (i.e., components), and is typi-cally divided into slope and erosion classes or phases. The database incorporates high, low, and representative values for many soil properties, including SOC. Map units are delineated by trained soil morphologists using aerial photographs and topographic in-terpretation in conjunction with field validation in each county.

Level 2 surveys, however, although detailed, still contain areas of unmapped soils series, or inclusions, which are generally <1 ha (2.5 acres) when mapping is conducted at a scale of 1:15,840. Also, the delineation and discretization necessary to create SSURGO cre-ates problems when estimating soil properties because one value must be selected to represent the properties across the entire map unit even though it is known that these properties vary on a contin-uum both within and across map unit boundaries. Inclusions and other problems make SSURGO data unsuitable for application to precision farming practices such as N management (Franzen et al., 2002). Despite these challenges, the general accuracy and util-ity of SSURGO expert data has been demonstrated by mapping quaternary parent material and landforms on the Des Moines lobe of Iowa and Minnesota at a higher resolution than conventional geologic maps (Miller et al., 2008).

The spatial limitations of SSURGO for precision manage-ment could be remedied with high-resolution aerial imagery that creates a spatial database of soil brightness. This imagery is rapidly becoming available from sources such as USGS orthophotos and NRCS soil survey photography (datagateway.nrcs.usda.gov/; veri-fied 20 Apr. 2011). Additionally, although coarse, the SSURGO database could be used to estimate the map units and the associ-ated range of SOC concentrations that are probably present in a field. Thus, by combining soil brightness and SOC range data in a GIS, it may be possible to estimate SOC concentrations by us-ing the brightness values at each pixel in a bare-soil aerial image as a scaling factor within a field’s range of brightness to linearly interpolate within the SSURGO-estimated range of SOC. The objective of this study was to apply this method to two agricultural fields and compare measured SOC values with those predicted by different methods of estimating the SOC range from SSURGO data and the brightness range from aerial imagery.

MATeRiALS AnD MeThoDS:Site Description

The study was conducted on two agricultural fields: Field 1 was a 32-ha field in Boone County, Iowa, at 42°4´49˝ N and 93°45´51˝ W, which contained Site 1 (Fig. 1); Field 2 was a 30-ha field in Story County, Iowa, at 41°58´26˝ N and 93°41´32˝ W; this field was subdivided into Site 2 (Fig. 2) and Site 3 (Fig. 3) due to different past management prac-tices on opposite sides of an old property line. Both fields are located in central Iowa and are typified by the soils of the Clarion–Nicollet–Webster soil association (Table 1). These soils are in low-relief prairie pothole topography typical of the Des Moines lobe of the Cary substage of the Wisconsin glaciation. Field 1 was mapped at a higher resolution by Steinwand and Fenton (1995) and Steinwand et al. (1996), revealing high-organic-matter depressional soils such as Okoboji and Canisteo and low-organic-matter summit soils such as Storden and Zenor (Table 1). The SSURGO soils information for both sites was obtained from soil-datamart.nrcs.usda.gov/ (verified 20 Apr. 2011). Both Fields 1 and 2 have been in long-term (>15-yr) corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotations, with primary tillage consisting of chisel plow-ing or disking. Additional information on tillage and fertility practices for Field 1 is available in Kaspar et al. (2004).

Aerial Data

Resampled eight-bit (0–255), gray-scale, bare-soil aerial imagery of the sampled portions of Fields 1 and 2 plus a 25-m buffer (approximately half the sampling distance) was used as the background in Fig. 1, 2, and 3. The aerial photographs were originally acquired by the USGS as digi-tal orthophoto quarter-quadrangles (DOQQs) on 16 Apr. 1994, which were georectified to the universal transverse Mercator (UTM) Zone 15N coordinate system using the North American datum of 1983 (NAD83). The DOQQs were then processed by the USDA Aerial Photography Field Office into 1- by 1-m resolution mosaics with MrSID compression and default values for the compression ratio (20:1) and compression level (10). During the mosaic process, the image tone of all input DOQQs was adjusted toward a common value. The aerial image mosaic for Boone County, Iowa, which includes Field 2 in Story County, was resampled to 5-m resolution to reduce the effects of inaccuracies in image compres-sion, georeferencing, and locating soil sampling cores. The aerial imagery was visually inspected to confirm low levels of residue cover (<30%) at the sites of interest as well as homogeneous surface roughness, residue distribution, and soil surface moisture conditions.

Measured Soil organic Carbon DataSoil organic C data were collected for validation during two different

field campaigns. Fifty-six soil cores on a 48.4- by 48.4-m grid were collected in April 1999 from Field 1 using a truck-mounted hydraulic soil sampler. Cores were 1.20 m long and 0.032 m in diameter. The upper 0.150 m of each soil core was removed, air-dried, mixed, ground using a roller mill,

Fig. 1. Resampled 1994 USGS aerial imagery, measured soil organic C (SoC) values, and SSURGo map unit boundaries for Site 1. The aerial imagery also represents the estimated SoC values for Site 1.

Fig. 2. Resampled 1994 USGS aerial imagery, measured soil organic C (SoC) values, and SSURGo map unit boundaries for Site 2. The aerial imagery also represents the estimated SoC values for Site 2.

Fig. 3. Resampled 1994 USGS aerial imagery, measured soil organic C (SoC) values, and SSURGo map unit boundaries for Site 3. The aerial imagery also represents the estimated SoC values for Site 3.

and 5-mg subsamples were analyzed for organic C and other properties detailed in Kaspar et al. (2004). Organic C was measured using the dry combustion method (NA 1500 NCS elemental analyzer, ThermoElectron SpA, Milan, Italy) after treatment with 1 mol L−1 H2SO4 to remove car-bonates. Two measurements with abnormally high values were removed from this data set because of suspected contamination with surface resi-dues. Soil cores from Field 2 were collected in October 2005 using a truck-mounted hydraulic soil sampler. Cores were 1.20 m long and 0.0382 cm in diameter, and they were collected in pairs at every grid point of a 50- by 50-m grid across an area covering 300 by 550 m (168 cores, 84 sites, Fig. 1b). The upper 150 mm of each soil core was removed, air dried, mixed, ground using a roller mill, and subsampled for organic C and N. Total C and N were quantified using dry combustion methods (NA 1500 NCS elemental analyzer, ThermoElectron SpA). Soil organic C was calculated as the difference between soil total C and soil inorganic C as measured using pressure calcimetry (Sherrod et al., 2002). The average soil organic C for each site was calculated as the mean of both cores. Three sites were removed from this data set because the difference between the paired mea-surements exceeded 25% of the average measurement.

The locations of all soil cores were collected using a survey-grade GPS receiver and converted into the UTM Zone 15N, NAD83 coor-dinate system to minimize projection errors. Soil organic C measure-

ments from both fields were added as attributes to each corresponding point and converted into raster format using SOC content as the value field. This raster was combined with the aerial imagery to generate each combination of SOC content and image digital number (i.e., bright-ness). These combinations were then graphed to confirm the assumed linear relationship between SOC content and soil brightness for each field (Fig. 4); these relationships were not used in the estimation meth-odology. The assumed linear relationship between brightness and SOC contrasts with the work of Chen et al. (2000, 2005, 2008); however, the absolute value and range of measured SOC content is much greater than in those studies. We hypothesize that this difference occurred due to both texture and SOC influencing the reflectance at the lower SOC contents found in Georgia, whereas SOC dominated the textural influ-ences on reflectance in the soils of the Des Moines lobe.

Soil organic Carbon estimationEstimating and evaluating the accuracy and precision of SOC es-

timates required measured SOC data, SOC estimates from SSURGO, SOC estimation algorithms, and a method of statistical analysis.

SSURGo Soil organic Carbon estimation and Supplementation with Aerial imagery

The SSURGO databases for Boone and Story counties in Iowa were downloaded on 1 June 1 2008 from soildatamart.nrcs.usda.gov and the soil map units are overlaid on the aerial images in Fig. 1, 2, and 3. A soil map unit usually contains only one named soil; however, in cases where a map unit contains multiple named soils and associated sets of proper-ties, the simplifying assumption was made that each soil map unit would be represented by the properties of the surface horizon of the majority soil map unit (component). Table 1 lists the properties of the soils repre-sented by map units in the two fields. The soil organic matter (SOM) per-centage from the soil survey was converted to SOC percentage using the standard soil survey approximation (Soil Survey Division Staff, 1993):

SOMSOC

1.724= [1]

Boundaries for the three sites were delineated using a 25-m buffer around the measured points. The site boundaries were then intersected with the soil map unit layer to determine the maximum, minimum, and range of SSURGO SOC values in the field. The lower bound for SOC

Table 1. Soil map units and associated soil organic C (SoC) values from the SSURGo database for soils in or around Sites 1, 2, and 3.

Map unit Soil series Slope Taxonomic class

SSURGo SoC

Low Rep.† high

%6 Okoboji 0–2 fine, smectitic, mesic Cumulic Vertic Endoaquolls 5.2 6.1 7.0

55 Nicollet 0–2 fine-loamy, mixed, superactive, mesic Aquic Hapludolls 2.9 3.2 3.5

62C2 Storden 5–9 fine-loamy, mixed, superactive, mesic Typic Eutrudepts 1.0 1.3 1.6

95 Harps 0–2 fine-loamy, mixed, superactive, mesic Typic Calciaquolls 2.6 2.9 3.2

107 Webster 0–2 fine-loamy, mixed, superactive, mesic Typic Endoaquolls 3.5 3.8 4.1

138B Clarion 2–5 fine-loamy, mixed, superactive, mesic Typic Hapludolls 1.7 2.0 2.3

138C2 Clarion 5–9 fine-loamy, mixed, superactive, mesic Typic Hapludolls 1.3 1.6 1.9

507 Canisteo 0–2 fine-loamy, mixed, superactive, calcareous, mesic Typic Endoaquolls 2.9 3.5 4.1828C2 Zenor 5–9 coarse-loamy, mixed, superactive, mesic Typic Hapudolls 0.6 0.9 1.2

† Representative value for the map unit.

Fig. 4. Measured soil organic C (SoC) vs. image brightness at all sample locations for all sites. These relationships are shown only to confirm the linear correlation between SoC and image brightness and were not used in the interpolation.

at each site was estimated to be the low value of the map unit with the least SOC. Similarly, the upper bound for SOC estimates was set at the high value of the map unit with the most SOC. Site boundaries were also used to define zones to extract the maximum and minimum bright-ness values from the USGS orthophoto with a zonal statistics function. Soil organic C concentration for the georeferenced location of each pix-el in the aerial photo was then estimated by linearly interpolating within the SSURGO-estimated range of SOC for each field using the bright-ness values at each pixel in the aerial photograph as a scaling factor:

SOC FieldLowSOC FieldRangeSOC

PixelBV MinBV1

RangeBV

= + ×

−−

[2]

where SOC is the estimated SOC concentration, FieldLowSOC is the low value of SOC for the map unit with the lowest SOC in the field, FieldRangeSOC is the range of low values for SOC in the field of interest, PixelBV is the brightness value (digital number [DN]) in the pixel of in-terest, MinBV is the minimum brightness value (DN) in the field of inter-est, and RangeBV is the range of brightness values in the field of interest.

To examine the impact of nonrepresentative, high and low reflecting pixels that are likely due to variations in residue cover, soil water content, and surface roughness, the contrast of the images were stretched by using a threshold function that assigned the brightest and darkest 0.13, 0.62, 2.28, and 6.68% of pixels the value of the next brightest or darkest pixel, respectively. This correction was then applied to develop the following:

SOC FieldLowSOC FieldRangeSOC

ThreshPixelBV ThreshMinBV1

ThreshRangeBV

= + ×

−−

[3]

where ThreshPixelBV is the threshold brightness value (DN) in the pix-el of interest, ThreshMinBV is the threshold minimum brightness value (DN) in the field of interest, and ThreshRangeBV is the threshold range of brightness values in the field of interest.

Statistical Analysis

For both fields, the raster of measured SOC values was combined with the raster estimates of SOC using Eq. [2] and [3] to output each combination of SOC measurement and estimation. Measured values of SOC for the sampling points in each field were regressed against SOC estimates derived by each method. The resulting linear regression model results were then analyzed for their ability to estimate SOC using a sub-set of the test statistics demonstrated by Meek et al. (2009). The overall suitability of the different models was tested using the Kolmorgov–Smirnov statistic, D, which measures the similarity of measured and estimated distributions. The normality of the residuals was tested with the Shapiro–Wilk test, W, and bias was tested with the mean bias er-ror (MBE). Precision was tested using the coefficient of determination, R2, Lin’s concordance correlation coefficient, rc, and Lin’s scale shift, v. Accuracy was tested using Lin’s correlation factor, Cb, Lin’s location shift, u, and the RMSE. The resulting linear regression model should approximate the 1:1 line with a slope of 1 and intercept of 0 if there is perfect agreement between the estimated and measured values.

ReSULTS AnD DiSCUSSionThe comparison of Eq. [2] and [3] estimates of SOC to

measured values is shown in Table 2. The analysis shows that for almost every measure (except MBE at Site 1), model accuracy in-creased with image thresholding, indicating that a small percent-age of pixels needed to be thresholded to return the best estimates

Table 2. Model performance statistics for eq. [2] and [3] estimates of soil organic C (SoC) vs. measured values for Sites 1, 2, and 3. equation [2] utilizes the brightness range from the raw imagery to interpolate between the SSURGo-estimated range of SoC, whereas eq. [3] utilizes increasing amounts of thresholding to remove outliers. Values in bold indicate the best performance at that site.

Site Model n D† P > D W‡ P < W R2 RMSe Cb§ rc¶ u# v†† MBe‡‡

Site 1 Eq. [2] 54 0.444 <0.0001 0.988 0.848 0.75 7.54 0.66 0.57 −0.41 2.45 −2.73Eq. [3], 0.13% 54 0.444 <0.0001 0.988 0.848 0.75 7.59 0.73 0.63 −0.57 1.88 −4.34

Eq. [3], 0.62% 54 0.370 0.001 0.988 0.848 0.75 6.99 0.80 0.70 −0.47 1.66 −3.81

Eq. [3], 2.28% 54 0.278 0.031 0.988 0.847 0.74 6.20 0.89 0.77 −0.33 1.42 −2.86

Eq. [3], 6.68% 54 0.241 0.087 0.993 0.983 0.71 6.69 0.92 0.78 −0.39 1.17 −3.75

Site 2 Eq. [2] 42 0.333 0.018 0.978 0.577 0.60 4.04 0.81 0.63 0.41 1.70 1.75

Eq. [3], 0.13% 42 0.191 0.431 0.978 0.578 0.60 3.51 0.95 0.73 0.06 1.39 0.28

Eq. [3], 0.62% 42 0.191 0.431 0.978 0.576 0.60 3.53 0.98 0.76 0.08 1.19 0.39

Eq. [3], 2.28% 42 0.191 0.431 0.976 0.525 0.59 3.85 0.97 0.75 0.08 0.80 0.74

Eq. [3], 6.68% 42 0.143 0.784 0.970 0.328 0.59 4.43 0.97 0.75 0.08 0.80 0.52

Site 3 Eq. [2] 42 0.548 <0.0001 0.944 0.038 0.81 8.23 0.49 0.44 1.09 2.48 5.94

Eq. [3], 0.13% 42 0.333 0.018 0.945 0.041 0.82 4.76 0.87 0.79 0.27 1.58 1.87

Eq. [3], 0.62% 42 0.262 0.112 0.949 0.061 0.82 4.50 0.91 0.82 0.23 1.48 1.65

Eq. [3], 2.28% 42 0.214 0.289 0.953 0.081 0.81 4.22 0.94 0.85 0.21 1.32 1.58Eq. [3], 6.68% 42 0.214 0.289 0.950 0.064 0.81 4.23 0.96 0.87 0.24 1.16 1.91

† Kolmorgov–Smirnov statistic, which tests the null hypothesis that measured and estimated values are similarly distributed.‡ Shapiro–Wilks test, which tests the null hypothesis that residuals of estimates are normally distributed.§ Lin’s bias correction factor, a measure of accuracy; optimal is 1.¶ Lin’s concordance correlation coefficient, a combined measure of accuracy and precision; perfect correlation is 1.# Lin’s location shift, a measure of closeness to an intercept of 0; optimal is 0.†† Lin’s scale shift, a measure of closeness to the 1:1 line; optimal is 1.‡‡ mean bias error, a measure of the overall bias of the estimates; optimal is 0.

of SOC. The potential reasons for this include inaccurate estima-tion of the SOC minimum and range from SSURGO, hetero-geneous residue cover or surface moisture conditions, or artifacts from the digitizing process. All models performed better at Site 2 (Fig. 6) than Sites 1 (Fig. 5) or 3 (Fig. 7), demonstrating little bias, scale shift, or slope shift, although this site also returned the poor-est correlation between brightness and SOC. The superior per-

formance of the model on this field was probably due to excellent estimation of the minimum and range of SOC. The SSURGO-estimated minimum SOC was 17.4 g kg−1 and the maximum was 40.6 g kg−1; this is very similar to the measured minimum of 17.4 g kg−1 and maximum of 39.3 g kg−1.

Examination of the MBE and the measured–estimated pairs indicated that SOC was overestimated at Site 1 and underesti-mated at Site 3. These biases were due to errors in estimating the minimum and range of SOC contents in the fields, shifting the interpolation. At Site 1, the measured minimum and maximum SOC values were 3.4 and 50.4 g kg−1, respectively; however, the estimated minimum and maximum were 22 and 49.9 g kg−1, respectively, resulting in overestimation at the lower end. At Site 3, the measured minimum and maximum were 17.2 and 48.9 g kg−1, respectively, and the estimated minimum and maxi-mum were 17.4 and 40.6 g kg−1, resulting in underestimation at the upper end. These nonsymmetric errors in estimation caused non-normal residuals, indicated by the results of the Shapiro–Wilks test. It should also be noted that Sites 1 and 3 required additional thresholding to achieve a minimum error of the test statistics; this was probably needed due to the overestimation of the lower SOC bound at Site 1 and the underestimation of the upper SOC bound at Site 3.

To determine if a Level 1 soil survey could improve the re-sults, the 1:3305 soil survey of Field 1 conducted by Steinwand et al. (1996) was consulted, revealing a number of soils not mapped in the corresponding areas of the Level 2 soil maps. The survey mapped Storden and Zenor loam soils on the eroded hilltop areas in the center north of Field 1. Inclusion of Zenor’s 5.8 to 11.6 g kg−1 SOC range would have lowered the minimum esti-

Fig. 7. Measured soil organic C (SoC) estimates for Site 3 vs. values estimated using eq. [3] thresholding the brightest and darkest 0.62% of pixels. notice underestimation of SoC at the high end due to erroneously low SSURGo estimates of SoC.

Fig. 6. Measured soil organic C estimates for Site 2 vs. values estimated using eq. [3] thresholding the brightest and darkest 0.62% of pixels.

Fig. 5. Measured soil organic C (SoC) estimates for Site 1 vs. values estimated using eq. [3] thresholding the brightest and darkest 0.62% of pixels. notice overestimation of SoC at the low end due to erroneously high SSURGo estimates of SoC.

mate to 5.8 g kg−1 and increased the models’ accuracy. The mini-mum estimated SOC of 5.8 g kg−1, however, is still not as low as the lowest measured value observed (3.4 g kg−1). This could be due to an error in the measured data because samples taken 12 m on either side of the suspect sample, as well as another sample of the same location from a later survey, all returned SOC con-tents at least twice as great as that measured; it could also indi-cate the presence of small inclusions not mapped in the survey of Steinwand et al. (1996), which is likely (Fenton, personal commu-nication, 2009). Conversely, Steinwand et al. (1996) also mapped Okoboji and Delft clay loam soils in and around the localized de-pression just west of the center of the field. Inclusion of Okoboji’s 46.4 to 69.6 g kg−1 SOC range in the models would have raised SOC estimates, negating the improvement from including Zenor’s lower SOC range. The overestimation that would result from including Okoboji illustrates a problem caused by using the lowest and highest values from SSURGO to estimate the SOC range: the range given in SSURGO often does not describe the range of properties found in an actual map unit, especially those with a large range and small enough to be overlooked as inclu-sions in a Level 2 survey. The amount of SOC accumulation is de-pendent on the area contributing to the depression, and thus the low to representative values for Okoboji SOC would be a more appropriate estimate for the upper bound of SOC concentration at Site 1 than the high values for a map unit of this size.

Although a Level 1 soil survey was not available for Field 2, highly accurate LiDAR topographic surveys were available (data not shown). These maps revealed small depressional and depositional areas in the upper center and lower right corner of Site 3; this cor-relates well with the observed areas of higher than predicted SOC. No depressional soils were mapped in SSURGO, and mapping such depressional and depositional inclusions would increase the up-per value of SOC estimates from Eq. [2] and [3]. Doing so would increase the accuracy of the model predictions for this field; how-ever, the previously mentioned limitations on the use of SSURGO-estimated ranges, especially for inclusions, still apply.

The optimal amount of image thresholding is not clear from this study. The minimum error at Sites 1 and 3 was obtained by thresholding the images at either 2.28 or 6.68%, depending on the test statistic used; however, the minimum error at Site 2 was ob-tained by thresholding at either 0.13 or 0.62%. The greater amount of thresholding needed at Sites 1 and 3 can be explained by the SSURGO underestimation of the actual range of SOC at both sites. This underestimation requires additional trimming of high and low values to reduce the range of brightness values and make it more closely approach the reduced SSURGO range at Sites 1 and 3. The SSURGO data at Site 2 correctly estimated the actual range of SOC values and probably provided a better evaluation of the appropriate level of thresholding in unbiased situations. Thus the optimal amount of thresholding probably depends on the ac-curacy of the SSURGO estimates of the SOC range. It appears that thresholding at either 0.62 or 2.28% will return minimal errors un-der most conditions; however, additional evaluation of this meth-odology at other sites is recommended.

The R2 values from each of these models are nearly equal to those of the linear regression of soil brightness vs. measured SOC (Fig. 4), indicating that precision was retained. The coefficients of determination are also similar to those of Chen et al. (2008), although RMSE values are about two to four times higher. The study of Chen et al. (2008), however, differs significantly from this study in that their model predictions were developed from actual measured data whereas this study used only expert inter-pretation from the SSURGO database as a basis for estimating the range of SOC estimates for each field. Additionally, the mea-sured SOC concentrations of Chen et al. (2008) ranged from 2 to 25 g kg−1, whereas measured concentrations in this study ranged from 3.4 to 50 g kg−1, leading to a larger range of estimates and a higher potential RMSE even at the same error percentage. Considering this, the estimates provided via Eq. [3] can facilitate the development of SOC maps that are of greater continuity and higher resolution than those from SSURGO data alone.

ConCLUSionSThis study demonstrates that readily available aerial im-

agery and SSURGO SOC data can be used to generate con-tinuous, normally distributed, high-resolution, and highly precise and accurate estimates of SOC values within areas of similar residue cover, tillage practices, and soil moisture. The errors observed in the model predictions were not sys-tematic and were related to known shortcomings with the use of SSURGO data for high-spatial-resolution applications. Analysis of the mean bias errors and measured–estimated plots illustrates the importance of accurately determining the minimum value and range of SOC concentrations within a field to estimate the SOC without bias. Thresholding im-ages at 0.62 or 2.28% is probably the best option to remove outliers from most imagery. Higher amounts of thresholding returned better results at two sites; however, the additional thresholding was needed to correct SOC estimation errors from the SSURGO data. Consequently, the data from aerial imagery and SSURGO can provide estimates of SOC suitable for site-specific management and analysis at similar locations at very low cost. The accuracy of these estimates will depend on obtaining quality aerial imagery with homogenous residue cover distribution after tillage and uniform surface moisture as well as the accuracy of SSURGO map unit delineations and soil property ranges. Imagery conforming to these condi-tions will yield SOC maps that are more useful for assessing fertility requirements, pesticide transport risk, soil moisture status, and crop yield trends by providing greater discern-ment of SOC spatial differences than estimates provided by SSURGO data alone.

ACKnowLeDGMenTSWe acknowledge the assistance of the USDA-ARS National Laboratory for Agriculture and the Environment in completing this project. Without their contributions of data, advice, and funding, this research would not have been possible.

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