Using Vis-NIR Spectroscopy for Monitoring Temporal Changes in Soil

TECHNICAL ARTICLE

Using Vis-NIR Spectroscopy for Monitoring Temporal Changes in Soil Organic Carbon Fan Deng1 Budiman Minasny2 Maria Knadel1 Alex McBratney2 Goswin Heckrath1 and Mogens H. Greve1 Abstract: Monitoring the spatial and temporal changes in soil organic carbon (SOC) brought about by climate change and agricultural practices is challenging because existing SOC monitoring methods are very time and resource consuming. This study examined the use of visible near-infrared spectroscopy (Vis-NIR) as a speedy method to predict SOC and to monitor spatial and temporal changes in SOC compared with labor-intensive traditional laboratory (TL) measurements. For SOC prediction, topsoil (0–25 cm) and subsoil (25–50 cm) samples in the Danish soil spectral library for the years 1986 and 2009 were used. Empirical Bayesian Kriging was used to map SOC. The Vis-NIR predictions indicated that average topsoil and subsoil SOC had decreased slightly in Denmark from 1986 to 2009, and this was confirmed by TL measurements of SOC. In East Denmark, Vis-NIR predictions differed significantly from the measured SOC values. For subsoil samples, the ability of Vis-NIR to predict SOC levels varied. In West Jutland, Central Jutland, North Jutland, and Thy, Vis-NIR–predicted SOC levels did not differ from TL-measured levels, showing good predictive ability. For topsoil samples, the spatial pattern of change in TLmeasured and predicted SOC was consistent during the 23-year study period, but there were significant discrepancies in the corresponding SOC change patterns for subsoil samples. To conclude, Vis-NIR is a promising method for monitoring spatial and temporal changes in SOC at the national scale, especially in the topsoil. Some difficulties can arise in low SOC subsoils, so more systematic work is needed to improve the method for practical applications. Key Words: Soil organic carbon, visible near-infrared spectroscopy, soil monitoring, temporal change, spatial pattern (Soil Sci 2013;178: 00–00)

S

oil is the largest global carbon (C) pool, containing approximately three times as much C as the terrestrial C pool and twice as much as the atmosphere (IPCC, 2000). Therefore, soil organic C (SOC) variations are of critical importance for the global climate, especially for global warming. It is recognized that a slight change in the SOC pool could have a profound impact on the atmospheric CO2 concentration (Smith et al., 2008). Therefore, when seeking to reduce greenhouse gas emissions to mitigate climate change, increasing C storage in the soil is a valuable option. Soil C is also important for the delivery of key ecosystem goods and services, such as food production and global food security, water quality, and soil biodiversity (Lal et al., 2007). Climate change and changes in land use interact in complex ways, causing the SOC content to either increase

1 Department of Agroecology, Faculty of Science and Technology, Aarhus University, Tjele, Denmark. Dr. Mogens H. Greve is corresponding author. 2 Faculty of Agriculture and Environment, The University of Sydney, Sydney, Australia. Address for correspondence: Dr. Mogens H. Greve, Department of Agroecology, Faculty of Science and Technology, Aarhus University, Blichers Allé 20, P.O. Box 50, DK-8830 Tjele, Denmark. E-mail: [email protected] Financial Disclosures/Conflicts of Interest: None reported. Received March 14, 2013. Accepted for publication August 30, 2013. Copyright © 2013 by Lippincott Williams & Wilkins ISSN: 0038-075X DOI: 10.1097/SS.0000000000000002

Soil Science • Volume 178, Number 8, August 2013

or decrease in different land use systems (FAO, 2012). However, monitoring changes in SOC stocks over time efficiently and accurately remains a challenge at the landscape scale (Saby et al., 2008; Minasny et al., 2013). Slow turnover times and small changes relative to total SOC stocks (Conant et al., 2011) further complicate monitoring work. Existing methods for SOC monitoring are based on modeling or on mechanical sampling at plot or field level, with statistical and geostatistical upscaling being used to estimate net ecosystem exchange (Bricklemyer et al., 2007; Conant et al., 2011). Most of these SOC monitoring methods are based on intensive sampling and are thus time and resource consuming (Goidts and van Wesemael, 2007). Therefore, developing a robust, accurate, and inexpensive method to estimate SOC content would greatly facilitate SOC inventory and assessment of spatial and temporal changes in SOC. Croft et al. (2012) suggested three different methods for estimating SOC content using sensors. These are (i) laboratory spectroscopy, (ii) in situ spectroscopy using field equipment, and (iii) remote spectroscopy using airborne or satellitemounted sensors (imaging spectroscopy). In the past 20 years, visible near-infrared spectroscopy (Vis-NIR) has evolved into a fast and nondestructive technique for estimating various soil properties including SOC content (Ben-Dor and Banin, 1995; Stenberg et al., 2010). Visible and near-infrared light can be used for bulk soils with little or no sample preparation, but more representative samples can be obtained if the bulk material is mixed. Visible NIR spectroscopy is based on weak overtones and combinations of fundamental vibrations from the stretching and bending of various atoms, such as the C–H, N–H, and O–H groups found in organic and inorganic compounds (Hunt, 1977). Therefore, Vis-NIR has besoil properties is now well advanced, so Vis-NIR can be routinely used for analysis of soil properties (Shepherd and Walsh, 2002; Schmid et al., 2004; Brown, 2007; Bellon-Maurel et al., 2010; Brodsky et al. 2011; Shepherd and Walsh, 2002; Terhoeven-Urselmans et al., 2010; Viscarra Rossel and McBratney, 2008). Knadel et al. (2012) developed a Danish national soil spectral library based on intensive sampling and analysis of soil types covering the entire country. Partial least squares (PLS) regression was used for model building, and SOC content was square root transformed. The model has successfully been used to determine SOC (Knadel et al., 2012). The application of a spectral library for soil evaluation is believed to involve prediction errors and uncertainties (Stenberg et al., 2010), but no previous study has combined Vis-NIR with a soil spectral library to monitor temporal changes in SOC content. A problem in establishing spectral libraries is the lack of uniform standards for national spectral databases and for ground object features such as SOC measurement (Zhou and Zhou, 2009). Croft et al. (2012) reviewed various studies and concluded that for sensing techniques (include Vis-NIR) to overcome site-specific constraints and to be useful for monitoring spatiotemporal SOC dynamics, better regional spectral libraries are required for more accurate estimation of SOC content. Therefore, the main aim of the present study was to address this knowledge gap. Specific objectives of the study were to (i) www.soilsci.com

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demonstrate the usefulness of the Danish spectral library for SOC prediction of new samples, (ii) compare maps of SOC measured in a traditional laboratory (TL) and predicted by Vis-NIR, and (iii) test the feasibility of using Vis-NIR as a tool for monitoring temporal SOC change. The Danish soil spectral library was used for predicting the SOC content in samples collected from the same locations in 1986 and 2009 in a national grid-sampling scheme. Soil organic C maps were generated to visualize the results so that the spatial pattern of SOC predicted by Vis-NIR spectroscopy could be compared against TL-measured SOC values. These maps were compiled using geostatistical methods. The study focused on analytical and methodological aspects of using Vis-NIR for monitoring temporal changes in SOC in the period 1986 to 2009, whereas the underlying reasons for these changes were beyond the scope of the work.

MATERIALS AND METHODS Study Site and Soil Samples Denmark is a Scandinavian country located in northern Europe. The land elevation ranges between 7 m below and 171 m above mean sea level, so it is a low-relief country (Greve et al., 2012). It has a temperate climate, with a mean temperature of 0°C

in winter and 16°C in summer (Danmarks Meteorologiske Institut, 1998). Mean annual precipitation ranges from 500 mm in the Great Belt region (east) to 800 mm in central Jutland (west). Denmark has the highest percentage of agricultural land in the world, with approximately 66% of the country being cultivated continuously and intensively. The cultivated areas are fertilized and limed. Low-lying areas and loamy soils have been drained artificially (Bou Kheir et al., 2010). Only 10% of the country is covered by forest, mostly spruce plantations. The landscapes of Denmark were formed by large glaciers through the last five to seven glaciations (Petersen, 1996). During the Weischelian glaciation, most of the country was covered by ice, but glaciers only covered half of the Jutland peninsula. Southwest Jutland has rather sandy soils that sedimented out from the large glaciofluvial rivers of melt water running from the glaciers, creating flat fluvial plains (Fig. 1). Landscapes in northern Jutland are dominated by uplifted marine deposits, whereas the rest of Jutland and the Danish isles are generally moraine landscape. Eastern regions of Denmark were once covered by ice advanced from the Baltic area, and the deposits from this advance are loamy and calcareous. As a consequence of this geological history, two distinctive georegions can be identified, namely, East Denmark and West Denmark, which are demarcated by the red line in Fig. 1.

FIG. 1. Distribution of the soil profiles used in this study, taken from a 7-km grid across Denmark. The red line divides eastern Denmark from the western regions (West Jutland, Central Jutland, North Jutland, and Thy).

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Vis-NIR Spectroscopy for Monitoring Temporal Changes in SOC

The Danish national soil spectral library was created for calibration purposes from soil samples collected in a 7-km grid in the period 1986 to 1989 (Knadel et al., 2012). For simplicity, in the following, we refer to these samples as originating from 1986. Samples were taken from 750 soil profiles, with sampling depth extending down to 2 m depending on the pedological horizons present. Locations on peatland were excluded from the present study because the 2009 sampling did not cover these. In total, 2,072 soil samples were selected from the spectral library.

Soil Monitoring in 1986 and 2009 The SOC content in the sample soils varied from 0 to 6%. In the 7-km national grid sampling in 1986, standard depth intervals of 0 to 25 cm and 25 to 50 cm were sampled routinely. Therefore, in the present study, samples from these two layers were taken from 168 soil profiles. In 2009, the same profiles were sampled again at 0 to 25 cm and 25 to 50 cm to compare the SOC change (Fig. 1). The same field sampling method was used on both occasions and involved taking five subsamples each from the topsoil and subsoil of each profile and pooling them to a bulk sample per layer to improve the representativeness. Hence, four data sets were used in analyses of the spatiotemporal change in SOC, topsoil (0–25 cm) and subsoil (25–50 cm) samples for 1986 and for 2009, which are referred to hereafter as 1986_25, 1986_50, 2009_25, and 2009_50.

Spectral Measurements The samples from the Danish soil spectral library and the four data sets of soil samples (1986_25, 1986_50, 2009_25, and 2009_50) were air-dried, sieved to less than 2 mm, and scanned with a LabSpec 5100 spectrometer (ASD Inc., Boulder, CO) with a measurement range of 350 to 2,500 nm. Physical conditions in the laboratory, including temperature and air humidity, were recorded during the scanning process as described by Knadel et al. (2012). A total of 50 internal scans were made, with a white reference sample used after every five samples. Two subsamples were scanned per data set sample, and the results were averaged into one spectrum.

Laboratory SOC Analyses Soil organic C content in the soils used in the present study had previously been determined by dry combustion (Van Moort and De Vries, 1970). Samples from 1986 were analyzed on a LECO CNS-1000 (LECO Corp., St. Joseph, MI) and those from 2009 on a Thermo Flash 2000 Organic Element Analyzer (Thermo Fisher Scientific Inc., Waltham, MA). However, the same analytical protocol was used for both series by the same technician (Rubæk and Sørensen, 2011). Four standard reference soils were analyzed with both instruments to ensure agreement between the

FIG. 2. Model-predicted and TL-measured values from the calibration and validation model of SOC content from the Danish spectral library (units: g/100 g, dotted line is 1:1 line).

two instruments. The change in SOC content between 1986 and 2009 was calculated as : Diff_25=2009_25A−1986_25A.

(1)

Diff_50=2009_50A−1986_50A.

(2)

where A denotes as TL analysis. Because of operational errors in transcribing from paper to various storage discs, there were some outliers in the national data set consisting of 2,072 samples. These outliers were deleted if the SOC difference (Diff-) was threefold higher than the S.D. in any of the data sets. In total, four outliers were deleted from each of the four data sets.

TABLE 1. Model Performance in Vis-NIR Prediction of SOC Content in Topsoil (
25) and Subsoil (
50) in 1986 and 2009 Data Set Calibration Validation 2009_25 2009_50 1986_25 1986_50

RMSE, g/100 g

RPD

R2

0.32 0.40 0.32 0.31 0.31 0.23

2.4 2.1 2.1 1.9 2.2 1.8

0.87 0.80 0.79 0.72 0.81 0.80

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Multivariate Data Analyses A prediction model was built based on the Danish soil spectral library. To reduce the size of the spectral data and computation time, the spectra were averaged every 10 nm and resampled. Wavelengths from 350 to 504 nm and 2,464 to 2,500 nm were removed to eliminate noise at both ends of each spectrum (Kuang and Mouazen, 2013). Spectral reflectance (R) was expressed as absorbance (A): A = log (1/R). Principal Component Analysis was applied for data compression, outlier detection, and pattern www.soilsci.com

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recognition using the Unscrambler X 10.1 software (Camo ASA, Oslo, Norway). The regression rule method was used instead of the commonly used multivariate PLS method because of nonnormality of the data set. The regression rule method is characterized by high accuracy, easy interpretation, variable selection, and incorporation of the upper and lower boundary values of the predictant (Minasny and McBratney, 2008). Compared with PLS, regression rule also gives a smaller RMSE, a smaller relative absolute error, and a larger ratio of performance to deviation (RPD) for prediction of total C (Minasny and McBratney, 2008). Therefore, a commercial regression rule program, Cubist 2.10 (Rulequest Research, Sydney, Australia), was applied to build the regression models for SOC. An extension of Quinlan M5 model tree, Cubist is a rule-based model (Quinlan, 1993). The final Cubist model was composed of a set of rules and their associated multivariate linear submodels. Whenever a situation corresponded to a rule's condition, the associated model was used for prediction. In the national data set, 75% of the samples were used for calibration and 25% randomly selected samples for validation, as the calibration subset must cover a wider range than the validation subset. The parameters used for model assessment were root mean square error (RMSE), correlation coefficient R2, and RPD. Whereas RPD has been criticized by some authors as not being suitable for non-normally distributed data (Bellon-Maurel et al., 2010), it is widely used in soil science. When the calibration model had been built from the Danish soil spectral library, it was used to predict SOC content for the four data sets, resulting in 1986_25M, 1986_50M, 2009_25M, and 2009_50M sets of data, where M denotes model predicted. Unlike

Knadel et al. (2012), SOC content was not transformed in any of the multivariate data analyses.

SOC Mapping Traditional laboratory–measured and model-predicted values of SOC content were used to produce the separate distribution maps for comparison of spatial SOC distribution. Mapping the total land mass of Denmark (43,000 km2) by applying only 164 data points requires a reliable statistical interpolation method. Empirical Bayesian Kriging (EBK) from Arcmap 10.1 (ESRI, CA) was applied for this purpose in the present study. Compared with classical kriging models, EBK has the advantage of being able to interpolate weekly nonstationary data for large areas (Krivoruchko, 2012) and is considered a more accurate method than ordinary kriging for small data sets (Pilz and Spock, 2007). It is a geostatistical interpolation method that automates the variogram estimation with minimal manual interference based on the sample distribution of the estimators of the variogram function. The EBK method differs from ordinary kriging by accounting for the error introduced by estimating the underlying variogram. Conventional geostatistical methods calculate the variogram from known data locations and fit the best model to this empirical variogram. The single variogram model generated from the previous step is then used to make predictions at unknown locations. Thus, the method does not take into account the uncertainty of variogram estimation, which may result in underestimation of the SE of prediction. It is analogous to the Markov Chain Monte Carlo method proposed by Minasny et al. (2011).

FIG. 3. Model-predicted and TL-measured SOC for (A, B) topsoil (0–25 cm; 1986_25, 2009_25) and (C, D) subsoil (25–50 cm; 1986_50, 2009_50) from 1986 and 2009 (in grams per 100 g, dotted line is 1:1 line).

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Vis-NIR Spectroscopy for Monitoring Temporal Changes in SOC

For a given distance h, EBK uses a power variogram model, γ (h), in the following form: γðhÞ ¼ Nugget þ bjhjα :

(3)

where the nugget and b (slope) must be positive and α (power) must be between 0.25 and 1.75 (Pliz et al., 2007). Under these restrictions, the parameters were estimated in the present study using the restricted maximum likelihood technique. This variogram model does not have a range or sill parameter because the function has no upper boundary. The EBK method was applied in the following manner: 1) The original data set was divided into subsets with 100 points, and the overlapping factor was 1, which means that there was no overlapping among subsets. 2) In each of these subsets, a semivariogram model was estimated from each data point using the restricted maximum likelihood. 3) A new value was simulated at each of the input data locations using this semivariogram, so the number of simulations in this process was 100. 4) A new semivariogram was estimated from the simulated data. 5) Steps 3 and 4 were repeated a specified number of times, so by applying a semivariogram, a prediction of SOC was generated for each location. Throughout the entire computation process, subsets closer to the prediction location were weighted higher than subsets farther away. In EBK, it is possible to analyze the empirical distribution of the parameter because many variograms are estimated at each location. The grid size of the final output maps was 1.5 km. No transformation of SOC was introduced in any of the mapping processes.

RESULTS AND DISCUSSION FIG. 4. (A) Model-predicted and (B) TL-measured SOC difference from 1986 to 2009 for (A) topsoil and (B) subsoil (in grams per 100 g).

Prediction of SOC With Vis-NIR While calibration models for predicting SOC from the Danish soil spectral library using the regression rules from Cubist were being built, the software generated nine different

TABLE 2. Descriptive Statistics of SOC (SOC Content in g/100 g) in the Different Data Sets Data Set

Size

Calibration_A Calibration_M Validation_A Validation_M 2009_25A 2009_25M 1986_25A 1986_25M Diff_25A Diff_25M 2009_50A 2009_50M 1986_50A 1986_50M Diff_50A Diff_50M

2,081 2,081 691 691 164 164 164 164 164 164 164 164 164 164 164 164

Mean 0.74 0.72 0.78 0.77 1.65 1.61 1.69 1.66
0.04
0.05 0.98 0.97 1.04 1.08
0.06
0.12

S.D.

Range

Maximum

0.89 0.79 0.89 0.85 0.68 0.67 0.71 0.68 0.29 0.32 0.54 0.55 0.49 0.44 0.39 0.45

5.97 4.69 5.51 5.02 3.87 3.75 3.77 3.86 1.87 2.14 3.05 3.01 2.73 2.59 2.19 2.22

5.97 4.69 5.51 5.02 4.26 3.99 4.35 4.17 1.10 0.91 3.24 3.19 2.96 2.92 1.10 1.10

Minimum 0.00 0.00 0.00 0.00 0.39 0.24 0.58 0.31
1.09
1.23 0.19 0.18 0.23 0.33
1.09
1.11

Median 0.35 0.36 0.35 0.37 1.54 1.52 1.51 1.50 0.00
0.01 0.84 0.86 0.93 1.03
0.06
0.15

Skewness 2.18 1.71 1.79 1.57 1.56 1.22 1.78 1.69
0.83
0.76 1.75 1.17 1.57 1.09 0.59 0.46

Kurtosis 5.96 3.09 3.74 2.38 2.98 2.12 3.49 3.51 1.93 2.39 3.61 1.62 3.67 2.15 1.20 0.18

A: TL measured; M: model predicted; Diff: difference (SOC2009-SOC1986); 2009_25: topsoil (0- to 25-cm depth) from 2009; 1986_50: subsoil (25- to 50-cm depth) from 1986.

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rules to follow as submodels if the wavelength value met certain criteria. This meant that there were nine corresponding regression equations for SOC. In the nine rules, wavelengths 654, 764, 774, 904, 924, 1,654, 1,954, 2,094, and 2,304 nm were treated as stratifiers to these nine regression equations. Some of these wavelengths contain important functional groups for the formation of SOC, namely, aromatics (C-H), phenolics (C-OH), aliphatic C-H, and organic matter including cellulose, glucan, and pectin (Ben-Dor et al., 1997; Stenberg et al., 2010). The performance of the calibration model and its validation are shown in Table 1 and Fig. 2. Without any spectral pretreatment, the calibration model achieved good predictive ability (R2 = 0.87 for calibration, R2 = 0.80 for validation, RMSE = 0.40 and RPD = 2.1 in the validation data set). When the model was applied to data sets 2009_25, 1986_25, 2009_50, and 1986_50, the prediction of SOC was promising. The RPD value of 1.9 obtained for 2009_50 (Table 1; Fig. 3) indicates good prediction where quantitative prediction is possible, according to Viscarra Rossel et al. (2006a). The topsoil data sets (2009_25M and 1986_25M) had RPD values of 2.0 to 2.5, indicating very good quantitative predictions (Table 1; Fig. 3). In general, predictions were more accurate for the topsoil than for the subsoil in both years, with higher RPD values for the topsoil. The RMSEP ranged from 0.23 to 0.32 g/100 g for both topsoil and subsoil, suggesting acceptable prediction accuracy according to previous studies (Dunn et al., 2002; Islam et al., 2003; Mouazen et al., 2007). The RMSE and RPD values for 1986_25M and 1986_50M were similar to those for 2009_25M and 2009_50M, but R2 was higher for the 1986 data sets (R2 > 0.8) than for the 2009 data sets (R2 < 0.8). Therefore, prediction uncertainty was lower for the 1986 than for the 2009 samples. This may be because the calibration and validation soil samples from 1986 came from the same field sampling program as the soils used in the development of the Danish spectral library. Comparison of the TL-measured and model-predicted changes in SOC content between 1986 and 2009 indicated that, in the topsoil, the changes were mostly underpredicted (Fig. 4). The R2 value for topsoil SOC change was higher (R2 = 0.84) than that for the subsoil (R2 = 0.71), confirming that the topsoil model had better predictive ability than the subsoil model (Fig. 4B). This can be explained by a lower SOC content in the subsoil in

all four data sets, which tended to lead to larger errors in SOC prediction by Vis-NIR (Stenberg et al., 2010).

Basic Statistics on TL-Measured and Model-Predicted SOC Change The SOC content of the calibration data set ranged from 0 to 5.97 g/100 g, which covered the full range observed in all the data sets used (Table 2). The maximum increases in TL-measured and model-predicted SOC content from 1986 to 2009 were observed in the same profile topsoil. That topsoil also had the largest decline in model-predicted SOC (1.23 g/100 g). For the subsoil samples, the magnitude of change was the same as in topsoil samples. The greatest gain in measured SOC was 1.10 g/100 g, and the greatest loss was 1.09 g/100 g. The ranges of modelpredicted and TL-measured SOC change were comparable. From 1986 to 2009, TL-measured SOC content decreased in both topsoil and subsoil. The average measured decrease in topsoil was 0.04 g/100 g, which agreed well with the decline in modelpredicted SOC of 0.05 g/100 g (Table 2). In contrast, the mean change in model-predicted SOC in subsoil (Diff_50M) was twice that in TL-measured SOC (Diff_50A). Normality tests showed that SOC content was not normally distributed in any of these data sets, as indicated by the skewness and kurtosis values (Table 2). This suggests that the regression rule can produce good models even with non-normally distributed data. Box plots (Fig. 5) showed closer agreement between TLmeasured and model-predicted SOC content from 1986 to 2009 for topsoil than for subsoil. Both the TL-measured and modelpredicted SOC content in subsoil changed less than in topsoil. This can be explained by typical plowing depth on arable land in Denmark varying between 0.25 and 0.30 m, causing different levels of soil disturbance in topsoil and subsoil (Parton et al., 1987). Even though the SOC change predicted by Vis-NIR spectroscopy did not always correspond with the TL-measured SOC change, the general trend was very similar (Fig. 5).

Variation in TL-Measured and Model-Predicted SOC Content In georegions excluding East Denmark (i.e., West Jutland, Central Jutland, North Jutland, and Thy), no significant differences

FIG. 5. Box plot for TL-measured (-A) and model predicted (-M) SOC for topsoil (25 cm) and subsoil (50 cm) from 1986 and 2009 and SOC difference (Diff-) (in grams per 100 g; circles are outliers, and the ends of whiskers represent the minimum and maximum).

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0.11 0.006** 3 0.97

2

1

2009_25M 0.51 0.33 3 0.11 2

1

1986_25A

0.17 0.62 3 0.18 1 0.22 2 0.09 3 0.87 2

1

1986_25M

Diff_25 A

1 0.12 0.045* 3 0.73

2

Diff_25M

2009_50A

0.30 0.1 3 0.99 2

1

2009_50M

2

1 0.007** < 0.001*** 3 0.79

1986_50A

2

2

1

< 0.001*** < 0.001*** 3 0.94 1 0.02* < 0.001*** 3 0.72

1986_50M

Diff_50A

*P < 0.05, **P < 0.01, ***P < 0.001. 1 Data for all Denmark. 2 Data from East Denmark. 3 Data from the other regions in Denmark. A: TL measured; M: model predicted; Diff is difference (SOC2009-SOC1986); 2009_25: topsoil (0- to 25-cm depth) from 2009; 1986_50: subsoil (25- to 50-cm depth) from 1986.

Diff_50M

1986_50M Diff_50A

1986_50A

2009_50M

Diff_25M 2009_50A

1986_25M Diff_25 A

1986_25A

2009_25M

2009_25A

2009_25A

TABLE 3. Signed Rank Test P (Hypothesized Median Difference = 0)

2

1

< 0.001*** < 0.001*** 3 0.44

Diff_50M

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FIG. 6. Distribution of TL-measured (A) and model-predicted (M) SOC from 1986 topsoil.

FIG. 7. TL-measured (A) and model-predicted (M) change in SOC from 1986 to 2009 for topsoil (0–25 cm).

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Soil Science • Volume 178, Number 8, August 2013

(P > 0.1) were seen in the TL-measured or model-predicted SOC content in topsoil or subsoil samples from 1986 and 2009 (Table 3). In other words, during the 23-year study period, measured SOC content did not change markedly in either soil horizon in these regions. Furthermore, the signed rank tests did not show any significant differences between measured and predicted SOC. However, TL-measured and model-predicted SOC content in subsoils in East Denmark showed a significant decline between 1986 and 2009, whereas TL-measured and model-predicted topsoil SOC content did not differ. There were no significant differences between the 1986_25A and 1986_25M data sets for the whole of Denmark. However, a significant difference was found between the 2009_25A and 2009_25M data sets (Table 3). No general conclusions can be drawn about prediction based on the data sets from 1986 and 2009 subsoil because 1986_50A and 1986_50M were significantly different (P < 0.001), but 2009_50A and 2009_50M were not. For East Denmark, there were significant differences in the TL-measured and model-predicted change in SOC at both soil depths (Table 3), although the significance level was much higher for the subsoils.

Mapping SOC To visualize topsoil SOC from 1986, a map was compiled (Fig. 6). The patterns of both TL-measured and model-predicted SOC corresponded visually with the soil organic matter map derived from choropleth maps and 45,000-point samples (Greve et al., 2007). In West Jutland and North Jutland, the TLmeasured SOC content tended to be higher than in the other regions. The SOC content in East Denmark was lower than that in other regions, as can be seen in Fig. 6A. The overall spatial patterns in the two maps were in agreement with each other. However, the map generated from model-predicted SOC content was associated with two types of uncertainties, uncertainty in prediction

Vis-NIR Spectroscopy for Monitoring Temporal Changes in SOC

of SOC with Vis-NIR and uncertainty in kriging. Thus, more investigation of error propagation is needed for the map generated from model-predicted SOC (Fig. 6B). The change in SOC in the topsoil was mapped using both TL-measured and model-predicted data (Fig. 7). As with the maps of SOC content in 1986 (Fig. 6), the spatial patterns of the modelpredicted and measured SOC change corresponded well. As the two maps in Fig. 7 indicate, there were hardly any areas with SOC increases in the interval 0.05 to 1.1 g/100 g between 1996 and 2009. Approximately 60% of areas lost or gained very small amounts of SOC (0.05 g/100 g), whereas the remainder lost up to 1.1 g/100 g SOC (see also Table 3). The SOC changes in the subsoil displayed more spatial variation than those in the topsoil (Fig. 8). East Denmark showed marked SOC losses in both maps, but the map generated from model predictions had more abrupt boundaries than that based on measured differences. In the extreme north of Denmark, the measured data indicated large-scale losses of 0.05 to 1.1 g/100 g, whereas the model-predicted data showed only small changes (±0.05 g/100 g). There were more discrepancies in the maps for Diff_50A and Diff_50M (see also Table 3). According to Saby et al. (2008), the minimum detectable change in SOC concentration in Denmark is approximately 0.3 g/100 g using TL soil monitoring. However, this minimum detectable change is based on five strictly defined assumptions, including normal distribution of the mean change in SOC concentration. In contrast, Vis-NIR monitoring of SOC changes does not impose such requirements but produces similar results, as shown in this study. Therefore, Vis-NIR can be a better technique for soil monitoring than traditional monitoring methods.

CONCLUSIONS Considering the importance of SOC for soil-related ecosystem services and food security, it is essential to have a cost-effective

FIG. 8. TL-measured (A) and model-predicted (M) change in SOC from 1986 to 2009 for subsoil (25–50 cm). © 2013 Lippincott Williams & Wilkins

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Deng et al.

SOC monitoring system. Our results suggest that the Vis-NIR spectroscopy can be an effective tool for monitoring spatial and temporal changes in SOC. With minor changes in topsoil SOC, we found no significant differences in measured and predicted SOC concentrations. However, for larger changes in subsoil SOC, Vis-NIR significantly underpredicted TL-measured SOC values. This was reflected in the spatial pattern of SOC difference from 1986 to 2009, for which subsoil had more dissimilarities. The TL-measured and the predicted topsoil SOC maps displayed more similarities than the corresponding SOC maps for subsoil. The SOC level in East Denmark decreased significantly from 1986 to 2009 according to both TL-measured and predicted data, whereas other regions of Denmark showed no obvious change. The signed rank test and SOC maps did not always show consistency between TL-measured and predicted SOC, but the general trend and most of the spatial characteristics were similar. Visible NIR prediction thus seems promising for monitoring SOC change compared with traditional monitoring methods. Longer-term or more frequently determined SOC monitoring data can provide a better understanding of how to use Vis-NIR for spatiotemporal detection. Further studies on improving the predictive ability of Vis-NIR based on a national spectral library are needed for national-scale SOC monitoring, involving, for example, the division of samples into different parent materials or georegions.

Croft H., N. J. Kuhn, and K. Anderson. 2012. On the use of remote sensing techniques for monitoring spatio-temporal soil organic carbon dynamics in agricultural systems. Catena 94:64–74.

ACKNOWLEDGMENTS Technical support from Karin Dyrberg, Henrik Nørgaard, and Jørgen Munksgaard Nielsen is greatly appreciated. Emmanuel Arthur and Margit Schacht are also gratefully acknowledged for language corrections. The authors thank the Danish Agency for Science, Technology and Innovation and the University of Sydney for providing the funding and laboratory facilities.

Hunt G. R. 1977. Spectral sigantures of particulate minerals in the visible and near infrared. Geophysics 42:501–513.

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