Natural Resources and Environmental Management Department, University of Hawai i Mānoa, 1910 East-West Road, Sherman 101, Honolulu, HI 96822, USA 2

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1 Hindawi Publishing Corporation Applied and Environmental Soil Science Volume, Article ID 9, pages doi:.//9 Research Article Effects of Subting by Carbon Content, Soil Order, and Spectral Classification on Prediction of Soil Total Carbon with Diffuse Reflectance Spectroscopy Meryl L. McDowell, Gregory L. Bruland,, Jonathan L. Deenik, and Sabine Grunwald Natural Resources and Environmental Management Department, University of Hawai i Mānoa, 9 East-West Road, Sherman, Honolulu, HI 98, USA Biology & Natural Resources Department, Principia College, Maybeck Place, Elsah, IL 8, USA Tropical Plant and Soil Sciences Department, University of Hawai i Mānoa, 9 Maile Way, Honolulu, HI 98, USA Soil and Water Science Department, University of Florida, 9 McCarty Hall, P.O. Box 9, Gainesville, FL -9, USA Correspondence should be addressed to Meryl L. McDowell, mcdowell@hawaii.edu Received March ; Revised August ; Accepted October Academic Editor: Sabine Chabrillat Copyright Meryl L. McDowell et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Subting of s is a promising avenue of research for the continued improvement of prediction models for soil properties with diffuse reflectance spectroscopy. This study examined the effects of subting by soil total carbon (C t ) content, soil order, and spectral classification withk-means cluster analysis on visible/near-infrared and mid-infrared partial least squares models for C t prediction. Our was composed of various Hawaiian from primarily agricultural lands with C t contents from <% to %. Slight improvements in the coefficient of determination (R ) and other standard model quality parameters were observed in the models for the sub of the high activity clay soil orders compared to the models of the full. The other sub models explored did not exhibit improvement across all parameters. Models created from subs consisting of only low C t s (e.g., C t < %) showed improvement in the root mean squared error (RMSE) and percent error of prediction for low C t soil s. These results provide a basis for future study of practical subting strategies for soil C t prediction.. Introduction Diffuse reflectance spectroscopy (DRS) and chemometric analysis have become popular subjects of research for their potential to predict soil carbon and other soil properties. This methodology could be beneficial for monitoring soil quality and temporal variation, as well as helping to facilitate digital soil mapping efforts. Both visible/near-infrared (VNIR) and mid-infrared (MIR) spectra show promise for the prediction of soil total carbon (C t ) and organic carbon, as well as organic matter, total N, total P, sand, silt, and clay fractions, cation exchange capacity, and ph (e.g., [ 8]). Particular attention has been given to soil carbon, which is an important indicator of soil fertility and biological activity and is crucial to carbon sequestration endeavors [9 ]. Partial least squares regression (PLSR) appears to be the most widely used chemometric method for developing prediction models from soil diffuse reflectancespectra. A is commonly divided into two groups with the larger used for calibration and the smaller for validation to approximate true independent model validation, but no clear or consistent guidelines have been adopted for this process. Model results are known to vary with different groupings of s for the calibration and validation s. To address this issue, some studies have created multiple models, each with different random divisions of the into calibration and validation s, to reflect the range of possible results [, ]. Highly accurate prediction models are required for DRS to be an effective method for soil carbon determination in practical applications. Many statistically robust models have

2 Applied and Environmental Soil Science been developed (e.g., [ 8, ]), but a single procedure is not necessarily the best for producing high quality models from different in different locations. Even models that have excellent correlation between soil spectra and properties could be improved. For instance, the robust PLSR models of McDowell et al. [8] have relatively large errors in C t prediction at very low C t values, which decreases the utility of the models in situations where low C t or small changes in C t are examined. Additional methods are being explored to produce the most robust and accurate DRS prediction models possible for different local and global soil datas. One promising idea is to split the into groups based on similar characteristics and to develop individual prediction models for each of these subs. In studies of from Poland, Brazil, and Florida (USA), previous researchers have investigated subting by characteristics such as carbon content, soil order, soil texture, and spectral similarity with varied success for their particular s [ 8]. The current work aimed to improve the prediction of C t with VNIR and MIR DRS by creating attributespecific chemometric models. Specifically, we investigated if predictions from a chemometric model built only from a sub of s that are similar with respect to a particular characteristic (i.e., C t ) will provide better predictions than a comprehensive model built from a of all possible s. The study investigated the following three subting strategies: () soil C t value; () soil order; () spectral classification with k-means cluster analysis. Each of the various sub models was compared against the original full model to assess the magnitude of changes in the predictions. This study was built upon the research reported in McDowell et al. [8]. In that work the authors demonstrated the ability of DRS to predict C t in Hawaiian. The success of different wavelength ranges (i.e., VNIR versus MIR) and chemometric methods was investigated, as well. Because these ideas have been previously explored in McDowell et al. [8], they will not be discussed further here.. Materials and Methods.. Sample Collection and Preparation. Thefor this study is composed of 7 soil s collected across the five main Hawaiian Islands of Kauai, Oahu, Molokai, Maui, and Hawaii, illustrated in Figure. Two hundred and sixteen of these s were collected from 98 to 7 and stored in the archive at the Natural Resources Conservation Service (NRCS) National Soil Survey Center in Lincoln, Nebraska, and the remaining 9 s were newly collected in. Within this full of s, soil orders and more than soil series are represented. Samples were predominantly from a variety of agricultural, hosting over different crop types. The majority of s are of surface ( 77%), and the remainder are of corresponding subsurface soil horizons from 7 of the collection sites. The soil s were dried and sieved to retain the less than mm fraction for VNIR DRS analysis. A portion of each was also ball-milled to less than µm for MIR DRS analysis. Soil order Andisol Aridisol Entisol Histosol Inceptisol Mollisol Oxisol Spodosol Ultisol Vertisol Kilometers Figure : Distribution of collection sites throughout the Hawaiian Islands with symbol color indicating soil order... Traditional Total Carbon Analysis. Dry combustion was used to measure the C t of ball-milled soil s. Several of the s obtained from the NRCS archive were previously measured for C t by dry combustion before storage. All remaining s were analyzed at the Agricultural Diagnostic Services Center (ADSC) at the University of Hawaii Mānoa with an LECO CN combustion gas analyzer [9]. A small portion of the previously measured NRCS archive s were reanalyzed at ADSC to provide a cross-check of the values obtained from different laboratories. The C t values of the full range from <% to % with a distribution weighted toward the lower C t end... Visible/Near-Infrared Diffuse Reflectance Spectroscopy. Visible/near-infrared diffuse reflectance spectra were collected from the mm sieved soil s with an Agrispec spectrometer and muglight light source (Analytical Spectral Devices, Inc., Boulder, CO, USA). The Agrispec has three detectors with a combined spectral range of to nm, sampling interval of nm, and spectral resolution from nm (at 7 nm) to nm (at nm). Each soil was measured three times, with the cup rotated between each measurement. The three spectra were averaged to produce the final spectrum for each. A Spectralon (Labsphere, North Sutton, NH, USA) white reference was measured as a reference spectrum to begin each session and again every minutes or less thereafter. A slight off in reflectance between the range covered by the first and second detectors was observed in many spectra, and, therefore, we removed the narrow region of 99 nm from the final spectra for analysis. The VNIR spectra of these commonly exhibit features associated with OH,H O, iron oxides, phyllosilicates, and organic molecules. For regression N

3 Applied and Environmental Soil Science analysis the spectra were transformed using the pretreatment identifiedasmosteffective for this data in McDowell et al. [8]. For the VNIR spectra, this optimal preprocessing transformation was mean normalization... Mid-Infrared Diffuse Reflectance Spectroscopy. Midinfrared diffuse reflectance spectra were collected from the ball-milled s in neat form with a Scimitar FTIR spectrometer (Varian, Inc., now Agilent Technologies, Santa Clara, CA, USA) and diffuse reflectance infrared Fourier transform (DRIFT) accessory. The spectral range is to cm, with a sampling interval of cm and spectral resolution of cm (note:therangeofourmirspectra overlaps slightly with the range of our VNIR spectra.) Spectra were corrected for background atmospheric and instrument effects by the subtraction of the spectrum of KBr powder measured between every seven s, but features in two narrow regions persisted. Therefore, we excluded the regions of 9 cm and 8 9 cm from the analysis. Features in the MIR spectra of these are attributable to OH, organic molecules, and a variety of silicate minerals. Based on the findings of McDowell et al. [8], before regression analysis the Savitzky-Golay st derivative transformation was applied to the MIR spectra as this was determined to be the most effective pretreatment for this data... Regression Analysis. Partial least squares regression (PLSR) was employed to develop the chemometric models for C t prediction. Models were generated using the Unscrambler X Software package (CAMO Software Inc., Woodbridge, NJ, USA). The spectral range included in the analysis was decreased slightly by removing any high noise portions at the limit of the range; therefore, the VNIR spectra were restricted to the range of nm, and the MIR spectra were restricted to 89 cm. All spectra were mean centered for PLSR analysis. The optimal number of factors for regression was chosen individually for each model based on maximizing the explained variance but minimizing the possibility of over fitting. We considered several parameters when assessing the quality of models, including the coefficient of determination (R ), root mean squared error (RMSE), residual prediction deviation (RPD) [], and the ratio of performance to interquartile distance (RPIQ) []. We defined the RPD as the ratio of the standard deviation of the validation to the standard error of prediction (RPD = SD/SEP) and the RPIQ as the ratio of the interquartile distance of the validation to the standard error of prediction (RPIQ = IQ/SEP), where the interquartile distance is the difference between the third and first quartiles (IQ = Q Q). With respect to these general model quality parameters, the best model would have the highest R,RPD, and RPIQ, and the lowest RMSE. We also examined the success of the predictions for individual s using the percent error, calculated as the absolute difference between the measured (i.e., by combustion) and predicted (i.e., by DRS) C t values, divided by the measured value, and multiplied by... Sample Subting. The motivation behind our selected subting strategies was to improve C t prediction while still retaining the simplicity that makes DRS attractive. We focused on subting criteria that did not require additional highly detailed soil characterization, instead relying on general soil data and information within Soil Taxonomy.... C t Content Subs. A simple grouping of into low and high C t was used for subting by C t value. Preliminary work tested a variety of low C t /high C t divisions (e.g.,,,, 8, and % C t ) iteratively. The initial results showed that a cutoff of % C t was most promising and therefore was used for the final analysis. Additionally, a division at % allows for fairly easy assignment of unknown into low or high C t groupings from C t estimates based on general or readily available soil information. To approximate independent validation, s were randomly split into a group of 7% for model calibration and % for model validation. This random selection was repeated to produce iterations of calibration/validation pairs from the full. After this split, the s from each iteration were divided into low C t (<%) and high C t (>%) subs. Separate VNIR and MIR regression models were then developed from the low C t and high C t portions of each of the iterations. For comparison, VNIR and MIR regression models from the full using these same calibration and validation divisions, but no separation by C t value, also were created.... Soil Order Subs. Four broad soil groups were created based on general similarity of soil order and number of s available of that type. The allophane-dominated volcanic comprised one group (n = 9), the Aridisol, Entisol, Inceptisol, Mollisol, and Vertisol were combined to make a second group (high activity ; n = ), Oxisol and Ultisol made a third group (low activity ; n = 7), and Histosol and Spodosol comprised the fourth group (organic-dominated ; n = ). These soil groupings are based upon information con tained in Soil Taxonomy allowing for the development of soil groups according to clay mineralogy and soil organic matter. Table provides information on additional soil properties for each soil sub where available. The average spectra for each of these soil groups are shown in Figure. Nine soil s from the NRCS archive had no recorded taxonomic classification and therefore were not included in these subs. The full was randomly divided times into a group of 7% of s to be used for the calibration of the regression models and % of the s to be used for validation. After this division, the s of each of the ten iterations were grouped according to soil order as described above. Separate VNIR and MIR regression models were then developed for each soil group sub within each of the ten calibration/validation iterations. Because the number of low activity clay and organic-dominated soil s was small (e.g., 8), full cross validation (i.e., leave-one-out cross validation) was used with the regression models for these

4 Applied and Environmental Soil Science Table : Soil properties of selected s for each soil grouping used in subting by soil order. Values listed in the table are the minimum and maximum for that specific sub with the mean in parentheses. Data is provided for s from the Natural Resources Conservation Service (NRCS) archive where it is available. The compositional information (i.e., ph, texture, Al, Ca, and Fe) for the s newly collected in has yet to be determined. Lowactivityclay a Only one data point available. Total carbon wt% Organic carbon wt% Clay wt% Silt wt% Sand wt% ph Total Al wt% Total Ca wt% Total Fe wt% (.9) (.) (7.) (.) (.8) (.) (8.) (.) (.9) (.) (.9) (.7) (.8) (.) (.89).9 a. a. a (.) (.) (7.) (.8) (7.) (.9) (8.8) (.9) (.) (.9) (.) (.8) (.) (7.8) (.9) Not available

5 Applied and Environmental Soil Science VNIR Reflectance (%) Wavelength (nm) (a) MIR Reflectance (%) Wavenumber (cm ) (b) Figure : Average (a) visible/near-infrared (VNIR) and (b) midinfrared (MIR) diffuse reflectance spectra of soil groups used in subting by soil order. Dashed lines represent one standard deviation from the average. two groups rather than committing % of those s to validation as with the other subs. Additional models were created from the calibration/validation divisions of the full with no separation of soil order for the comparison of results without subting. A full cross validation model of the full was developed to be compared with the low activity clay and organic-dominated soil subs full cross validation models.... Spectral Classification Subs. Our rationale behind grouping soil s by spectral character is based on the assumption that this approach removes major spectral variation from consideration so that small-scale variation is used to produce a more refined Ct prediction model. Also, the division of soil s into subs created solely from spectral classification has the advantage of requiring no additional information about the soil. The spectral classification subs were created by kmeans cluster analysis with Unscrambler X. Spectra were assigned to three cluster subs based on the minimum Euclidean distance to cluster centers. Separate analyses were conducted for the VNIR and MIR spectra, resulting in different combinations of s in their cluster subs. The spectral range used for these cluster analyses was limited to the regions most relevant to carbon prediction as previously determined by the PLSR variable significance analysis by McDowell et al. [8]. Specifically, the ranges used were 7, , 9 98, 7, and 88 nm for the VNIR spectra and 87, 9,,,, and 8 cm for the MIR spectra. Each cluster sub was randomly divided into a group of 7% for model calibration and a group of the remaining % for model validation, unless the number of s in the cluster was small (e.g., 8), in which case s were not divided and full cross validation was performed. The random division into calibration and validation groups was repeated nine more times to give calibration/validation pairs for each of the VNIR and MIR cluster subs. Separate Ct prediction models were created for each of the different cluster subs. For comparison, we also developed VNIR and MIR models from the full. The calibration and validation groups for these models were created by combining the respective calibration or validation groups from the three different cluster sub models. VNIR and MIR full cross validation models using the full were also produced to compare with full cross validation models from small cluster subs.. Results and Discussion.. Modeling of Ct Content Subs. The VNIR models sub by Ct content produced the results summarized in Table and plotted in Figure (a). The range of results from the random divisions of the s into 7% calibration and % validation groups is given along with their mean value. The R, RPD, and RPIQ values for the low Ct sub were not as good as those produced using the full, though the RMSE values were lower for the low Ct sub. The results for the high Ct models approached, but were not quite as good, as the results from the full. Results from the MIR Ct sub models are shown in Figure (b) and Table. The models produced by the low Ct sub were generally of lesser quality than those of the full, with the exception of better RMSE values, a trend similar to the VNIR models. The high Ct models were comparable overall to the high quality models produced by using the full. From these results, it appears that a separate high Ct prediction model is not an improvement over a model utilizing the full Ct range of available s for either the VNIR or MIR spectra from this data. This statement may be true for a separate low Ct prediction model as well, but the benefit of a lower RMSE should also be considered.

6 Applied and Environmental Soil Science Table : Detailed partial least squares regression model results for soil total carbon (C t ) prediction from the subs of visible/near-infrared diffuse reflectance spectra based on C t content. The range of values reflects the results of random iterations of the models, and the number in parentheses is the mean. Detailed results are also given for full models with no subting for comparison. C t < % 7 C t > % 8 8 n a R,b RMSE (%) c n R RMSE (%) RPD d RPIQ e (.) (.) (.) (.9) (.) (.) (.8) (.) (.8) (.87) (.) (.) (.9) (.) (.9) (.) (.) (.9) a Number of s. b Coefficient of determination. c Root mean squared error. d Residual prediction deviation. e Ratio of performance to interquartile distance. Table : Detailed partial least squares regression model results for soil total carbon (C t ) prediction from the subs of mid-infrared diffuse reflectance spectra based on C t content. The range of values reflects the results of random iterations of the models, and the number in parentheses is the mean. Detailed results are also given for full models with no subting for comparison. C t < % 7 C t > % 8 8 n a R,b RMSE (%) c n R RMSE (%) RPD d RPIQ e (.9) (.8) (.8) (.) (.) (.) (.9) (.) (.9) (.7) (.) (.7) (.9) (.) (.9) (.8) (.7) (.7) a Number of s. b Coefficient of determination. c Root mean squared error. d Residual prediction deviation. e Ratio of performance to interquartile distance. Results varied for previous studies examining the behavior of separate models based on carbon content. Madari et al. [] found that limiting the C t in their NIR and MIR calibration models to. 99. g kg and. 9.9 g kg decreased the not only R, but also the root mean squared deviation (RMSD) compared to the original NIR and MIR models (. g kg C t ); this behavior is similar to that observed in the low C t models presented here. The study by Vasques et al. [8] developed separate VNIR organic carbon prediction models for their mineral and organic soil s, which roughly correspond to division by carbon content in this case (mineral,..7% carbon; organic,. 7.% carbon). Compared to the original combined model, the R improved for both of the sub models, but the RMSE decreased for the lower carbon mineral group and increased for the higher carbon organic group. The increase in R values for the sub models differsfromwhatisseen in our work and that of Madari et al. [] and is an example of with different characteristics responding differently to the same treatment... Modeling of Soil Order Subs. The results of the VNIR models from the soil order subs are given in Table and Figure (a). The models from the Andisol sub did not perform as well as the models using the full. The R, RMSE, and RPD values for the high activity clay sub were similar to those of the full models, but the RPIQ values were generally slightly lower. The low activity clay and organic-dominated subs were not validated with an independent validation due to small numbers, and therefore their results may be overly optimistic. Compared to a full cross validation of a model created from the full, the low activity clay sub model did not perform as well, except when considering the RMSE parameter, whereas the organic-dominated sub model is broadly similar. Table and Figure (b) show the results of the MIR soil order sub models. The models produced by the Andisol sub had no improvement on the models produced by the full. Results for the high activity clay sub models were as good as or better than the full model results, with the exception of lower RPIQ values. The

7 Applied and Environmental Soil Science 7 8 VNIR R.8... RMSE 7 RPD RPIQ C t < C t > C t < C t > C t < C t > C t < C t > (a) MIR R.. RMSE RPD RPIQ. C t < C t > C t < C t > C t < C t > C t < C t > mean (b) Figure : Visual assessment of partial least squares regression model results for soil total carbon (C t ) prediction from subs of (a) visible/near-infrared (VNIR) and (b) mid-infrared (MIR) diffuse reflectance spectra based on C t content. The parameters given are the coefficient of determination (R ), root mean squared error (RMSE, %), residual prediction deviation (RPD), and the ratio of performance to interquartile distance (RPIQ). The range of values reflects the results of random iterations of the models. Results are also shown for full models with no subting for comparison. overall performance of the low activity clay and organicdominated sub models using full cross validation was not quite as good as the full cross validation model from the full. These results suggest that a separate prediction model for the high activity clay soil orders may have a slight advantage compared to a model with all available soil orders for both the VNIR and MIR spectra of this data. Separate prediction models for the other soil order subs do not appear to be as promising. AstudybyMadarietal.[] also investigated the benefits of subting their s according to soil order. The authors produced separate models for the Histosols and Spodosols, the Ferralsols (classification according to the World Reference Base [], approximately equivalent to most of the Oxisol soil order), and the Acrisols (classification according to the World Reference Base [], consisting of many Ultisol suborders and some Oxisols). The results of these models varied. The Ferralsol and the Acrisol NIR and MIR models had lower R than the original model and also lower RMSD; these two subs included relatively low C t ( 8. g kg and.7 9. g kg, resp.) compared to the full (. g kg ), so this lower R and lower RMSD are a similar behavior to the low C t sub models in the current study. The Histosol and Spodosol sub NIR and MIR models in Madari et al. [] resulted in slightly higher R values and much higher RMSD values. Our Histosol and Spodosol (i.e., organic-dominated ) sub models did

8 8 Applied and Environmental Soil Science Table : Detailed partial least squares regression model results for soil total carbon (C t ) prediction from the subs of visible/near-infrared diffuse reflectance spectra based on soil order. The range of values reflects the results of random iterations of the models, and the number in parentheses is the mean. Detailed results are also given for full models with no subting for comparison. For models with full cross validation (i.e., leave-one-out cross validation), the same s used to calibrate the model were used to validate the model. 7 n a R,b RMSE (%) c n R RMSE (%) RPD d RPIQ e (.7) (.) (.9) (.8) (.) (.8) (.9) (.7) (.9) (.) (.) (.8) cross validation cross validation (.9) (.89) (.9) (.) (.8) (.) cross validation a Number of s. Coefficient of determination. Root mean squared error. Residual prediction deviation. e Ratio of performance to interquartile distance. Table : Detailed partial least squares regression model results for soil total carbon (C t ) prediction from the subs of mid-infrared diffuse reflectance spectra based on soil order. The range of values reflects the results of random iterations of the models, and the number in parentheses is the mean. Detailed results are also given for full models with no subting for comparison. For models with full cross validation (i.e., leave-one-out cross validation), the same s used to calibrate the model were used to validate the model. 7 n a R,b RMSE (%) c n R RMSE (%) RPD d RPIQ e (.9) (.9) (.79) (.) (.) (.) (.98) (.7) (.9) (.) (.7) (.) cross validation cross validation (.9) (.78) (.9) (.9) (.7) (.89) 7.9. cross validation a Number of s. Coefficient of determination. Root mean squared error. Residual prediction deviation. e Ratio of performance to interquartile distance. not have significantly increased R values, but the validation RMSE values were greater than the full models values. Vasques et al. [8] developed separate organic carbon prediction VNIR models for each of the seven soil orders in their consisting of from Florida, southeastern USA Compared to the original model containing all of these mineral soil s, six of the seven soil order sub models resulted in improved R values (Alfisols, Entisols, Inceptisols, Mollisols, Spodosols, and Ultisols). The RMSE values were also similar or better for these subs. The Histosol sub model was the only one that did not improve in R or RMSE. These results are somewhat different from those in this study, where only the high activity (i.e., Aridisols, Entisols, Inceptisols, Mollisols, and Vertisols) are suggested to provide an overall improvement on models including all available s... Modeling of Spectral Classification Subs. The k-means cluster analysis of the VNIR spectra resulted in an unequal distribution of s between the three clusters. The

9 Applied and Environmental Soil Science 9 8 VNIR 8 7 R.8... RMSE 7 RPD 7 RPIQ (a) 7 MIR R.8... RMSE RPD RPIQ cross validation mean (b) Figure : Visual assessment of partial least squares regression model results for soil total carbon (C t ) prediction from subs of (a) visible/near-infrared (VNIR) and (b) mid-infrared (MIR) diffuse reflectance spectra based on soil order. The parameters given are the coefficient of determination (R ), root mean squared error (RMSE, %), residual prediction deviation (RPD), and the ratio of performance to interquartile distance (RPIQ). The range of values reflects the results of random iterations of the models. Results are also shown for full models with no subting for comparison. Cluster sub consisted of only 78 s ( % C t ) and therefore all 78 s were used in its model calibration and full cross validation. The Cluster and Cluster subs contained s ( % C t ) and s ( % C t ), respectively, allowing for the independent validation of the models as initially planned. The results of the VNIR C t prediction models from each of the clusters are given in Table and Figure (a). A comparison of the Cluster sub model with a full cross validation model of the full showed that the sub model was not quite as robust, though it did produce a higher RPIQ value. The Cluster and Cluster sub models results generally

10 Applied and Environmental Soil Science VNIR.8 R.. RMSE RPD RPIQ. Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster (a) MIR.8 R.. RMSE RPD RPIQ. Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster cross validation mean (b) Figure : Visual assessment of partial least squares regression model results for soil total carbon (C t ) prediction from the subs of (a) visible/near-infrared (VNIR) and (b) mid-infrared (MIR) diffuse reflectance spectra based on spectral classification with k-means cluster analysis. The parameters given are the coefficient of determination (R ), root mean squared error (RMSE, %), residual prediction deviation (RPD), and the ratio of performance to interquartile distance (RPIQ). The range of values reflects the results of random iterations of the models. Results are also shown for full models with no subting for comparison. had lower (i.e., better) RMSE values, but were otherwise not quite as robust as the full models results. In the cluster analysis of the MIR spectra, the distribution of s was heavily weighted toward the Cluster (7 s, % C t ) and Cluster ( s, % C t ) subs. The Cluster sub contained only 8 s ( % C t ) and was validated with full cross validation instead of independent validation. Table 7 and Figure (b) present the results of the prediction models from the cluster subs, as well as those from the full models for comparison. The results for the Cluster sub models are broadly similar to those of the full models but overall they are not an improvement. Results from the full cross validation of Cluster sub were slightly higher for calibration but much lower for validation than the full cross validation of the full. In general, the Cluster model is not as robust as the full model. The overall performance of Cluster sub models is not

11 Applied and Environmental Soil Science Table : Detailed partial least squares regression model results for soil total carbon (C t ) prediction from the subs of visible/near-infrared diffuse reflectance spectra based on spectral classification withk-means cluster analysis. The range of values reflects the results of random iterations of the models, and the number in parentheses is the mean. Detailed results are also given for full models with no subting for comparison. For models with full cross validation (i.e., leave-one-out cross validation), the same s used to calibrate the modelwereusedtovalidatethemodel. n a R,b RMSE (%) c n R RMSE (%) RPD d RPIQ e Cluster cross validation Cluster 87 Cluster (.77) (.8) (.7) (.89) (.) (.) (.8) (.9) (.8) (.) (.9) (.7) (.9) (.) (.88) (.) (.8) (.) cross validation a Number of s. b Coefficient of determination. c Root mean squared error. d Residual prediction deviation. e Ratio of performance to interquartile distance. Table 7: Detailed partial least squares regression model results for soil total carbon (C t ) prediction from the subs of mid-infrared diffuse reflectance spectra based on spectral classification with k-means cluster analysis. The range of values reflects the results of random iterations of the models, and the number in parentheses is the mean. Detailed results are also given for full models with no subting for comparison. For models with full cross validation (i.e., leave-one-out cross validation), the same s used to calibrate the modelwereusedtovalidatethemodel. n a R,b RMSE (%) c n R RMSE (%) RPD d RPIQ e Cluster 9 (.9) (.) (.8) (.) (.) (.) Cluster cross validation Cluster (.9) (.) (.8) (.) (.) (.) (.9) (.98) (.9) (.8) (.) (.8) 7.9. cross validation a Number of s. b Coefficient of determination. c Root mean squared error. d Residual prediction deviation. e Ratio of performance to interquartile distance. quite as good as the full models, but the limited C t range of Cluster sub is apparent from its much lower range of RMSE values. For this, the spectral classification by k-means clustering and separate prediction model for each cluster was not an obvious improvement over the original full VNIR or MIR models. The most noticeable difference is the lower RMSE for the sub models from clusters limited to low C t values. We have found one other study that investigated the effect of subting a by spectral classification for the prediction of soil carbon. Cierniewski et al. [7] tested the effectoffourdifferent unsupervised classification algorithms (k-means, expectation-maximization, Ward s Euclidean distance, and Lance and Williams Euclidean distance) on simple linear regression results from VNIR data. These clustering algorithms produced five or six clusters, and the number of s per cluster ranged from four to. This is in contrast to the method of k-means cluster analysis used in our study, where we specified that three clusters be produced to decrease the probability of a very low number of s in a cluster that would not be adequate for robust modeling. Cierniewski et al. [7] found that the majority of their cluster subs had improved R values compared to the original

12 Applied and Environmental Soil Science full. An increase in R was not observed for the spectral classification subs in the current work. Instead, the most significant improvement was a lower RMSE for many of the cluster sub models. Because other parameters such as RMSE were not provided in Cierniewski et al. [7], it is difficult to determine if this behavior is an effect of their subting study... Percent Error of Prediction. The sub models with improved RMSE values but an otherwise less-robust performance may still hold an advantage over the original full model. If a more accurate prediction of the low C t s makes a significant contribution to the lowered RMSE, the model could be very helpful in addressing the issue of large errors at low C t values. To evaluate the error at these low C t values, the percent error of prediction was calculated for the s with C t values less than % and the average value was reported for each model (Figure ). We use percent error rather than RMSE for comparing the sub models with the full model to normalize the error of the predicted value with respect to its measured value. The mean value of the average percent error for each of the ten iterations of the full model is %, but the average percent error for a single model could be up to almost % (Figure ). For example, with ameasuredvalueof%c t, an error of % would be translated to a predicted value of % C t. The MIR full models have lower average percent error, with a mean average percent error of % and a maximum average percent error of %. Many of the low RMSE sub models have noticeably lower average percent errors. The low C t VNIR and MIR models and the Cluster MIR models appear to have the most significant improvement, with average percent errors of 8% or less. For a measured value of % C t, a percent error of 8% would reduce the predicted value to.8% C t. Clusters and VNIR models also show moderate improvement, with all average percent error results below 7%. The average percent error of the low activity full cross validation model is slightly lower than the full model for both the VNIR and MIR data. The organic-dominated sub includes only two s with C t <%, so a comparison of average percent error is not as reliable in this case. The subs with the largest decreases in average percent error of prediction at low C t content (i.e., C t < %) are the ones that included only low C t s in their models. The low C t VNIR and MIR models contained s with C t values between and 9.9% C t, and the Cluster MIR models had s with C t values between and % C t. These results suggest that a separate model for low C t s is beneficial for the accuracy of prediction for the s in this range. This advantage is indicated by the RMSE of low C t models, but may not be obvious from the R parameter. The issue of relatively large errors of prediction for s with very low C t content has been understudied. To our knowledge there are no studies that have provided quantitative information addressing the degree of scatter observed for low C t on most predicted versus measured plots... Variation in Model Parameters. The ranges of PLSR model parameters produced by the iterations of random calibration/validation divisions in this study appear to be larger than the ranges of values encountered in previous studies where multiple PLSR model iterations were used. Brown et al. [] reported results for five models produced from different random divisions of the into 7% calibration and % validation groups. Values for organic carbon prediction from VNIR data ranged from.7 to.8 for R,.8 to. for RMSD, and.9 to. for RPD. Mouazen et al. [] included three model iterations with random divisions into 9% calibration and % validation groups in their study. The exhaustive results are not reported, but visual estimation from plots of the mean and standard deviation for the R and RMSE from the organic carbon prediction models suggests that the variation is similar to that in Brown et al. [] orless.thegreaterrangeinmodel parameters observed in our study may be related to the testing of a greater number of iterations (i.e., rather than five or three), or it could be related to a less obvious attribute, such as a greater variation in a spectral character within the.. Summary and Conclusions Our research has provided an introduction to the understudied idea of subting based on criteria that are simple and easily applied. This particular investigation of subting for C t prediction had varied results with our Hawaiian. Of all the different sub models created based on C t content, soil order, and spectral classification, the sub of high activity clay soil orders was the only one to show improvement across all parameters (i.e., R, RMSE, RPD, and RPIQ) compared to the full. Notably, one significant advantage was discovered; the subs including only low C t s (e.g., C t < % sub, MIR Cluster sub) produced models with much lower RMSE values compared to the full models, even though the other model parameters were not as robust. The lower RMSE for these models corresponds to a significant decrease in the percent error of predictions for low C t s, which could be very helpful for the analysis of with low C t content or monitoring of small changes in C t. Incorporation of a low C t sub model in the future prediction of unknown C t values could be done by first employing a model created with the full possible range of C t values and then utilizing the separate low C t sub model if the soil is predicted to have low C t. As seen from this study and previous studies, the effect of subting can have different results depending on the character of the and the number of s it includes. A small size may have limited the improvement possible by subting in the current work. In an effort to keep the size of subs large enough for regression analysis, the subting may have been too coarse (e.g., too few subs for C t prediction by soil order and spectral classification). The types of subting strategies explored here may be most helpful for large datas and should be tested with further research. Regardless of the strategy used to develop

13 Applied and Environmental Soil Science VNIR Average percent error for Ct <% s Average percent error for Ct <% s Average percent error for Ct <% s Ct < Cluster Cluster Cluster (a) 8 MIR 8 8 Average percent error for Ct <% s 8 Average percent error for Ct <% s 8 Average percent error for Ct <% s 8 Ct < Cluster Cluster 7% calibration/% validation results Mean cross validation result (b) Figure : Average percent error of the C t <% portion of the (a) visible/near-infrared (VNIR) and (b) mid-infrared (MIR) sub and full models in this study. The range of values reflects the results of random iterations of the models. The VNIR and MIR high C t models and the MIR Cluster models were not included because all s had C t >%.

14 Applied and Environmental Soil Science a model, our results suggest that multiple iterations of models with different calibration/validation groupings may help to produce a more complete picture of the overall model quality. Acknowledgments This research was supported by USDA CSREES TSTAR Project 9--8 and UHM College of Tropical Agriculture and Human Resources (CTAHRs) Hatch Project HA-. The authors thank J. Hempel, L. West, T. Reinsch, L. Arnold, and R. Nesser of the NRCS National Soil Survey Center in Lincoln, NE, USA, for help with access, sampling, and scanning of the archived s; L. Muller and A. Quidez for help with scanning of s at UHM; Drs. G. Uehara, R. Yost, and D. Beilman of UHM for the support of this project. They also appreciate the Hawaii landowners, managers, and extension agents that gave them access to their fields for collecting soil s. These include from Kauai: R. Yamakawa and J. Gordines (CTAHR), S. Lupkes (BASF), and Grove Farms; from Oahu: R. Corrales, A. Umaki, and J. Grzebik (CTAHR), Hoa Aina, MAO Organic Farm, Nii Nursery, J. Antonio and M. Conway (Dole), C. and P. Reppun,L.Santo,T.Jones,andN.Dudley(HARC),andA. Sou (Aloun Farms); from Molokai: A. Arakaki (CTAHR), K. Duvchelle (NRCS), and R. Foster (Monsanto); from Maui: J. Powley and D. Oka (CTAHR), M. Nakahata and M. Ross (HC&S), T. Callender (Ulupono), and B. Abru. References [] J. B. Reeves III, G. W. McCarty, and V. B. Reeves, Mid-infrared diffuse reflectance spectroscopy for the quantitative analysis of agricultural, Journal of Agricultural and Food Chemistry, vol. 9, no., pp. 7 77,. [] G.W.McCarty,J.B.Reeves,V.B.Reeves,R.F.Follett,andJ.M. Kimble, Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement, Soil Science Society of America Journal, vol., no., pp.,. [] K. D. Shepherd and M. G. Walsh, Development of reflectance spectral libraries for characterization of soil properties, Soil Science Society of America Journal, vol., no., pp ,. [] R.A.V.Rossel,D.J.J.Walvoort,A.B.McBratney,L.J.Janik, and J. O. Skjemstad, Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties, Geoderma, vol., no. -, pp. 9 7,. [] G. M. Vasques, S. Grunwald, and J. O. Sickman, Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra, Geoderma, vol., no. -, pp., 8. []G.M.Vasques,S.Grunwald,andJ.O.Sickman, Modeling of soil organic carbon fractions using visible near-lnfrared spectroscopy, Soil Science Society of America Journal, vol. 7, no., pp. 7 8, 9. [7] R. A. V. Rossel and T. Behrens, Using data mining to model and interpret soil diffuse reflectance spectra, Geoderma, vol. 8, no. -, pp.,. [8] M. L. McDowell, G. L. Bruland, J. L. Deenik, S. Grunwald, and N. M. Knox, Soil total carbon analysis in Hawaiian with visible, near-infrared and mid-infrared diffuse reflectance spectroscopy, Geoderma, vol. 89-9, pp.,. [9] K. Paustian, O. Andrén, H. H. Janzen et al., Agricultural as a sink to mitigate CO emissions, Soil Use and Management, vol., no., pp., 997. [] H. Tiessen, E. Cuevas, and P. Chacon, The role of soil organic matter in sustaining soil fertility, Nature, vol. 7, no., pp , 99. []E.T.CraswellandR.D.B.Lefroy, Theroleandfunction of organic matter in tropical, Nutrient Cycling in Agroecosystems, vol., no. -, pp. 7 8,. [] R. Lal, Soil carbon sequestration impacts on global climate change and food security, Science, vol., no. 77, pp. 7,. [] D. J. Brown, R. S. Bricklemyer, and P. R. Miller, requirements for diffusereflectance soilcharacterization models with a case study of VNIR soil C prediction in Montana, Geoderma, vol. 9, no. -, pp. 7,. [] A. M. Mouazen, B. Kuang, J. De Baerdemaeker, and H. Ramon, Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy, Geoderma, vol. 8, no. -, pp.,. [] D. V. Sarkhot, S. Grunwald, Y. Ge, and C. L. S. Morgan, Comparison and detection of total and available soil carbon fractions using visible/near infrared diffuse reflectance spectroscopy, Geoderma, vol., no. -, pp.,. [] B. E. Madari, J. B. Reeves, M. R. Coelho et al., Mid- and near-infrared spectroscopic determination of carbon in a diverse of from the Brazilian national soil collection, Spectroscopy Letters, vol. 8, no., pp. 7 7,. [7] J. Cierniewski, C. Kaźmierowski, K. Kuśnierek et al., Unsupervised clustering of soil spectral curves to obtain their stronger correlation with soil properties, in Proceedings of the nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, (WHISPERS ), Reykjavik, Iceland, June. [8] G. M. Vasques, S. Grunwald, and W. G. Harris, Spectroscopic models of soil organic carbon in Florida, USA, Journal of Environmental Quality, vol. 9, no., pp. 9 9,. [9] AOAC International, OfficialMethodsofAnalysisofAOAC International, AOAC International, Arlington, Va, USA, th edition, 997. [] P. C. Williams, Variables affecting near-infrared reflectance spectroscopic analysis, in Near-Infrared Technology in the Agricultural and Food Industries, P. Williams and K. Norris, Eds., pp. 7, American Association of Cereal Chemists, St. Paul, Minn, USA, 987. [] V. Bellon-Maurel, E. Fernandez-Ahumada, B. Palagos, J. M. Roger, and A. McBratney, Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy, Trends in Analytical Chemistry, vol. 9, no. 9, pp. 7 8,. [] W. R. B. IUSS Working Group, World reference base for soil resources, World Soil Resources report no., FAO, Rome, Italy,.