- Email: [email protected]
Ratio of Clay Spectroscopic Indices and its approach on soil morphometry
Geoderma 357 (2020) 113963
Contents lists available at ScienceDirect
Geoderma journal homepage: www.elsevier.com/locate/geoderma
Ratio of Clay Spectroscopic Indices and its approach on soil morphometry ⁎
T
Arnaldo B. Souza, José A.M. Demattê , Fellipe A.O. Mello, Diego F.U. Salazar, Wanderson S. Mendes, José L. Safanelli Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Pádua Dias Av., 11, Piracicaba, Postal Box 09, São Paulo 13416-900, Brazil
A R T I C LE I N FO
A B S T R A C T
Handling Editor: Alex McBratney
Textural differentiation between soil horizons occurs due to the soil formation processes and is one of the main characteristics used for soil classification and erosion risk analysis. The aim of this work was to develop a Ratio of Clay Spectroscopic Index (RCSI) and establish a degree of granulometric differentiation between soil horizons using VIS-NIR-SWIR (350–2500 nm) and MIR (2500–25,000 nm) data. We described morphologically and collected 150 soil profile samples of five representative soils from the southeastern and central-western Brazil. Three spectral models (VIS-NIR-SWIR, MIR and VIS-NIR-SWIR-MIR) were classified in terms of analytical organic matter contents from A and B horizons. We constructed 15 RCSI and considered reflectance difference for specific bands or band combinations, which were selected based on the qualitative and quantitative spectral signatures assessment. The soil horizons were differentiated through their spectral regions associated to iron oxides, clay minerals and quartz. The use of RCSI-15 resulted in a R2 of 0.79 and a RPD of 2.21. The indices reduce the dependence on advanced statistical methods and support the development of optical equipment to work with specific spectral bands.
Keywords: Pedology Diagnostic attribute Soil spectroscopy Textural differentiation
1. Introduction The “Soil Security” concept brings light to soil preservation and its importance in the ecological services that the soil performs (Koch et al., 2013). Inadequate management affects soil drainage, water storage and erodibility. Notably, soil losses by erosion are the major challenges. This process costs between US$ 250 million to US$ 45 billion a year (Pimentel et al., 1995; Montanarella, 2007). This issue is even more critical on soils with high increment of clay on subsurface horizons. That is why this attribute is considered on Soil Classifications Systems worldwide (Brazilian System of Soil Science, SiBCS - Embrapa, 2013; Soil Taxonomy - Soil Survey Staff, 2014; World Reference Database Iuss Working Group WRB, 2015). Ratio of Clay is a mathematic index that informs about clay translocation on soil profiles. This process is one of the most important diagnostic attribute related to soil genesis and stability. Possibly, the identification of the first soil formation process was based on textural differentiation (Glinka, 1914). This diagnostic attribute is a result of particle accumulation within the soil profile and is often associated with the illuviation of clay. Textural differentiation between soil horizons can even be estimated by expert pedologists or assessed by traditional methods. However, traditional methods for soil analyses lacks
providing enough spatial and temporal information to better understand and manage the soil. We need portable and faster ways to better characterize soil profiles and reduce the risks of soil losses. Spectroscopy has been used for many applications in soil science. Sensors obtain information on molecular and atomic levels by nondestructive, rapid, multi-informational and environmentally friendly methods (Dufréchou et al., 2015). This information is mainly affected by particle size distribution, mineralogy and organic matter content (Viscarra-Rossel et al., 2006). O'Rourke and Holden (2011) reported the possibility of analyzing about 720 soil samples per day using hyperspectral sensors, thus, decreasing costs by 90% compared to the conventional method. Paz-Kagan et al. (2014) highlighted the use of reliable soil spectral indices on environmental monitoring. Dufréchou et al. (2015) highlighted spectroscopy as a complementary and alternative analysis to traditional techniques to characterize soil attributes. Demattê (2002) presented pioneer observations on the correlation between horizon spectra and Ratio of Clay. To the best of our knowledge, there are no reports on a statistical spectroscopic method that determines this parameter faster and at a lower cost. As this is a one-off assessment within the profile, such technique is placed within the newly named Digital Soil Morphometrics research field (Hartemink and Minasny, 2016) and deserves a better comprehension. With the focus on
⁎
Corresponding author. E-mail addresses: [email protected] (J.A.M. Demattê), [email protected] (F.A.O. Mello), [email protected] (D.F.U. Salazar), [email protected] (W.S. Mendes), [email protected] (J.L. Safanelli). https://doi.org/10.1016/j.geoderma.2019.113963 Received 25 April 2019; Received in revised form 27 August 2019; Accepted 5 September 2019 0016-7061/ © 2019 Elsevier B.V. All rights reserved.
Geoderma 357 (2020) 113963
A.B. Souza, et al.
spectral characterization of soils, Demattê et al. (2014) presented a system to analyze spectra by their morphological signatures, considering the comparative analyses between horizons of the same profile. Research on soil profiles allows to understand soil particles, water and nutrient dynamics, highlighting limitations and potentials, and providing conditions for accurate decisions about soil use. Based on the high qualitative (Demattê et al., 2014) and quantitative (Terra et al., 2015) relationship between the reflected energy and soil attributes, such as mineralogy and particle size distribution, it is expected that the spectral analysis between horizons of a soil profile allows to identify the degree of soil textural differentiation using spectral indices. Thus, this study aimed to develop the Ratio of Clay Spectroscopic Indices (RCSI) to improve the identification of soil textural differentiation degree between horizons using VIS-NIR-SWIR (350–2500 nm) and MIR (2500–25,000 nm) spectroscopy.
Table 1 Ratio of Clay classification criteria.
2. Material and methods
2.3. Ratio of Clay classification
2.1. Study site
Surface horizons were classified in the following three textural groups (Embrapa, 2013): “Sandy” clay content ≤150 gkg−1; “Medium” clay content between 150 and 350 gkg−1; and “Clayey” clay content ≥350 gkg−1. The Ratio of Clay (RC) values were obtained through the ratio between clay contents in B (Clay_BH) and A (Clay_AH) horizons. The B/A and A/B transitional horizons were also used to quantify the RC. RC values (RC = Clay_BH/Clay_AH) were allocated in three classes considering Clay_AH (Table 1), according to Embrapa (2013). The method to determine the RC is similar to the one used on “World Reference Base for Soil Classification” (Iuss Working Group WRB, 2015) where this criterion is used to distinguish argic horizons. The VIS-NIR-SWIR and MIR spectral data from each horizon were processed to obtain the reflectance average of A (AmR) and B (BmR) horizons, as calculated for Clay-AH and Clay-BH. Afterward, reflectance difference between the horizons (AmR - BmR) was calculated to enhance the textural differentiation. This new approach gives a more logical view of soils as natural bodies once the spectral relationship between horizons are considered.
Textural class of A horizona
Ratio of Clay class
Criteriab
Sandy
Not Significant – N Significant – S Highly significant – A Not Significant – N Significant – S Highly significant – A Not Significant – N Significant – S Highly significant – A
RCc ≤ 1.8 1.8 < RC ≤ 2.3 RC > 2.3 RC ≤ 1.7 1.7 < RC ≤ 2.2 RC > 2.2 RC ≤ 1.5 1.5 < RC ≤ 1.8 RC > 1.8
Medium
Clayey
“Sandy”, clay content ≤150 g kg−1; “Medium”, clay content between 150 and 350 g kg−1; and “Clayey”, clay content ≥350 g kg−1. b Limits suggested by EMBRAPA (2013) considering the textural class. c Ratio of Clay (RC). a
The study area comprises nine municipalities among the states of São Paulo, Mato Grosso do Sul and Goiás in Brazil. The sites are mainly formed from basaltic spills and recent sandstone deposits giving a flat and undulate relief (Terra et al., 2015). Köppen classification set the regions in tropical zone with dry winters (Aw - Goianésia, Maracajú, Andradina, Mirandópolis, Valparaíso and Araraquara) and humid subtropical zones with or without dry seasons (Cwa - Piracicaba and São Carlos, Cfa - Ipaussu). To collaborate with this work, we adapted the spectral library created by Bellinaso et al. (2010) using 150 soil profile samples collected in Lixisols, Ferralsols, Cambisols, Nitosols, and Planosols (IUSS Working Group WRB, 2015).
2.2. Soil sampling and data collection It was collected core samples from each soil horizon in the 150 trenches. The Samples were dried, sieved (2 mm) and submitted to particle size analysis according to Pipet Method of sedimentation and Walkley-Black method for Soil Organic Carbon (SOC) using acid wet oxidation (Camargo et al., 2009). The OM content was used on the model calibration and validation although this work aims to create indices to predict RC from spectra. This was done to prove that analytical OM is not necessary to predict RC through spectra once it contains this information. The spectral VIS-NIR-SWIR reflectance (350–2500 nm) was obtained by a FieldSpec Pro sensor (Analytical Spectral Devices, Boulder, Colo.). The equipment has 3 nm spectral resolution from 350 to 1000 nm and 10 nm in 1000 to 2500 nm range, which was automatically resampled to the 1 nm interval by the equipment system. The dry-sifted samples were placed on petri dishes and were planed with a spatula to reduce surface roughness. The optical fiber cable was positioned at 8 cm from the sample surface, which was lit by two halogen lamps (50 W) 35 cm far from the sample platform with 30° zenith angle. A Spectralon white plate was used to obtain the maximum reflectance reference every 20 min. The spectral curves comprised an average of three replicates acquired with rotation (90°, 180° and 270°), each with 100 readings to reduce shading and the roughness effects. The MIR spectra data were acquired by a Nicolet 6700 Fourier Transform Infrared (FT-IR) sensor (Termo Fisher Scientific Inc., Waltham, MA), equipped with accessories to diffuse reflectance acquisition (Smart Diffuse Reflectance) and HeNe laser. The samples were grounded at approximately 1 cm3 and placed in a container for data collection. The spectra were registered with 1.2 nm resolution and 64 readings per second, being calibrated with a gold plate.
2.4. Ratio of Clay Spectroscopic Index (RCSI) Simple spectral indices were calculated between spectral bands to predict the Ratio of Clay between soil horizons (Table 2). The spectral bands were selected from different regions of the electromagnetic spectrum, aiming to cover the particularities of each spectral region. Subsequently, all indices were submitted to simple linear regression and were evaluated by external validation with the following parameters: Coefficient of determination – R2; Root Mean Square Error – RMSE; and Ratio of Performance to Deviation - RPD. The models were classified according to the RPDs obtained in predictions with spectral data as “precise” (RPD ≥ 2.0), “acceptable” (1.6 ≤ RPD < 2.0) or “weak” (RPD < 1.6) (Chang et al., 2001; Dunn et al., 2002). 3. Results and discussion 3.1. Spectral patterns of soil horizons and Ratio of Clay The textural differentiation degree between horizons is clearly observed in the graphical representation of surface and subsurface reflectance difference for each band (Fig. 1). This is expressed by spectral curves differences, with maximum expression at 2495 nm. Spectral differences are less pronounced between clayey to very clayey soils and more significant for sandy to clayey textures profiles (Fig. 1). Naturally, soils with sandier surface horizons are more easily distinguishable from the underlying horizons because the absorbing features of clay minerals are expressive. This effect is greatly reduced in soils with clayey or very clayey texture, reducing the expression of textural differentiation 2
Geoderma 357 (2020) 113963
A.B. Souza, et al.
Table 2 Spectral indices tested for textural relationship according to its spectral regions. RCSI
VIS-NIR-SWIR
1 2 3 4 5
Dri i =350Σ Dr366 − (Dr2200 + Dr2495) Dr366 + Dr2200 + Dr2495 Dr366 + Dr2495 Dr2495
a
2500
a
RCSI
MIR
6 7 8 9 10
Dri i =4000Σ Dr3642 − (Dr933 + Dr656) Dr3642 + Dr933 + Dr656 Dr3642 + Dr656 Dr3642 400
RCSI
VIS-NIR-SWIR-MIR
11 12 13 14 15
25000 Dri i=350Σ Dr366 − (Dr3642 + Dr656) Dr366 + Dr3642 + Dr656 Dr366 + Dr3642 Dr2495 + Dr3642
Dri: Reflectance difference (Dr) between horizons (B and A) for band i. Underlined bands are in cm−1 unit, and the others are in nm unit.
positive and negative annulation of reflectance difference, causing a loss of variability. Especially in the spectral region of VIS-NIR-SWIR and for soils without Ratio of Clay, the spectral patterns of subsurface horizons tend to have a higher reflectance in the region affected by iron oxides, which confuses the index with module. The other spectral indices presented encouraging results. The RCSI2 to RCSI5, composed by the spectral region VIS-NIR-SWIR, resulted in R2 from 0.61 to 0.70, RMSE from 0.24 to 0.28 and RPD from 1.58 to 1.84 (Fig. 2). The RCSI4 was the best index and was classified as accurate, which considers the difference in reflectance between horizons on bands 366 and 2495 nm. These bands have great influence of iron oxides, OM and clay, which are factors that define the differences between horizons in the spectral analysis. Regression with RCSI generated from the MIR data and a combination of VIS-NIR-SWIR-MIR (Figs. 3 and 4), except for RCSI7, always resulted in accurate models with R2 from 0.76 to 0.79, RMSE from 0.20 to 0.22 and RPD from 2.01 to 2.21. The RCSI9 and RCSI15 indices were calculated using spectral regions
degree. The degree of textural differentiation is expressed by the differences of spectral curves in MIR and VIS-NIR-SWIR spectra. Notably, stronger differences are related to regions with clay mineral absorption bands. Absorption bands associated with quartz in the MIR region between 2050 and 1100 cm−1 show an opposite relationship to that observed for the clay content, which have a higher reflectance in subsurface. In the MIR region, there are two main morphological features (Fig. 1). A peak at 1200 and 400 cm−1 is stronger for sandy and weaker for clayey samples. In addition, we observed higher intensity between 4000 and 1900 cm−1 for sandier samples. 3.2. Spectral indices for Ratio of Clay The reflectance differences modules sum (RCSI1, RCSI6, and RCSI11) is not an appropriate approach to study the Ratio of Clay degree of soil horizons (Figs. 2–4). This fact is expected because of the
Fig. 1. Average reflectance of surface (RmA) and subsurface (RmB) horizons, from soil profiles with not significant, significant and highly significant Ratio of Clay between soil horizons. 3
Geoderma 357 (2020) 113963
A.B. Souza, et al.
Fig. 2. Multiple linear regression performance for Ratio of Clay estimation, using spectral indices of VIS-NIR-SWIR, from 150 soil profiles of southeastern and centralwestern Brazil.
4. Conclusions
related to the clay particles. The use of spectral indices in the study of soil attributes has always been the subject in literature. The Normalized Iron Oxide Difference Index (NIODI), proposed by Viscarra-Rossel et al. (2010), measures the relative content between hematite and goethite, which were used to generate color maps of soils in Australia, with less impediment regarding the use of financial resources and computation time. The use of spectral indices with knowledge of the factors that affect them can simplify the interpretation and increase the applicability of this kind of data. In the present case, spectral indices were successfully developed and applied to estimate Ratio of Clay between soil horizons, which can aid in soil erosion investigations and improve soil security. In addition, the development of spectral indices related to Ratio of Clay can reduce dependence on advanced statistical methods and support the development of optical equipment to operates in specific spectral regions, simplifying prediction systems and reducing costs of implementation.
Textural differentiation between soil horizons could be estimated with excellent accuracy by using CRSI. We proposed these indices to reduce dependence on complex statistical and traditional wet chemical analysis methods for estimating ratio of clay. The modelling was performed by using reflectance difference between horizons (BmR - AmR) to maximize and get focus on the contrasts along spectral curves. This approach can support the development of optical equipment to operate in specific spectral regions in the field and improve the understanding of soil variability more efficiently. In terms of practicality, the VIS-NIRSWIR spectral region is still the best one regarding data acquisition. This region presented significant results to study the textural differentiation degree between soil horizons. Despite of this, the MIR spectral region had the best performances. This fact is due to the specific spectral features occurrence. Merging both spectral sources showed no
Fig. 3. Multiple linear regression performance for Ratio of Clay estimation, using spectral indices of MIR, from 150 soil profiles of southeastern and central-western Brazil. 4
Geoderma 357 (2020) 113963
A.B. Souza, et al.
Fig. 4. Multiple linear regression performance for Ratio of Clay estimation, using spectral indices of VIS-NIR-SWIR-MIR, from 150 soil profiles of southeastern and central-western Brazil.
significant advantage. The spectral-morphological analysis was required to understand soil variability within the profile and formulate the hypothesis for model calibrations. Thus, we strongly emphasize the continuity of morphological spectral interpretations of soils.
of south-eastern Australia. Aust. J. Exp. Agric., Melbourne 42, 607–614. https://doi. org/10.1071/EA01172. EMPRESA BRASILEIRA DE PESQUISA AGROPECUÁRIA-EMBRAPA, 2013. Sistema brasileiro de classificação de solos, 3 ed. (Brasília, 353 pp.). Glinka, K.D., 1914. Die Typen der Bodenbildung, irhe Klassification und Geographische Verbreitung. Gebruder Borntraeger, Berlin (365 pp.). Hartemink, A.E., Minasny, B. (Eds.), 2016. Digital Soil Morphometrics. 442 Springer Dordrecht978-3-319-28295-4, . Iuss Working Group WRB, 2015. World Reference Base for Soil Resources 2014, update 2015 International soil classification system for naming soils and creating legends for soil maps. In: W. Soil Res. Rep. No. 106. FAO, Rome 203p. Koch, A., Mcbratney, A., Adams, M., Field, D., Hill, R., Crawford, J., Minasny, B., Lal, R., Abbott, L., O’donnell, A., Angers, D., Baldock, J., Barbier, E., Binkley, D., Parton, W., Wall, D.H., Bird, M., Bouma, J., Chenu, C., Flora, C.B., Goulding, K., Grunwald, S., Hempel, J., Jastrow, J., Lehmann, J., Lorenz, K., Morgan, C.L., Rice, C.W., Whitehead, D., Young, I., Zimmermann, M., 2013. Soil security: solving the global soil crisis. Glob. Pol., Durham 4 (4), 434–441. https://doi.org/10.1111/1758-5899. 12096. Montanarella, L., 2007. Trends in land degradation in Europe. In: Sivakumar, M.V.K., Ndiang’ui, N. (Eds.), Climate and Land Degradation. Springer, New York, pp. 83–104. O’Rourke, S.M., Holden, N.M., 2011. Optical sensing and chemometric analysis of soil organic carbon: a cost effective alternative to conventional laboratory methods? Soil Use Manag., New York 27 (2), 143–155. https://doi.org/10.1111/j.1475-2743.2011. 00337.x. Paz-Kagan, T., Shachak, M., Zaady, E., Karnieli, A., 2014. A spectral soil quality index (SSQI) for characterizing soil function in areas of changed land use. Geoderma 230/ 231, 171–184. https://doi.org/10.1016/j.geoderma.2014.04.003. Amsterdam. Pimentel, D., Harvey, C., Resosudarmo, P., Sinclair, K., Kurz, D., Mcnair, M., Crist, S., Sphpritz, L., Fitton, L., Saffouri, R., Blair, R., 1995. Environmental and economic costs of soil erosion and conservation benefits. Science 267, 1117–1123. https://doi. org/10.1126/science.267.5201.1117. Soil Survey Staff, 2014. Keys to Soil Taxonomy, 14th ed. (Washington. 372 pp.). Terra, F.S., Demattê, J.A.M., Viscarra Rossel, R.A., 2015. Spectral libraries for quantitative analyses of tropical Brazilian soils: comparing vis–NIR and mid-IR reflectance data. Geoderma, Amsterdam 255–256, 81–93. https://doi.org/10.1016/j.geoderma. 2015.04.017. Viscarra-Rossel, R.A., Walvoort, D.J.J., Mcbratney, A.B., Janik, L.J., Skjemstad, J.O., 2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, Amsterdam 131 (1/2), 59–75. Viscarra-Rossel, R.A., Bui, E.N., De Caritat, P., Mckenzie, N.J., 2010. Mapping iron oxides and the color of Australian soil using visible–near-infrared reflectance spectra. J. Geoph. Res., Washington 115 (F4), F04031. https://doi.org/10.1029/ 2009JF001645.
Acknowledgements We are grateful to Geotechnologies in Soil Science Group (GEOSS/ ESALQ-USP; http://esalqgeocis.wixsite.com/english) for team support. We also thank the anonymous reviewers to improve this paper. The MSc scholarship of the first author was provided by the National Council for Scientific and Technological Development (CNPq). Funding was provided by São Paulo Research Foundation (FAPESP, grant numbers 2014/22262-0, 2016/26124-6, and 2016/01597-9). References Bellinaso, H., Demattê, J.A.M., Romeiro, S.A., 2010. Soil spectral library and its use in soil classification. Rev. Bras. Ciên. Solo, Viçosa 34, 861–870. https://doi.org/10.1590/ S0100-06832010000300027. Camargo, O.A., Moniz, A.C., Jorge, J.A., Valadares, J.M., 2009. Métodos de análise química, mineralógica e física de solos do IAC. Inst. Agron. Camp, Campinas (77 p. (IAC. Boletim Técnico, 106)). Chang, C.W., Laird, D.A., Mausbach, M.J., Hurburgh, C.R., 2001. Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties. Soil Sci. Soc. Am. J., Madison 65, 480–490. https://doi.org/10.2136/sssaj2001. 652480x. Demattê, J.A.M., 2002. Characterization and discrimination of soils by their reflected electromagnetic energy. Braz. J. Agric. Res., Brasília 37, 1445–1458. https://doi.org/ 10.1590/S0100-204X2002001000013. Demattê, J.A.M., Bellinaso, H., Romero, D.J., Fongaro, C.T., 2014. Morphological Interpretation of Reflectance Spectrum (MIRS) using libraries looking towards soil classification. Sci. Agric., Piracicaba 71 (6), 509–520. https://doi.org/10.1590/01039016-2013-0365. Dufréchou, G., Grandjean, G., Bourguignon, A., 2015. Geometrical Analysis of Laboratory Soil Spectra in the Short-wave Infrared Domain: Clay Composition and Estimation of the Swelling Potential. 243/244. Geoderma, Amsterdam, pp. 92–107. https://doi. org/10.1016/j.geoderma.2014.12.014. Dunn, B.W., Beecher, H.G., Batten, G.D., Ciavarella, S., 2002. The potential of near infrared reflectance spectroscopy for soil analysis: a case study from the Riverine Plain
5
Contents lists available at ScienceDirect
Geoderma journal homepage: www.elsevier.com/locate/geoderma
Ratio of Clay Spectroscopic Indices and its approach on soil morphometry ⁎
T
Arnaldo B. Souza, José A.M. Demattê , Fellipe A.O. Mello, Diego F.U. Salazar, Wanderson S. Mendes, José L. Safanelli Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Pádua Dias Av., 11, Piracicaba, Postal Box 09, São Paulo 13416-900, Brazil
A R T I C LE I N FO
A B S T R A C T
Handling Editor: Alex McBratney
Textural differentiation between soil horizons occurs due to the soil formation processes and is one of the main characteristics used for soil classification and erosion risk analysis. The aim of this work was to develop a Ratio of Clay Spectroscopic Index (RCSI) and establish a degree of granulometric differentiation between soil horizons using VIS-NIR-SWIR (350–2500 nm) and MIR (2500–25,000 nm) data. We described morphologically and collected 150 soil profile samples of five representative soils from the southeastern and central-western Brazil. Three spectral models (VIS-NIR-SWIR, MIR and VIS-NIR-SWIR-MIR) were classified in terms of analytical organic matter contents from A and B horizons. We constructed 15 RCSI and considered reflectance difference for specific bands or band combinations, which were selected based on the qualitative and quantitative spectral signatures assessment. The soil horizons were differentiated through their spectral regions associated to iron oxides, clay minerals and quartz. The use of RCSI-15 resulted in a R2 of 0.79 and a RPD of 2.21. The indices reduce the dependence on advanced statistical methods and support the development of optical equipment to work with specific spectral bands.
Keywords: Pedology Diagnostic attribute Soil spectroscopy Textural differentiation
1. Introduction The “Soil Security” concept brings light to soil preservation and its importance in the ecological services that the soil performs (Koch et al., 2013). Inadequate management affects soil drainage, water storage and erodibility. Notably, soil losses by erosion are the major challenges. This process costs between US$ 250 million to US$ 45 billion a year (Pimentel et al., 1995; Montanarella, 2007). This issue is even more critical on soils with high increment of clay on subsurface horizons. That is why this attribute is considered on Soil Classifications Systems worldwide (Brazilian System of Soil Science, SiBCS - Embrapa, 2013; Soil Taxonomy - Soil Survey Staff, 2014; World Reference Database Iuss Working Group WRB, 2015). Ratio of Clay is a mathematic index that informs about clay translocation on soil profiles. This process is one of the most important diagnostic attribute related to soil genesis and stability. Possibly, the identification of the first soil formation process was based on textural differentiation (Glinka, 1914). This diagnostic attribute is a result of particle accumulation within the soil profile and is often associated with the illuviation of clay. Textural differentiation between soil horizons can even be estimated by expert pedologists or assessed by traditional methods. However, traditional methods for soil analyses lacks
providing enough spatial and temporal information to better understand and manage the soil. We need portable and faster ways to better characterize soil profiles and reduce the risks of soil losses. Spectroscopy has been used for many applications in soil science. Sensors obtain information on molecular and atomic levels by nondestructive, rapid, multi-informational and environmentally friendly methods (Dufréchou et al., 2015). This information is mainly affected by particle size distribution, mineralogy and organic matter content (Viscarra-Rossel et al., 2006). O'Rourke and Holden (2011) reported the possibility of analyzing about 720 soil samples per day using hyperspectral sensors, thus, decreasing costs by 90% compared to the conventional method. Paz-Kagan et al. (2014) highlighted the use of reliable soil spectral indices on environmental monitoring. Dufréchou et al. (2015) highlighted spectroscopy as a complementary and alternative analysis to traditional techniques to characterize soil attributes. Demattê (2002) presented pioneer observations on the correlation between horizon spectra and Ratio of Clay. To the best of our knowledge, there are no reports on a statistical spectroscopic method that determines this parameter faster and at a lower cost. As this is a one-off assessment within the profile, such technique is placed within the newly named Digital Soil Morphometrics research field (Hartemink and Minasny, 2016) and deserves a better comprehension. With the focus on
⁎
Corresponding author. E-mail addresses: [email protected] (J.A.M. Demattê), [email protected] (F.A.O. Mello), [email protected] (D.F.U. Salazar), [email protected] (W.S. Mendes), [email protected] (J.L. Safanelli). https://doi.org/10.1016/j.geoderma.2019.113963 Received 25 April 2019; Received in revised form 27 August 2019; Accepted 5 September 2019 0016-7061/ © 2019 Elsevier B.V. All rights reserved.
Geoderma 357 (2020) 113963
A.B. Souza, et al.
spectral characterization of soils, Demattê et al. (2014) presented a system to analyze spectra by their morphological signatures, considering the comparative analyses between horizons of the same profile. Research on soil profiles allows to understand soil particles, water and nutrient dynamics, highlighting limitations and potentials, and providing conditions for accurate decisions about soil use. Based on the high qualitative (Demattê et al., 2014) and quantitative (Terra et al., 2015) relationship between the reflected energy and soil attributes, such as mineralogy and particle size distribution, it is expected that the spectral analysis between horizons of a soil profile allows to identify the degree of soil textural differentiation using spectral indices. Thus, this study aimed to develop the Ratio of Clay Spectroscopic Indices (RCSI) to improve the identification of soil textural differentiation degree between horizons using VIS-NIR-SWIR (350–2500 nm) and MIR (2500–25,000 nm) spectroscopy.
Table 1 Ratio of Clay classification criteria.
2. Material and methods
2.3. Ratio of Clay classification
2.1. Study site
Surface horizons were classified in the following three textural groups (Embrapa, 2013): “Sandy” clay content ≤150 gkg−1; “Medium” clay content between 150 and 350 gkg−1; and “Clayey” clay content ≥350 gkg−1. The Ratio of Clay (RC) values were obtained through the ratio between clay contents in B (Clay_BH) and A (Clay_AH) horizons. The B/A and A/B transitional horizons were also used to quantify the RC. RC values (RC = Clay_BH/Clay_AH) were allocated in three classes considering Clay_AH (Table 1), according to Embrapa (2013). The method to determine the RC is similar to the one used on “World Reference Base for Soil Classification” (Iuss Working Group WRB, 2015) where this criterion is used to distinguish argic horizons. The VIS-NIR-SWIR and MIR spectral data from each horizon were processed to obtain the reflectance average of A (AmR) and B (BmR) horizons, as calculated for Clay-AH and Clay-BH. Afterward, reflectance difference between the horizons (AmR - BmR) was calculated to enhance the textural differentiation. This new approach gives a more logical view of soils as natural bodies once the spectral relationship between horizons are considered.
Textural class of A horizona
Ratio of Clay class
Criteriab
Sandy
Not Significant – N Significant – S Highly significant – A Not Significant – N Significant – S Highly significant – A Not Significant – N Significant – S Highly significant – A
RCc ≤ 1.8 1.8 < RC ≤ 2.3 RC > 2.3 RC ≤ 1.7 1.7 < RC ≤ 2.2 RC > 2.2 RC ≤ 1.5 1.5 < RC ≤ 1.8 RC > 1.8
Medium
Clayey
“Sandy”, clay content ≤150 g kg−1; “Medium”, clay content between 150 and 350 g kg−1; and “Clayey”, clay content ≥350 g kg−1. b Limits suggested by EMBRAPA (2013) considering the textural class. c Ratio of Clay (RC). a
The study area comprises nine municipalities among the states of São Paulo, Mato Grosso do Sul and Goiás in Brazil. The sites are mainly formed from basaltic spills and recent sandstone deposits giving a flat and undulate relief (Terra et al., 2015). Köppen classification set the regions in tropical zone with dry winters (Aw - Goianésia, Maracajú, Andradina, Mirandópolis, Valparaíso and Araraquara) and humid subtropical zones with or without dry seasons (Cwa - Piracicaba and São Carlos, Cfa - Ipaussu). To collaborate with this work, we adapted the spectral library created by Bellinaso et al. (2010) using 150 soil profile samples collected in Lixisols, Ferralsols, Cambisols, Nitosols, and Planosols (IUSS Working Group WRB, 2015).
2.2. Soil sampling and data collection It was collected core samples from each soil horizon in the 150 trenches. The Samples were dried, sieved (2 mm) and submitted to particle size analysis according to Pipet Method of sedimentation and Walkley-Black method for Soil Organic Carbon (SOC) using acid wet oxidation (Camargo et al., 2009). The OM content was used on the model calibration and validation although this work aims to create indices to predict RC from spectra. This was done to prove that analytical OM is not necessary to predict RC through spectra once it contains this information. The spectral VIS-NIR-SWIR reflectance (350–2500 nm) was obtained by a FieldSpec Pro sensor (Analytical Spectral Devices, Boulder, Colo.). The equipment has 3 nm spectral resolution from 350 to 1000 nm and 10 nm in 1000 to 2500 nm range, which was automatically resampled to the 1 nm interval by the equipment system. The dry-sifted samples were placed on petri dishes and were planed with a spatula to reduce surface roughness. The optical fiber cable was positioned at 8 cm from the sample surface, which was lit by two halogen lamps (50 W) 35 cm far from the sample platform with 30° zenith angle. A Spectralon white plate was used to obtain the maximum reflectance reference every 20 min. The spectral curves comprised an average of three replicates acquired with rotation (90°, 180° and 270°), each with 100 readings to reduce shading and the roughness effects. The MIR spectra data were acquired by a Nicolet 6700 Fourier Transform Infrared (FT-IR) sensor (Termo Fisher Scientific Inc., Waltham, MA), equipped with accessories to diffuse reflectance acquisition (Smart Diffuse Reflectance) and HeNe laser. The samples were grounded at approximately 1 cm3 and placed in a container for data collection. The spectra were registered with 1.2 nm resolution and 64 readings per second, being calibrated with a gold plate.
2.4. Ratio of Clay Spectroscopic Index (RCSI) Simple spectral indices were calculated between spectral bands to predict the Ratio of Clay between soil horizons (Table 2). The spectral bands were selected from different regions of the electromagnetic spectrum, aiming to cover the particularities of each spectral region. Subsequently, all indices were submitted to simple linear regression and were evaluated by external validation with the following parameters: Coefficient of determination – R2; Root Mean Square Error – RMSE; and Ratio of Performance to Deviation - RPD. The models were classified according to the RPDs obtained in predictions with spectral data as “precise” (RPD ≥ 2.0), “acceptable” (1.6 ≤ RPD < 2.0) or “weak” (RPD < 1.6) (Chang et al., 2001; Dunn et al., 2002). 3. Results and discussion 3.1. Spectral patterns of soil horizons and Ratio of Clay The textural differentiation degree between horizons is clearly observed in the graphical representation of surface and subsurface reflectance difference for each band (Fig. 1). This is expressed by spectral curves differences, with maximum expression at 2495 nm. Spectral differences are less pronounced between clayey to very clayey soils and more significant for sandy to clayey textures profiles (Fig. 1). Naturally, soils with sandier surface horizons are more easily distinguishable from the underlying horizons because the absorbing features of clay minerals are expressive. This effect is greatly reduced in soils with clayey or very clayey texture, reducing the expression of textural differentiation 2
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Table 2 Spectral indices tested for textural relationship according to its spectral regions. RCSI
VIS-NIR-SWIR
1 2 3 4 5
Dri i =350Σ Dr366 − (Dr2200 + Dr2495) Dr366 + Dr2200 + Dr2495 Dr366 + Dr2495 Dr2495
a
2500
a
RCSI
MIR
6 7 8 9 10
Dri i =4000Σ Dr3642 − (Dr933 + Dr656) Dr3642 + Dr933 + Dr656 Dr3642 + Dr656 Dr3642 400
RCSI
VIS-NIR-SWIR-MIR
11 12 13 14 15
25000 Dri i=350Σ Dr366 − (Dr3642 + Dr656) Dr366 + Dr3642 + Dr656 Dr366 + Dr3642 Dr2495 + Dr3642
Dri: Reflectance difference (Dr) between horizons (B and A) for band i. Underlined bands are in cm−1 unit, and the others are in nm unit.
positive and negative annulation of reflectance difference, causing a loss of variability. Especially in the spectral region of VIS-NIR-SWIR and for soils without Ratio of Clay, the spectral patterns of subsurface horizons tend to have a higher reflectance in the region affected by iron oxides, which confuses the index with module. The other spectral indices presented encouraging results. The RCSI2 to RCSI5, composed by the spectral region VIS-NIR-SWIR, resulted in R2 from 0.61 to 0.70, RMSE from 0.24 to 0.28 and RPD from 1.58 to 1.84 (Fig. 2). The RCSI4 was the best index and was classified as accurate, which considers the difference in reflectance between horizons on bands 366 and 2495 nm. These bands have great influence of iron oxides, OM and clay, which are factors that define the differences between horizons in the spectral analysis. Regression with RCSI generated from the MIR data and a combination of VIS-NIR-SWIR-MIR (Figs. 3 and 4), except for RCSI7, always resulted in accurate models with R2 from 0.76 to 0.79, RMSE from 0.20 to 0.22 and RPD from 2.01 to 2.21. The RCSI9 and RCSI15 indices were calculated using spectral regions
degree. The degree of textural differentiation is expressed by the differences of spectral curves in MIR and VIS-NIR-SWIR spectra. Notably, stronger differences are related to regions with clay mineral absorption bands. Absorption bands associated with quartz in the MIR region between 2050 and 1100 cm−1 show an opposite relationship to that observed for the clay content, which have a higher reflectance in subsurface. In the MIR region, there are two main morphological features (Fig. 1). A peak at 1200 and 400 cm−1 is stronger for sandy and weaker for clayey samples. In addition, we observed higher intensity between 4000 and 1900 cm−1 for sandier samples. 3.2. Spectral indices for Ratio of Clay The reflectance differences modules sum (RCSI1, RCSI6, and RCSI11) is not an appropriate approach to study the Ratio of Clay degree of soil horizons (Figs. 2–4). This fact is expected because of the
Fig. 1. Average reflectance of surface (RmA) and subsurface (RmB) horizons, from soil profiles with not significant, significant and highly significant Ratio of Clay between soil horizons. 3
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Fig. 2. Multiple linear regression performance for Ratio of Clay estimation, using spectral indices of VIS-NIR-SWIR, from 150 soil profiles of southeastern and centralwestern Brazil.
4. Conclusions
related to the clay particles. The use of spectral indices in the study of soil attributes has always been the subject in literature. The Normalized Iron Oxide Difference Index (NIODI), proposed by Viscarra-Rossel et al. (2010), measures the relative content between hematite and goethite, which were used to generate color maps of soils in Australia, with less impediment regarding the use of financial resources and computation time. The use of spectral indices with knowledge of the factors that affect them can simplify the interpretation and increase the applicability of this kind of data. In the present case, spectral indices were successfully developed and applied to estimate Ratio of Clay between soil horizons, which can aid in soil erosion investigations and improve soil security. In addition, the development of spectral indices related to Ratio of Clay can reduce dependence on advanced statistical methods and support the development of optical equipment to operates in specific spectral regions, simplifying prediction systems and reducing costs of implementation.
Textural differentiation between soil horizons could be estimated with excellent accuracy by using CRSI. We proposed these indices to reduce dependence on complex statistical and traditional wet chemical analysis methods for estimating ratio of clay. The modelling was performed by using reflectance difference between horizons (BmR - AmR) to maximize and get focus on the contrasts along spectral curves. This approach can support the development of optical equipment to operate in specific spectral regions in the field and improve the understanding of soil variability more efficiently. In terms of practicality, the VIS-NIRSWIR spectral region is still the best one regarding data acquisition. This region presented significant results to study the textural differentiation degree between soil horizons. Despite of this, the MIR spectral region had the best performances. This fact is due to the specific spectral features occurrence. Merging both spectral sources showed no
Fig. 3. Multiple linear regression performance for Ratio of Clay estimation, using spectral indices of MIR, from 150 soil profiles of southeastern and central-western Brazil. 4
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Fig. 4. Multiple linear regression performance for Ratio of Clay estimation, using spectral indices of VIS-NIR-SWIR-MIR, from 150 soil profiles of southeastern and central-western Brazil.
significant advantage. The spectral-morphological analysis was required to understand soil variability within the profile and formulate the hypothesis for model calibrations. Thus, we strongly emphasize the continuity of morphological spectral interpretations of soils.
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Acknowledgements We are grateful to Geotechnologies in Soil Science Group (GEOSS/ ESALQ-USP; http://esalqgeocis.wixsite.com/english) for team support. We also thank the anonymous reviewers to improve this paper. The MSc scholarship of the first author was provided by the National Council for Scientific and Technological Development (CNPq). Funding was provided by São Paulo Research Foundation (FAPESP, grant numbers 2014/22262-0, 2016/26124-6, and 2016/01597-9). References Bellinaso, H., Demattê, J.A.M., Romeiro, S.A., 2010. Soil spectral library and its use in soil classification. Rev. Bras. Ciên. Solo, Viçosa 34, 861–870. https://doi.org/10.1590/ S0100-06832010000300027. Camargo, O.A., Moniz, A.C., Jorge, J.A., Valadares, J.M., 2009. Métodos de análise química, mineralógica e física de solos do IAC. Inst. Agron. Camp, Campinas (77 p. (IAC. Boletim Técnico, 106)). Chang, C.W., Laird, D.A., Mausbach, M.J., Hurburgh, C.R., 2001. Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties. Soil Sci. Soc. Am. J., Madison 65, 480–490. https://doi.org/10.2136/sssaj2001. 652480x. Demattê, J.A.M., 2002. Characterization and discrimination of soils by their reflected electromagnetic energy. Braz. J. Agric. Res., Brasília 37, 1445–1458. https://doi.org/ 10.1590/S0100-204X2002001000013. Demattê, J.A.M., Bellinaso, H., Romero, D.J., Fongaro, C.T., 2014. Morphological Interpretation of Reflectance Spectrum (MIRS) using libraries looking towards soil classification. Sci. Agric., Piracicaba 71 (6), 509–520. https://doi.org/10.1590/01039016-2013-0365. Dufréchou, G., Grandjean, G., Bourguignon, A., 2015. Geometrical Analysis of Laboratory Soil Spectra in the Short-wave Infrared Domain: Clay Composition and Estimation of the Swelling Potential. 243/244. Geoderma, Amsterdam, pp. 92–107. https://doi. org/10.1016/j.geoderma.2014.12.014. Dunn, B.W., Beecher, H.G., Batten, G.D., Ciavarella, S., 2002. The potential of near infrared reflectance spectroscopy for soil analysis: a case study from the Riverine Plain
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