نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری، دانشکده کشاورزی، دانشگاه صنعتی اصفهان، اصفهان، ایران
2 استاد، دانشکده کشاورزی، دانشگاه صنعتی اصفهان، اصفهان، ایران
3 استاد، گروه علوم خاک، دانشگاه ESALQ، سائوپائولو- برزیل
چکیده
در سالهای اخیر طیفسنجی بهعنوان یکی از تکنیکهای ممکن در جایگزینی روشهای مرسوم آزمایشگاهی در علوم خاک معرفی شده است. هدف از پژوهش حاضر، ارزیابی قابلیت این تکنیک در دو محدودهی مرئی-مادون قرمز نزدیک (Vis-NIR) و مادون قرمز میانی (mid-IR) در پیشبینی کربناتهای خاک در منطقه جونقان استان چهارمحال و بختیاری میباشد. به این منظور 272 نمونهی خاک سطحی از عمق 10-0 سانتیمتری جمعآوری و میزان کربنات هر یک با روش تیتراسیون برگشتی تعیین شد. اطلاعات طیفی خاکها در گستره Vis-NIRبا استفاده از اسپکترورادیومتر زمینیFieldSpec 3, ASD-Analytical Spectral Devices, Boulder Colorado, USA))، در محدودهی 2500-350 نانومتر با تفکیک طیفی 1 نانومتر و در محدوده mid-IR با اسپکترورادیومتر تبدیل فوریه مادون قرمز FT-IR (Thermo Fisher Scientific Inc., Waltham, MA)، در گستره cm-14000-400 (25000-2500 نانومتر) با تفکیک طیفی 2/1 نانومتر استخراج شد. سپس انواع مختلف روشهای پیشپردازش بر اطلاعات طیفی، اعمال شده، دادهها به دو گروه واسنجی (70%) و اعتبارسنجی (30%) تقسیم و چهار مدل، رگرسیون حداقل مربعات جزئی (PLSR)، ماشین بردار پشتیبان (SVM)، جنگل تصادفی RF)) و رگرسیون فرآیند گاوسی (GPR) برای پیشبینی کربناتها از این اطلاعات، برازش یافت. نتایج نشان داد که ترکیب مدل SVM با دادههای خام طیفی در محدودهی Vis-NIR و ترکیب مدل PLSR با روش پیشپردازش منحنی حذف پیوستار (CR) در گستره mid-IR، به ترتیب با 81/0R2= و 86/0 R2= بهترین عملکرد را در پیشبینی کربناتها داشتهاند. همچنین نتایج نشان داد که عملکردگستره mid-IR در برآورد کربناتها نسبت به Vis-NIR بالاتر بوده است. در مجموع میتوان تکنیک طیفسنجی را بهعنوان روشی سریع و البته دقیق در تخمین کربناتهای خاک مطرح و موردارزیابیهای بیشتر قرار داد.
کلیدواژهها
عنوان مقاله [English]
Evaluation of reflectance spectroscopy for assessment of soil carbonates (case study: Juneqan district in Chaharmohal and Bakhtiari Province)
نویسندگان [English]
- N. Asgari 1
- S. Ayoubi 2
- A. Dematte 3
- H. Khademi 2
1 Ph.D student, College of Agriculture, Isfahan University of Technology, Iran.
2 Professor, College of Agriculture, Isfahan University of Technology, Iran.
3 Professor, Department of Soil Science, College of Agriculture Luiz de Queiróz, Piracicaba, SP, Brazil.
چکیده [English]
Introduction Carbonates are an essential and prominent constituent of soil chemical properties particularly in arid and semiarid regions, in regards with soil productivity and conservation. The conventional techniques for assessing soil properties rely on direct laboratory measurements which are expensive, time consuming and labor intensive. Hence, it is required to develop fast and cost-efficient techniques for evaluation of mentioned parameters. The Koppen climatic classification generally categorizes Iran among the arid and semi-arid climates. About 90 % of its lands are arid or semiarid. According to Soil Survey Staff (2014), calcareous soils contain 5% or more volumes of inorganic carbon (or carbonate calcium equivalent), which are the prevailing formation in arid and semi-arid areas. These soils are typical of areas where minerals cannot be leached away from the soil profile due to low precipitation. Based on the reports of FAO.UNDP (1972), approximately 12% of soils all over the world and 65% in Iran are calcareous. Therefore, carbonate is a key component that physically and chemically influences soil properties, as well as its fertility and productivity. One of the fast, easy-to-use, cost-effective and non-destructive methods of soil analysis is the visible to near-infrared (Vis-NIR) and mid-infrared (mid-IR) spectroscopy, that can partly be employed for the optimization of traditional techniques. Therefore, the reflectance spectroscopy is considered as one of relatively inexpensive and fast techniques to evaluate these features. The purpose of the present study was to evaluate the capability of the reflectance spectroscopy technique in Vis-NIR (250-2500 nm) and mid-IR (400-400 cm1-) ranges to estimate soil carbonates content as one of the key components affecting the physical and chemical properties of soils (especially in arid and semi-arid regions).
Materials and Methods The study area is located in Juneqan District, Chaharmohal and Bakhtiari Province, southwest of Iran. 272 soil samples were collected from a depth of 0-10 cm, air dried and passed through a 2 mm sieve. The carbonates value of each sample was determined by standard laboratory method. The spectral reflectance of soil samples was extracted in the Vis-NIR (250-2500 nm) and mid-IR (400-400 cm1-) ranges using a spectroradiometer FieldSpec 3 (ASD-Analytical Spectral Devices, Boulder Colorado, USA) and Nicolet 6700 Fourier Transform Infrared (FT-IR) (Thermo Fisher Scientific Inc., Waltham, MA), respectively. In the next step, seven preprocessing methods included absorbance transformation (log [1/reflectance]) (Abs), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), Savitzsky-Golay derivation (SGD), Continuum removal transformation (CR), Normalization in range <-1,>1 (Nor) and Detrend (Det), were performed over original spectra for correcting light scattering in reflectance measurements and data improvement before using data in calibration models. Afterward, The dataset (272 samples) for each spectra range was randomly divided in calibration (70%) and validation (30%) datasets. Four different calibration models were fitted over Vis-NIR and mid-IR spectra to develop carbonates prediction models including: Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), Random Forest (RF) and Gaussian Process Regression (GPR). The evaluation of soil predicting models was done according to the value of R2, RMSE and RPD. According to some researches, RPD values more than 2 shows that the models provide precise predictions, values of RPD between 1.4 and 2 are considered to be reasonably representative, and values less than 1.4 indicate poor predictive value.
Results and Discussion The carbonates content in studied samples ranged from 1 to 76% with an average value of 24.7%. Overall, carbonates content promoted increase of spectral reflectance intensity on several region of spectrum in both spectral ranges. The specific absorption wavelength in Vis-NIR spectra used to indicate the presence of soil carbonates was 2338 nm and in the mid-IR range were 714, 850, 870, 1796, and 2510 cm1. The results showed that the best performance of the used models in the Vis-NIR spectral range was related to the SVM model (R2=0.81, RMSE=5.36) and in the mid-IR range allocated to PLSR model (R2=0.86, RMSE=4.5). Both of these models showed great accuracy in carbonates estimating (RPD>2). Besides, the results showed that the mid-IR spectral range in the prediction of carbonates provided better performance than the Vis-NIR range. This can explained by the fact that the fundamental molecular vibrations of soil components occur in the mid-IR range, while only their overtones and combinations are detected in the Vis-NIR range.
Conclusion It seems that the reflectance spectroscopy technique can be considered as a precise substitute for the conventional methods of measuring carbonates, which are sometimes costly, time consuming and destructive. However, due to the spatial and temporal variability of soil properties as well as the huge variety of models and spectral preprocessing methods, it is necessary to examine the capability of this technique in other areas with other preprocessing methods and regression models.
کلیدواژهها [English]
- Spectral preprocessing
- Diffuse reflectance
- spectroscopy
- Soil reflection spectrum
- PLSR
- Spectral continuum-removed curve
- Allison, L.E., and Morse, J.W. 1965. Carbonate. InC. A. Black et al. (ed). Methods of soil analysis, part 2. Agronomy, 9: 1379-1400.
- Ben-Dor, E., and Banin, A. 1995. Near infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Science Society of America Journal, 59: 364–372.
- Breiman, L. 2001. Random forests. Mach. Learn, 45: 5–32. 4. Brown, D.J., Shepherd, K.D., Walsh, M.G., Mays, M.D., and Reinsch, T.G. 2006. Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma, 132: 273–290.
- Chang, C.W., Laird, D.A., Mausbach, M.J., Maurice, J., and Hurburgh. J.R. 2001. Near- Infrared reflectance spectroscopy – principal components regression analyses of soil properties. Soil Science Society of America Journal, 65: 480–490.
- Changwen, D., Zhaoyang, Ma., Jianmin, Z., and Goyne, K.W. 2013. Application of mid-infrared photoacoustic spectroscopy in monitoring carbonate content in soils. Sensors and Actuators B: Chemical, 188, 1167–1175.
- Curran, P.L., Dungan, J.L. and Peterdon, D.L. 2001. Estimation the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies. Remote sensing of Environment, 76: 349-359.
- Demattê, J.A.M. 2002. Characterization and discrimination of soils by their reflected electromagnetic energy. Pesq. agropec. Brasília, 37: 1445-1458.
- Demattê, J.A.M., Silva, M.L., Rocha, G.C., Carvalho, L.A.De., Formaggio, A.R., and Firme, L.P. 2005. Variações espectrais em solos submetidos à aplicação de torta de filtro. Rev. Bras. Ciênc. Solo, 29(3): 317-326.
- Demattê, J.A.M.; Garcia, G.J., and Prochnow, L.I. 1998. Variações induzidas de atributos químicos e sua influência na reflectância espectral de três solos do estado do Paraná. R. Bras. Ci. Solo, 22: 479-490.
- FAO. 1996. Digital soil map of the world and derived soil properties. Vers. 3.5., Nov, 1995. FAO, Rome.
- FAO/UNDP. 1972. Calcareous Soils. Report of the Regional Seminar on Reclamation and Management of Calcareous Soils. 27 November–2 December, Cairo. Egypt.
- Gholizadeh, A., Borůvka, L., Saberioon, M.M., Kozák, J., Vašát, R., and Němeček, K. 2015. Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. Soil and Water Research, 10: 218–227.
- Gomez, C., and Coulouma, G. 2018. Importance of the spatial extent for using soil properties estimated by laboratory VNIR/SWIR spectroscopy: Examples of the clay and calcium carbonate content. Geoderma, 330: 244–253.
- Hunt, G.R., and Salisbury, J.W. 1971. Visible and near-infrared spectra of minerals and rocks: II. Carbonates. Modern Geology, 2: 23-30.
- Janik, L.J., Merry, R.H., and Skjemstad, J.O. 1998. Can mid-infrared diffuse reflectance analysis replace soil extractions? Australian Journal of Experimental Agriculture, 38: 681–696.
- Janik, L.J., and Skjemstad, J.O. 1995. Characterization and analysis of soils using midinfrared partial least- squares. 2. Correlations with some laboratory data. Australian Journal of Soil Research, 33: 637–650.
- Khayamim, F., Wetterlind, J., Khademi, H., Jean Robertson, A.H., Faz Cano, A., and Stenberg, B. 2015. Using visible and near infrared spectroscopy to estimate carbonates and gypsum in soils in arid and sub-humid regions of Isfahan, Iran. Journal of Near Infrared Spectroscopy, 23: 155–165.
- McCarty, G.W., Reeves, J.B., Reeves, V.B., Follett, R. F., and Kimble J.M. 2002. Mid-Infrared and Near-Infrared Diffuse Reflectance Spectroscopy for Soil Carbon Measurement. Soil Science Society of America Journal, 66: 640-646.
- Minasny, B., Tranter, G., McBratney, A.B., Brough, D., and Murphy, B.W. 2009. Regional transferability of mid-infrared 106 Agron. Colomb. 33(1) 2015 diffuse reflectance spectroscopic prediction for soil chemical properties. Geoderma, 153, 155- 162.
- Neal, R.M. 1997. Monte carlo implementation of gaussian process models for bayesian regression and classification, University of Toronto, Toronto: Department of Statistics and Department of Computer Science, Technical report no, 9702.
- Reeves, J.B., and Smith, D.B. 2009. The potential of mid- and near-infrared diffuse reflectance spectroscopy for determining major- and trace-element concentrations in soils from a geochemical survey of North America. Applied Geochemistry 24: 1472– 1481.
- Rinnan, A., Van den Berg, F., and Engelsen, S.B. 2009. Review of the most common preprocessing techniques for near-infrared spectra. TrAC Trends in Analytical Chemistry, 28: 1201–1222.
- Schwertmann, U. 1993. Relations between iron oxides, soil color and soil formation. In J, M, Bigham and E. J. Ciolcosz (eds): Soil colors. (pp. 51-69). Soil Science Society of America, Madison.
- Soil Survey Staff. (2014). Keys to Soil Taxonomy, twelfth ed. USDA Natural Resources Conservation Service, USA.
- Spielvogel, S., Knicker, H., and Kogel-Knabner, I. 2004. Soil organic matter composition and soil lightness. Journal of Plant Nutrition and Soil Science, 167: 545- 555.
- Summers, D., Lewis. M., Ostendorf, B., and Chittleborough, D. 2011. Visible nearinfrared reflectance spectroscopy as a predictive indicator of soil properties. Ecol. Indic. Journal. 11: 123-131.
- Tatzber, M., Mutsch, F., Mentler, A., Leitgeb, E., Englisch, M., and Gerzabek, M. H. 2010. Determination of Organic and Inorganic Carbon in Forest Soil Samples by MidInfrared Spectroscopy and Partial Least Squares Regression. Applied Spectroscopy, 10(64):1167-1175.
- Terra, F.S., Demattê, J.A.M., and Viscarra Rossel, R.A. 2015. Spectral libraries for quantitative analyses of tropical Brazilian soils: comparing Vis–NIR and mid-IR reflectance data. Geoderma, 255–256: 81–93.
- Vapnik V.N. 1995. The Nature of Statistical Learning Theory. New York, USA: Springer-Verlag.
- Viscarra Rossel, R.A., Cattle, S.R., Ortega, A., and Fouad, Y. 2009. In situ measurements of soil colour, mineral composition and clay content by Vis–NIR spectroscopy. Geoderma, 150, 253–266.
- Volkan Bilgili, A., Van, H.M.Es, Akbas, F., Durak, A., and. Hively. W.D. 2009. Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semi-arid area of Turkey. Journal of Arid Environments, 74: 229–238.
- Wold, S., Martens, H. and Wold, H. 1983. The multivariate calibration method in chemistry solved by the PLS Method. In Ruhe A, Kagstrom B. (eds.) Proceedings of the Conference on Matrix Pencils, Lecture Notes in Mathematics. Springer-Verlag, Heidelberg. pp. 286–293.