برآورد برخی ویژگی‌های مبنایی خاک‌ توسط طیف‌سنجی مرئی - مادون قرمز نزدیک در استان کردستان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد علوم خاک دانشگاه کردستان

2 عضو هیئت علمی گروه علوم خاک دانشگاه کردستان

3 عضوهیئت علمی گروه خاکشناسی دانشگاه تربیت مدرس

4 محقق پسا دکتری، گروه آب خاک و محیط زیست، دانشگاه اریزونا

چکیده

طیف­سنجی مرئی-مادون قرمز نزدیک به­عنوان روشی غیر مخرب، سریع، ارزان، دارای حداقل آماده­سازی نمونه­ها و بدون آسیب به زیست­بوم می­تواند جایگزین روش­های مرسوم آزمایشگاهی شود. هدف از این پژوهش ارزیابی طیف­سنجی انعکاسی در برآورد برخی ویژگی­های خاک­های دشت­های کشاورزی قروه و دهگلان در استان کردستان بود. بدین منظور تعداد 120 نمونه خاک از منطقه مورد مطالعه جمع­آوری و ویژگی­های مبنایی آن­ها در آزمایشگاه با روش­های استاندارد اندازه­گیری شد. آنالیز طیفی خاک­ها با استفاده از دستگاه طیف­سنجی زمینی با طول موج 2500- 350 نانومتر انجام شد. پس از ثبت طیف­ها انواع روش­های پیش­پردازش مورد ارزیابی قرار گرفت. سپس از رگرسیون خطی چند­گانه گام­به­گام، برای پیش­بینی پارامترهای مورد­مطالعه استفاده گردید. با توجه به آماره RPD، بهترین تخمین توابع رگرسیونی پیشنهادی برای ظرفیت تبادل کاتیونی (02/2) و تخمین‌هایی قابل قبول برای رس (70/1)، سیلت (59/1)، شن (80/1)، جرم ویژه ظاهری (53/1) و حقیقی (55/1)، میانگین قطر ذرات (52/1) و انحراف معیار هندسی قطر ذرات خاک (66/1)، کربن آلی (74/1) و کربنات کلسیم معادل (49/1) به‌دست آمد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Predicting some soil properties using VIS-NIR spectroscopy in the Kurdistan province

نویسندگان [English]

  • salaheddin Karimi 1
  • Masood Davari 2
  • Hoseinali Bahrami 3
  • Ibrahim Babaeian 4
  • Seyem Mohamad Taher Hoseini 2
1 University of Kordestan
2 University of Kordestan
3 University of Tarbiat Modares
4 University of Arizona
چکیده [English]

The visible and near-infrared (VIS-NIR) spectroscopy are non-destructive, rapid, cost-effective techniques, with minimal samples preparation and no loss or damage to the environment that could be alternatives to conventional soil analysis methods. The objective of this study was to evaluate the ability of VIS-NIR spectroscopy to predict some soil properties of Qorveh and Dehgolan plains, Kurdistan Province. For this propose, 120 soil samples were collected from the study area. Soil properties were measured by standard laboratory methods. The soils spectral reflectance over 350 to 2500 nm range were measured using a handheld spectrometer apparatus. Different pre-processing techniques were evaluated after recording the spectra. Stepwise multiple linear regression (SMLR) was used to estimate some soil properties. According to RPD values, statistically percision predictions were obtained for cation exchange capacity (2.02), and estimations for clay (1.7), silt (1.59), sand (1.8), geometric mean particle diameter (1.52) and geometric particle-size standard deviations (1.66), bulk density (1.53), particle density (1.55), organic carbon (1.74) and calcium carbonate equivale (1.49) were acceptable.

کلیدواژه‌ها [English]

  • Soil properties
  • Spectra pre-processing
  • Spectral reflectance

مراجع

Babaeian, E., M. Homaee, C. Montzka, H. Vereecken, and A.A. Norouzi. (2015a). Towards retrieving soil hydraulic properties by hyperspectral remote sensing. Vadoze Zone J. 14(3),doi: 10.2136/ vzj2014.07.0080.
Babaeian, E., Homaee, M., Vereecken, H., Montzka, C., Norouzi, A. A., & van Genuchten, M. T. (2015b). A Comparative Study of Multiple Approaches for Predicting the Soil–Water Retention Curve: Hyperspectral Information vs. Basic Soil Properties. Soil Science Society of America Journal, 79, 1043-1058.
Ben-Dor, E., 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.
Bilgili, A. V., Van Es, H. M., Akbas, F., Durak, A., & Hively, W. D. (2010). Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semi-arid area of Turkey. Journal of Arid Environments, 74(2), 229-238.
Cecillon, L.C., Barthesb, B.G., Gomez, C., Ertlen, D., Genot, V., Hedde, M., Stevengs, A. and Brun, J. (2009). Assessment and monitoring of soil quality using near-infrared reflectance spectroscopy (NIRS). European Journal of SoilScience, 60, 770–784.
Clark, R.N., King, T.V.V., Klejwa, M., Swayze, G.A. and Vergo, N. (1990). High spectral resolution reflectance spectroscopy of minerals. Journal of Geophysical Research, 95, 12653–12680.
Daniel, K.W., Tripathi, N.K. and Honda, K. (2003). Artificial neural network analysis of laboratory and in situ spectra for the estimation of macronutrients in soils of Lop Buri (Thailand). Australian Journal of Soil Research, 41, 47–59.
Debaene, G., Niedzwiecki, J., & Pecio, A. (2010). Visible and near-infrared spectrophotometer for soil analysis: preliminary results. Polish Journal of Agronomy, 3, 3-9.
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. Scientia Agricola, 71(6), 509-520.
Demattê, J., Sousa, A.A., Alves, M.C., Nanni, M.R., Fiorio, P.R., Campos, R.C. (2006). Determining soil water status and other soil characteristics by spectral proximal sensing. Geoderma, 135, 179-195.
Genot, V., Colinet, G., Bock, L., Vanvyve, D., Reusen, Y. and Dardenne, P. (2011). Near infrared reflectance spectroscopy for estimating soil characteristics valuable in the diagnosis of soil fertility. Journal of Near Infrared Spectroscopy, 19, 117-138.
Gomez, C., Lagacherie, P., & Coulouma, G. (2012). Regional predictions of eight common soil properties and their spatial structures from hyperspectral Vis–NIR data. Geoderma, 189, 176-185.
Gomez, C., Lagacherie, P., Coulouma, G. (2008). Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements. Geoderma, 148, 141-148.
Hunt, G. R. (1977). Spectral signatures of particulate minerals in the visible and near infrared. Geophysics, 42(3), 501-513.
Janik, L.J., Forrester, S.T. and Rawson, A. (2009). The prediction of soil chemical and physical properties from mid-infrared spectroscopy and combined partial least-squares regression and neural networks (PLS-NN) analysis. Chemometrics and Intelligent Laboratory Systems, 97, 179-188.
Kim, I., Pullanagari, R.R., Deurer, M., Singh, R., Huh, K.Y., Clothier, B.E. (2014). The use of visible and near-infrared spectroscopy for the analysis of soil water repellency. European Journal of Soil Science, 65, 360-368.
Klute, A. (1986). Methods of soil analysis. Part 1. Physical and mineralogical methods (No. Ed. 2). American Society of Agronomy, Inc.
Kodaira, M., Shibusawa, S. (2013). Using a mobile real-time soil visible-near infrared sensor for high resolution soil property mapping. Geoderma, 199, 64-79.
Lagacherie, P., Baret, F., Feret, J.B., Netto, J.M and Robbez-Masson, J.M. (2008). Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements. Remote Sensing of Environment, 112, 825-835.
Liu, X., Xu, J., Zhang, M., Si, B., and Zhao, K. (2008). Spatial variability of soil available Zn and Cu in paddy rice fields of China. Environmental Geology, 55, 1569-1576.
Minasny, B., McBratney, A., Tranter, G., Murphy, B. (2008). Using soil knowledge for the evaluation of mid‐infrared diffuse reflectance spectroscopy for predicting soil physical and mechanical properties. European Journal of Soil Science, 59, 960-971.
Mutuo, P.K., Shepherd, K.D., Albrecht, A. and Cadisch, G. (2006). Predic- tion of carbon mineralization rates from different soil physical fractions using diffuse reflectance spectroscopy. Soil Biology and Biochemistry, 38,1658 1664.
Nanni, M. R., & Demattê, J. A. M. (2006). Spectral reflectance methodology in comparison to traditional soil analysis. Soil Science Society of America Journal, 70(2), 393-407.
Nocita, M., Stevens, A., Noon, C., van Wesemael, B. (2013). Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy. Geoderma, 199, 37-42.
Page, A. L., Miller, R. H., & Keeney, D. R. (1982). Methods of soil analysis. Part 2. Chemical and microbiological properties. American Society of Agronomy. Soil Science Society of America, Madison, Wisconsin, 1159.
Savvides, A., Corstanje, R., Baxter, S.J., Rawlins, B.J. and Lark, R.M. (2010). The relationship between diffuse spectral reflectance of the soil and its cation exchange capacity is scale dependent. Geoderma, 154, 353–358.
Shepherd, K.D., Walsh, M.G. (2002). Development of reflectance spectral libraries for characterization of soil properties. Soil Science Society of America Journal, 66, 988-998.
Shirazi, M.A., Boersma, L. (1984). A unifying quantitative analysis of soil texture. Soil Science Society of America Journal, 48, 142-147.
Stenberg, B., Rossel, R. A. V., Mouazen, A. M., & Wetterlind, J. (2010). Chapter five-visible and near infrared spectroscopy in soil science. Advances in Agronomy, 107, 163-215.
Stoner, E.R., Baumgardner, M. (1981). Characteristic variations in reflectance of surface soils. Soil Science Society of America Journal, 45, 1161-1165.
Summers, D., Lewis, M., Ostendorf, B., and Chittleborough, D. (2011). Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecological Indicators, 11(1), 123-131.
Viscarra Rossel, R.A.V. (2008). ParLeS: Software for chemometric analysis of spectroscopic data. Chemometrics and Intelligent Laboratory Systems, 90, 72–83.
 Viscarra Rossel, R., McGlynn, R., McBratney, A. (2006). Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy. Geoderma, 137, 70-82.
 
Willmott, C.J. (1981). On the validation of models. Physical Geography, 2, 184–194.
Workman, J., (2000). Handbook of Organic Compounds: UV-Vis and NIR spectra. Academic press, pp. 77-197.
تحقیقات آب و خاک ایران
دوره 48، شماره 3
مهر و آبان
مهر 1396
صفحه 573-585

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