برآورد مقدار مادة آلی خاک، با استفاده از داده‌های طیفی و مدل‌های آماری رگرسیون حداقل مربعات جزئی و رگرسیون مؤلفة اصلی

نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشیار گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه لرستان

2 دانشجوی دکتری علوم خاک دانشگاه لرستان

3 دانشجوی دکتری مدیریت منابع خاک، دانشکده کشاورزی، دانشگاه لرستان

چکیده

مادة آلی خاک از مهم‌ترین ویژ‌گی‌های فیزیکوشیمیایی خاک است که در تعیین کیفیت و مدیریت آن نقش بسزایی دارد. مقدار کربن آلی خاک دچار تغییرات زمانی و مکانی بالایی می‌شود و بنابراین، تعیین آن در آزمایشگاه هزینه‌بر، مشکل و وقت‌گیر است. طیف‌سنجی مرئی‌ـ مادون قرمز نزدیک، از نظر کاهش زمان و هزینه، روش توجیه‌پذیری است که برای بررسی کربن آلی خاک پیشنهاد شده است. هدف این پژوهش بررسی کربن آلی خاک ازطریق طیف‌‌سنجی مرئی‌ـ مادون قرمز نزدیک و برآورد آن با استفاده از مدل‌های آماری، PCA، PLSR و PCR است. برای این منظور، چهل نمونه خاک از عمق صفر تا 30 سانتی‌متری، به‌روش سیستماتیک تصادفی، برمبنای مطالعات پیشین و تعیین طبقات متفاوت خاک‌های منطقه، برداشت شد. تجزیة شیمیایی خاک‌ها طبق روش‌های استاندارد صورت گرفت. بازتاب طیفی نمونه‌های خاک در محدودة طیفی ٣٥٠ تا 2500 نانومتر اندازه‌گیری شد و پس از اعمال روش‌های پیش‌‌پردازش فیلتر ساویتزکی‌ـ گلای، به‌کمک تحلیل مؤلفه‌های اصلی (PCA)، رگرسیون حداقل مربعات جزئی(PLSR)  و رگرسیون مؤلفة اصلی (PCR)، کربن آلی خاک برآورد شد. نتایج این پژوهش نشان داد که فیلتر ساویتزکی‌ـ ‌گلای قوی‌ترین روش پیش‌پردازش داده‌های طیفی بوده است. نتایج مدل‌‌سازی به‌روش PLSR  نشان داد مقادیر R2، RMSE و RPD در مرحلة اعتبارسنجی، برای پیش‌بینی مادة آلی، به‌ترتیب 85/0، 14/0 و 78/2 بوده درحالی‌که نتایج مدل‌سازی PCR، برای پارامترهای آماری یادشده، به‌ترتیب 78/0، 19/0 و 05/2 است و دقت بیشتر روش آماری PLSR را، در مقایسه با روش PCR، برای مدل‌سازی برآورد کربن آلی خاک، می‌رساند. بنابراین، به‌نظر می‌رسد مدل PLSR، برای پیش‌بینی سریع کربن آلی خاک‌های مناطق خشک و نیمه‌خشک، کارآیی و دقت بیشتری داشته باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Estimation Soil Organic Matter (SOM) Content Using Visible and Near Infrared Spectral data, PLSR and PCR Statistical Models

نویسندگان [English]

  • Hamidreza Matinfar 1
  • H Mahmodzadeh 2
  • A Fariabi 3
1 Associate Professor Soil Science, remote sensing and GIS
2 Ph.D. Student of Soil Science, Lorestan University, Soil Science, Genesis and Classification
3 Ph.D. Student of Soil Science Department, Lorestan University, Soil Science, Genesis and Classification
چکیده [English]

Soil organic matter is one of the most important Physical and chemical properties of soil that it iscritical in determining the quality and management of soils. Quantify of soil organic carbon due to thehigh spatial variability and changes over time is difficult. Near-infrared-visible spectroscopy is afeasible method to reduce the time and cost to check the organic carbon. The aim of this study was toevaluate soil organic carbon through near-infrared and visible spectroscopy with the statistical modelsPLSR and PCR. For this purpose, 40 soil samples from depths of 0 to 30 cm were collected bysystematic random method based on previous studies and determination of different classes of soils inthe region. Chemical analysis of soils was performed according to standard methods. Spectralreflectance of soil samples in the range of 350 to 2500 nm was measured then after applying thepreprocessing methods such as Savitzky and Golay filter, Soil organic carbon were calculated byprincipal component analysis (PCA), regression partial least squares (PLSR) and principal componentregression (PCR) models. The results of this study showed that the Savitzky and Golay filter was thestrongest preprocessing method for spectral data. Coefficients of determination (R2), root mean squareerror of Prediction (RMSE) and ratio of prediction to deviation (RPD) in the calibration andvalidation to predict organic matter, respectively, 0.97, 0.05, 5.09 and 0.85, 0.14, 2.78 respectively.Therefore, for dry and semi-arid soils of the PLSR model, it is more efficient to predict the organiccarbon of the soil. The results showed that the PLSR model has better performance than the PCRmodel in soil organic carbon estimation.

کلیدواژه‌ها [English]

  • organic matter
  • spectroscopy
  • PLSR
  • PCR
  • Principal Component Analysis
  1. عباسی، مژگان، 1388."بررسی مشخصه های طیفی برگ گونه های راش، ممرز ، توسکا ، بلوط وانجیلی با استفاده از طیف سنج زمینی"، رساله دکترا، دانشکده منابع طبیعی پردیس.
  2. ملاح نوکنده، س.، همایی، م.، نوروزی، ع.ا. 1993. "بررسی امکان پذیری برآورد مواد آلی خاک با استفاده از تصاویر ابرطیفی هایپریون در مناطق ایوانکی و ارومیه". نشریه علمی-پژوهشی مهندسی و مدیریت آبخیز. جلد 6، شماره 3، 190-200.
  3. Aїchi, H., Y. Fouad, C. Walter, R. A. Viscarra Rossel, Z. L. Chabaane and M. Sanaa. 2009. Regional predictions of soil organic carbon content from spectral reflectance measurements. Biosys. Eng. 104: 442-446.
  4. Ben-Dor, E. and A. Banin. 1995. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Sci. Soc. Am. J. 59: 364-372.
  5. Blackmer, A.M., and S.E. White. 1998. Using precision farming technology to improve management of soil and fertilizer nitrogen. Australian Journal of Agricultural Research 49: 555-564.
  6. Bouma, J. 2002. Land quality indicators of sustainable land management across scales. Agriculture, Ecosystems and Environment. 88 (2), 129–136.
  7. Brunet, D., Barthes, B.G., Chotte, J.L., and Feller, C. 2007. Determination of carbon and nitrogen contents in alfisols, oxisoils and ultisols from Africa and Brazil using NIRS analysis: Effects of sample grinding and set heterogeneity. Geoderma. 139: 106-117.
  8. Cambou, A; Cardinael, R; Kouakoua, E; Villeneuve, M; Durand, C; Barthès, B.G., 2015. Prediction of soil organic carbon stock using visible and near infrared reflectance spectroscopy (VNIRS) in the field. Geoderma 261, 151–159.
  9. Chang, C.W., Laird, D.A., Mausbach, M.J., and Hurburgh, Jr.C.R. 2001. Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties. Soil Science Society of America Journal. 65 (2): 480–490.
  10. Christy, C.D. 2008. Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy. Computers and Electronics in Agriculture 61: 10-19.
  11. Clark, R.N. 1999. Spectroscopy of rocks and minerals, and principles of spectroscopy. In: Rencz, A.N. (Ed.), Remote Sensing for Earth Sciences. Manual of Remote Sensing. John Wiley and Sons, Inc., Toronto, pp. 3–58.
  12. Cohen, M.J., Prenger, J.P., DeBusk, W.F., 2005. Visible-Near infrared reflectance spectroscopy for rapid, non-destructive assessment of wetland soil quality. Journal of Environmental Quality 34, 1422–1434.
  13. Conforti, M; Castrignanò, A; Robustelli, G; Scarciglia, F; Stelluti, M; Buttafuoco. 2015. Laboratory-based Vis–NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content. Catena 124, 60–67.
  14. Conforti, M., Froio, R., Matteucci, G., Caloiero, T., Buttafuoco, G., 2013. Potentiality of laboratory visible and near infrared spectroscopy for determining clay content in forest soils: a case study from high forest beech (Fagus sylvatica) in Calabria (southern Italy). EQA Int. J. Environ. Qual. 11, 49–64.
  15. Cozzolino, D., and Moron, A. 2003. The potential of near-infrared reflectance spectroscopy to analyse soil chemical and physical characteristics. Journal of Agricultural Sciences. 140: 65– 71.
  16. Esbensen, K. H. 2006. Multivariate Data Analysis. CAMO Software AS. 5th Edition. 589 Pages.
  17. Farifteh, J., Van Der Meer, F., Atzberger, C., Carranza, E.J.M., 2007. Quantitative analysis of salt-affected soil reflectance spectra: a comparison of two adaptive methods (PLSR and ANN). Remote Sens. Environ. 110, 59–78.
  18. Fideˆncio, P.H., Poppi, R.J., Andrade, J.C., Cantarella, H., 2002. Determination of organic matter in soil using near-infrared spectroscopy and partial least squares regression. Communications in Soil Science and Plant Analysis 33, 1607–1615.
  19. Guerrero, C., R. A. Viscarra Rossel and A. M. Mouazen. 2010. Diffuse reflectance spectroscopy in soil science and land resource assessment. Geoderma. 158: 1-2.
  20. IPCC (International Panel on Climate Change), 2007. Climate change 2007: synthesis report. In: Writing Team, Core, Pachauri, R.K., Reisinger, A. (Eds.), Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva.
  21. Khayamim F, Khademi H, Stenberg B, Wetterlind J. Capability of vis-NIR Spectroscopy to Predict Selected Chemical Soil Properties in Isfahan Province. JWSS - Isfahan University of Technology. 2015; 19 (72):81-92
  22. Lee, K.S., Lee, D.H., Sudduth, K.A., Chung, S.O., Drummond, S.T., 2007. Wavelength Identification for Reflectance Estimation of Surface and Subsurface Soil Properties. ASABE Annual International Meeting, St. Joseph, Michigan.
  23. Mabit, L., Bernard, C., 2009. Spatial distribution and content of soil organic matter in an agricultural field in eastern Canada, as estimated from geostatistical tools. Earth Surf. Proc. Land 35, 278–283.
  24. McBratney, A.B., Stockmann, U., Angers, D., Minasny, B., Field, D., 2014. Challenges for soil organic carbon research. In: Alfred, E., Hartemink, A.E.,
  25. McBratney, A.B., Minasny, B., Viscarra Rossel, R.A., 2006. Spectral soil analysis and inference systems: a powerful combination for solving the soil data crisis. Geoderma 136, 272–278.
  26. McSweeney, K. (Eds.), Soil Carbon. Springer, New York, pp. 3–16.
  27. Nelson, D. W. and. Sommers, L. E. 1982. Total carbon, organic carbon and organic matter. In: Methods of soil analysis. Page A. L. et al. (Eds.). Part 2. 2nd ed. Agron. Monogr. 9. ASA and SSSA Madison, WI. 101–129.
  28. Nieder, R., Benbi, D.K., 2008. Carbon and Nitrogen in the Terrestrial Environment. Springer.
  29. Nocita, M., Stevens, A., Noon, C., vanWesemael, B. 2013. Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy. Geoderma 199, 37–42.
  30. Reeves III, J.B., McCarty, G.W., Mimmo, T., 2002. The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soils. Environ. Pollut. 116, S277–S284.
  31. Saeys, W., Mouazen, A.M., Ramon, H., 2005. Potential for onsite and online analysis of pig manure using visible and near infrared reflectance spectroscopy. Biosystems Engineering 91, 393–402.
  32. Santra, P., Sahoo, R.N., Das, B.S., Samal, R.N., Pattanaik, A.K., and Gupta, V.K. 2009. Estimation of soil hydraulic properties using proximal spectral reflectance in visible, near-infrared, and shortwave-infrared (VIS–NIR–SWIR) region. Geoderma. 152:338–349.
  33. Sarkhot, D.V., Grunwald, S., Ge, Y., Morgan, C.L.S., 2011. Comparison and detection of total and available soil carbon fractions using visible/near infrared diffuse reflectance spectroscopy. Geoderma 164, 23–32.
  34. Savitzky, A.; Golay, M.J. 1964. Smoothing and differentiation of data by simplified least squares
  35. Shepherd, K.D., and Walsh, M.G. 2007. Infrared spectroscopy enabling an evidence based diagnostic surveillance approach to agricultural and environmental management in developing countries. Journal of Near Infrared Spectroscopy. 15 (1): 1–19.
  36. Shepherd, K.D., Walsh, M.G., 2002. Development of reflectance spectral libraries for characterization of soil properties. Soil Sci. Soc. Am. J. 66, 988–998.
  37. Stenberg, B., Viscarra Rossel, R.A., Mouazen, A.M., Wetterlind, J., 2010. Visible and near infrared spectroscopy in soil science. Adv. Agron. 107, 163–215.
  38. Soon, Y.K. and Hendershot, W.H. 2009. In Carter, M.R., Gregorich, E.G. (eds.), Soil Sampling and Methods of Analysis part III: soil chemical analyses. CRC Press Taylor & Francis Group, USA. pp: 173-178.
  39. Stenberg, B., Viscarra Rossel, R.A., Mouazen, A.M., Wetterlind, J., 2010. Visible and near infrared spectroscopy in soil science. Adv. Agron. 107, 163–215.
  40. Stevens, A., Wesemael, B.v., Bartholomeus, H.M., Rossilon, D., Tychon, B., Ben-Dor, E., 2007. CARBIS final report: detecting soil carbon and its spatial variability by imaging spectroscopy. Belspo, Louvain-la-Neuve, p. 14.
  41. Summers, D., M. Lewis, B. Ostendorf and D. Chittleborough. 2011. Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecol. Indic. 11: 123-131.
  42. Viscarra Rossel, R.A., Behrens, T., 2010. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158, 46–54.
  43. 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 131, 59–75.
  44. Walkly, A. and Black, I.A. 1934. An examination of digestion method for determining soil organic matter and a proposed modification of the chromic acid titration. Soil Science Society of American journal. 37: 29- 38.
  45. Walvoort, D.J.J., and McBratney, A.B. 2001. Diffuse reflectance spectrometry as a proximal sensing tool for precision agriculture. In: Grenier, G., Blackmore, S. (Eds.), ECPA 2001, Third European Conference on Precision Agriculture, vol. 1. Agro Montpellier, pp. 503– 507.
  46. Williams, P.C. 2001. Implementation of near-infrared technology. PP. 145-169. In: Williams, P., Norris, K. (Eds.), Near-infrared Technology in the Agricultural and Food Industries. American Association of Cereal Chemists Inc., St. Paul, MN.
  47. Zornoza, R; Guerrero, C; Mataix-Solera, J; Scow, K.M; Arcenegui, V; Mataix-Beneyto, J. 2008. Near infrared spectroscopy for determination of various physical, chemical and biochemical properties in Mediterranean soils. Soil Biology & Biochemistry 40: 1923–1930.