برآورد برخی خصوصیات خاک با استفاده از تحلیل داده‌های طیفی (Vis-NIR) و انواع روش‌های پیش‌پردازش

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید باهنر کرمان، کرمان، ایران

2 استاد گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید باهنر کرمان، کرمان، ایران

چکیده

طیف‌سنجی خاک به‌دلیل سرعت و دقت بالا، هزینه پایین و غیرمخرب بودن بر بسیاری از محدودیت­های روش­های سنتی تجزیه خاک غلبه کرده است. مطالعه حاضر با هدف امکان­سنجی استفاده از اطلاعات طیفی خاک به‌منظور برآورد برخی از ویژگی­های کلیدی خاک و مقایسه انواع روش­های پیش­پردازش طیفی در تعیین عملکرد مدل رگرسیون حداقل مربعات جزئی (PLSR) انجام گردید. بدین منظور 100 نمونه خاک سطحی از اراضی واقع در حدفاصل شهرستان­های نی­ریز تا استهبان در شرق استان فارس جمع­آوری و مقادیر کربن ­آلی، قابلیت هدایت الکتریکی، کربنات کلسیم معادل و گچ با استفاده از روش­های ­استاندارد آزمایشگاهی اندازه­گیری گردید. سپس بازتاب طیفی نمونه­های خاک در محدوده 2500-350 نانومتر ثبت و روش­های مختلف پیش­پردازش طیفی بر روی داده­ها اعمال گردید. در ادامه، برآورد خصوصیات خاک با استفاده از روش PLSR انجام شد. نتایج حاکی از توانایی مطلوب روش PLSR در تخمین میزان گچ (2< RPD، 81/0 = R2، 87/3 = RMSE) و توانایی قابل قبول آن برای سایر ویژگی­ها نظیر کربنات ­کلسیم معادل، کربن آلی و قابلیت هدایت الکتریکی خاک (2>RPD> 4/1) بود. همچنین، نتایج نشان داد که روش ­پیش­پردازش مشتق اول به همراه فیلتر ساویتزکی و گلای، بهترین مدل­سازی را برای کربن آلی، روش متغیر نرمال استاندارد (SNV) برای گچ و روش مشتق دوم به همراه فیلتر ساویتزکی و گلای برای قابلیت هدایت الکتریکی خاک ارائه کردند. از طرفی، برآورد کربنات کلسیم خاک با داده­های بدون پیش­پردازش، تخمین بهتری نسبت به استفاده از انواع روش­های پیش­پردازش ارائه داد. به‌طور کلی، نتایج نشان داد که محدوده طیف مرئی برای برآورد کربن آلی و قابلیت هدایت الکتریکی و محدوده مادون قرمز نزدیک برای کربنات کلسیم معادل و گچ کارایی بهتری ارائه دادند.

کلیدواژه‌ها


عنوان مقاله [English]

Estimation of Some Soil Properties Using Spectral Data Analysis (Vis-NIR) and Various Pre-Processing Methods

نویسندگان [English]

  • Sahar Taghdis 1
  • Mohammad Hady Farpoor 2
  • Majid Fekri 2
  • Majid Mahmoodabadi 2
1 PhD Student, Department of Soil Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
2 Department of Soil Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
چکیده [English]

Soil spectroscopy has overcome many limitations of conventional soil analysis methods due to its rapid, accurate, cost-effective, and non-destructive nature. This study was aimed to investigate the capability of soil spectral data in estimating some key soil properties and comparing different spectral preprocessing methods in determining the performance of the partial least squares regression (PLSR) model. For this purpose, 100 soil surface samples were collected from the study area which was located between Neyriz and Estahban regions in the east of Fars Province. The samples were analyzed for organic carbon (OC), electrical conductivity (EC), calcium carbonate equivalent (CaCO3) and gypsum using standard laboratory methods. Then, the spectral reflectance of the soil samples was recorded in the range of 350-2500 nm and various spectral pre-processing methods were applied to the data. Afterwards, the soil properties were estimated using PLSR. The results indicated the desirable capability of PLSR method in estimating the amount of gypsum (RPD >2, R2 = 0.81, RMSE = 3.87) and its acceptable ability for OC, EC and CaCO3 (2< RPD < 1.4). Also, the best modeling systems for OC, gypsum and EC were obtained as the first derivative with Savitzky-Golay smoothing method (FD-SG), the standard normal variate method (SNV), and the second derivative with SG smoothing method (SD-SG), respectively. Besides, non-preprocessing data of soil CaCO3 provided better estimations than various pre-processing methods. Overall, the results revealed that the visible spectrum range provided the best performance for estimating of OC and EC, and the NIR range for CaCO3 and gypsum.

کلیدواژه‌ها [English]

  • Absorption bands
  • Soil spectral behavior
  • Vis-NIR spectroscopy
  • PLSR
Banaie M. H. (2001).  Map of Iran soils moisture and temperature regimes. Soil and Water research institute. Tehran. Iran. (in Farsi)
Ben-Dor, E., Inbar, Y., & Chen, Y. (1997). The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400–2500 nm) during a controlled decomposition process. Remote Sensing of Environment, 61(1), 1-15.
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 Science Society of America Journal, 65(2), 480-490.
Daniel, K., Tripathi, N., Honda, K., & Apisit, E. (2004). Analysis of VNIR (400–1100 nm) spectral signatures for estimation of soil organic matter in tropical soils of Thailand. International Journal of Remote Sensing, 25(3), 643-652.
Demattê, J. A. M. (2002). Characterization and discrimination of soils by their reflected electromagnetic energy. Pesquisa Agropecuária Brasileira, 37(10), 1445-1458
Farifteh, J., Farshad, A., & George, R. (2006). Assessing salt-affected soils using remote sensing, solute modelling, and geophysics. Geoderma, 130(3-4), 191-206.
Gee, G.W., & Bauder, J.W. (1986) Particle‐size analysis Methods of soil analysis: Part 1 Physical and mineralogical methods. 5:383-411.
Gomez,  C.,  P.  Lagacherie,  and  G.  Coulouma.  2008.  Continuum  removal  versus  PLSR method for clay  and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements. Geoderma,148:141-148.
Gomez, C., & 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
Hassani, A., Bahrami, H.A., Noroozi, A.A., & Oustan, Sh., (2014). Visible-near infrared reflectance spectroscopy for assessment of soil properties in gypseous and calcareous soils. Watershed engineering and management, 6(2), 125-138. (In Farsi)
Henderson, T., Baumgardner, M., Franzmeier, D., Stott, D., & Coster, D. (1992). High dimensional reflectance analysis of soil organic matter. Soil Science Society of America Journal, 56(3), 865-872.
Hummel, J. W., Sudduth, K. A., & Hollinger, S. E. (2001). Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor. Computers and electronics in agriculture, 32(2), 149-165.
Hunt, G. R., & Salisbury, J. W. (1971). Visible and near infrared spectra of minerals and rocks. II. Carbonates. Modern Geology, 2, 23-30.
Iran Geology Organization (1995) Neyriz and Estahban map 1:250000. Tehran map publication
Islam, K., Singh, B., & McBratney, A. (2003). Simultaneous estimation of several soil properties by ultra-violet, visible, and near-infrared reflectance spectroscopy. Soil Research, 41(6), 1101-1114.  
Khayamim, F., Khademi, H., Stenberg, B., & Wetterlind, J. (2015a) Capability of vis-NIR Spectroscopy to Predict Selected Chemical Soil Properties in Isfahan Province. JWSS. 19 (72) :81-92. (In Farsi)
Khayamim, F., Wetterlind, J., Khademi, H., Robertson, A. J., Cano, A. F., & Stenberg, B. (2015b). Using visible and near infrared spectroscopy to estimate carbonates and gypsum in soils in arid and subhumid regions of Isfahan, Iran. Journal of Near Infrared Spectroscopy, 23(3), 155-165.
Kim, I., Pullanagari, R., Deurer, M., Singh, R., Huh, K., & Clothier, B. (2014). The use of visible and near‐infrared spectroscopy for the analysis of soil water repellency. European journal of soil science, 65(3), 360-368.
McBratney, A. B., Minasny, B., & Rossel, R. V. (2006). Spectral soil analysis and inference systems: A powerful combination for solving the soil data crisis. Geoderma, 136(1-2), 272-278.
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(5), 960-971.
Mohamed, E.S., Saleh, A.M., Belal, A.B., & Gad, A., (2018). Application of near-infrared reflectance for quantitative assessment of soil properties. Egypt. J. Rem. Sens. Space Sci,  21 (1), 1 – 14.
Mousavi, F., Abdi, E., Ghalandarzadeh, A., Bahrami, H., & Majnounian, B. (2020). Investigating the ability of Visible-NIR spectrometry to estimate some soil properties. Iranian Journal of Forest, 11(4), 443-458.  (in Farsi)
Nawar, S., Buddenbaum, H., Hill, J., Kozak, J., & Mouazen, A. M. (2016). Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy. Soil and Tillage Research, 155, 510-522.
Nelson, R.E. (1982). Carbonate and gypsum. In: Page, A.L. (Ed.), Methods of Soil Analysis. Agron. Monger. vol. 9. ASA and SSSA, Madison, WI, pp. 181–196.
Ostovari, Y., Ghorbani-Dashtaki, S., Bahrami, H.-A., Abbasi, M., Dematte, J. A. M., Arthur, E., & Panagos, P. (2018). Towards prediction of soil erodibility, SOM and CaCO3 using laboratory Vis-NIR spectra: A case study in a semi-arid region of Iran. Geoderma, 314, 102-112.
Page, A., Miller, R., & Keeney, D. (1982). Methods of soil analysis, Part 2: Chemical and microbiological properties 2nd ed. Madison, Wisconsin, USA.
Pinheiro, É. F., Ceddia, M. B., Clingensmith, C. M., Grunwald, S., & Vasques, G. M. (2017). Prediction of soil physical and chemical properties by visible and near-infrared diffuse reflectance spectroscopy in the central Amazon. Remote Sensing, 9(4), 293.
Rasooli, N., Farpoor, M., Khayamim, F., & Ranjbar, H. (2018). Prediction of selected soil properties using visible and near infrared spectroscopy in Bardsir area, Kerman Province. Iranian Journal of Soil Research, 32(2), 231-243. (in Farsi)
Reeves III, J. B., & 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(8), 1472-1481.
Reeves Iii, J., McCarty, G., & Mimmo, T. (2002). The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soils. Environmental pollution, 116, S277-S284.
Rinnan, Å., Van Den Berg, F., & Engelsen, S. B. (2009). Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends in Analytical Chemistry, 28(10), 1201-1222.
Rossel, R. V., McGlynn, R., & McBratney, A. (2006). Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy. Geoderma, 137(1-2), 70-82.
Savitzky, A., & Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry, 36(8), 1627-1639.
Seifi, M., Ahmadi, A., Neyshabouri, M.-R., Taghizadeh-Mehrjardi, R., & Bahrami, H.-A. (2020). Remote and Vis-NIR spectra sensing potential for soil salinization estimation in the eastern coast of Urmia hyper saline lake, Iran. Remote Sensing Applications: Society and Environment, 20, 100398.
Shahrayini, E., Noroozi, A., & Eghbal, M. K. (2020). Prediction of Soil Properties by Visible and Near-Infrared Reflectance Spectroscopy. Eurasian Soil Science, 53(12), 1760-1772.
Stenberg, B. (2010). Effects of soil sample pretreatments and standardised rewetting as interacted with sand classes on Vis-NIR predictions of clay and soil organic carbon. Geoderma, 158(1-2), 15-22.
Stenberg, B., Rossel, R. A. V., Mouazen, A. M., & Wetterlind, J. (2010). Visible and near infrared spectroscopy in soil science. Advances in agronomy, 107, 163-215.
Summers, D., Lewis, M., Ostendorf, B., & 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.,  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.
Walkley, A., & Black, I. A. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil science, 37(1), 29-38.
Wang, J., Ding, J., Abulimiti, A., & Cai, L. (2018). Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China. PeerJ, 6, e4703.
Williams, P., Dardenne, P., & Flinn, P. (2017). Tutorial: Items to be included in a report on a near infrared spectroscopy project. Journal of Near Infrared Spectroscopy, 25(2), 85-90.
Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and intelligent laboratory systems, 58(2), 109-130.
Xie, X.-L., & Li, A.-B. (2016). Improving spatial estimation of soil organic matter in a subtropical hilly area using covariate derived from vis-NIR spectroscopy. Biosystems engineering, 152, 126-137.
Yao, X., Huang, Y., Shang, G., Zhou, C., Cheng, T., Tian, Y., . . . Zhu, Y. (2015). Evaluation of six algorithms to monitor wheat leaf nitrogen concentration. Remote Sensing, 7(11), 14939-14966.
Yitagesu, F. A., van der Werff, H., van der Meer, F., & Hecker, C. (2012). On the relationship between plasticity and spectral characteristics of swelling soils: The 3–5 μm wavelength region. Applied clay science, 69, 67-78.