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Spectral characterization and estimation of soil properties formed on different parent materials with VNIRS technique for forensic science

Year 2021, , 497 - 513, 25.12.2021
https://doi.org/10.29050/harranziraat.931045

Abstract

Soils differ significantly depending on the parent material, climatic conditions and topography where they are formed. Identifying these differences which also are important in forensic science requires more field and laboratory work, which requires more labor, chemical use and time. On the other hand, approaches such as Visible and Near Infrared Spectroradiometry (VNIRS) Technique have recently begun to be widely used in the rapid characterization of soils and simultaneous characterization of multiple soil properties. This method allows non-destructive analysis of soil samples; It has great potential for forensic uses where preservation of original specimens is of great importance. In this study which was performed between years of 2019 and 2020, 59 soil samples were taken on a horizon basis from soil profiles formed on four
different main materials in Sanliurfa, including Mud streams, Limestone, Marl and Basalt; Routine physicochemical analyzes were performed in the laboratory and spectral reflections in the 350-2500 nm wavelength range were obtained and subjected to multivariate statistical methods such as Principal Component Analysis (PCA), Cluster analysis and PLSR. The predictability success of soil parameters using PLSR models was tested with the cross-validation approach. PC1 and PC2 explained more than 99% of the change in reflections of soils and were able to group different soils formed on different material according to their spectral properties. Clustering analysis using the PCA analysis applied raw soil reflections was able to classify the soils according to the origin type with 61% success (Kappa statistic = 0.62). According to the cross validation results, parameters such as CaCO3 (R2 = 0.75, RPD = 1.99), clay (R2 = 0.72, RPD = 1.87), Fe (R2 = 0.66, RPD = 1.72), Al (R2 = 0.64, RPD = 1.64), exchangeable Ca (R2 = 0.73, RPD = 2.03), Na (R2 = 0.65, RPD = 1.71) and Mg (R2 = 0.59, RPD = 1.55)could be predicted moderately successfully with spectral reflections.

References

  • Allison, L. E., & Moodie, C. D. (1965). Carbonate. In A.G. Norman (Ed.), Methods of Soil Analysis: Part 2 Chemical and Microbiological Properties, (pp. 1379-1396), Second Edition. Agronomy, No. 9, Part 2, American Society of Agronomy, Soil Science Society of America, Madison, Wl.
  • Başbozkurt, H., Öztaş, T., Karaibrahimoğlu , A., Gündoğan , A., & Genç, A. (2013). Toprak Özelliklerinin Mekânsal Değişim Desenlerinin Jeoistatistiksel Yöntemlerle Belirlenmesi. Atatürk Üniv. Ziraat Fak. Derg., 44 (2): 169-181.
  • Bellinaso, H., Demattê, J. A. M., & Romeiro, S. A. (2010). Soil spectral library and its use in soil classification. Revista Brasileira de Ciência do Solo, 34(3), 861-870.
  • Bendor, E., & Banin, A. (1995). Near-infrared analysis (NIRA) as a method to simultaneously evaluate spectral featureless constitutes in soils. Soil Science, 159(4), 259-270.
  • Brejda, J. J., Karlen, D. L., Smith, J. L., & Allan, D. L. (2000). Identification of regional soil quality factors and indicators II. Northern Mississippi Loess Hills and Palouse Prairie. Soil Science Society of America Journal, 64, 2125-2135.
  • Bilgili, A., Çullu, M., & Aydemir, S. (2014). Tuzdan Etkilenmiş Toprakların Yakın Kızılötesi Yansıma Spektroradyometre Ve Elektromanyetik İndüksiyon Tekniği Yardımıyla Karakterize Edilebilme Potansiyelinin Araştırılması. Harran Tarım ve Gıda Bilimleri Dergisi, 18(1), 33-46.
  • Bogrekci, İ., & Lee, W.S. (2007). Comparison of Ultraviolet, Visible, and Near Infrared Sensing for Soil Phosphorus. Biosystem Engineering, 96 (2), 293-299.
  • Brown, D.J., Shepherd, K.D., Walsh, M.G., Mays, M.D., & Reinsch, T.G. (2006). Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma, 132, 273–290.
  • Cambardella C.A., Moorman T.B., Novak J.M., Parkin T.B., Karlen D.L., Turco R.F. & Konopka, A.E. (1994). Field scale variability soil properties in central Iowa soils. Soil Science Society America Journal, 58:1501-1511.
  • Chang, C.W., Laird, D., Mausbach, M.J., & 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.
  • Clark, R.N., & Roush, T.L. (1984). Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. Journal of Geophysical Research: Solid Earth, 89(B7), 6329-6340.
  • Dalal, R.C., & Henry, R.J. (1986). Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry. Soil Science Society of America Journal, 50(1), 120-123.
  • Demattê, J.A., Campos, R.C., Alves, M.C., Fiorio, P.R., & Nanni, M R. (2004). Visible–NIR reflectance: a new approach on soil evaluation. Geoderma, 121(1-2), 95-112.
  • Dunn, A.J. (2002). Survey of Legislation, Agricultural Law. University of Arkansas at Little Rock. Law Review.
  • Fang, Y.M., Zhu, B.K., Zhu, D., Christie, P., Ke, X., & Zhu, Y. G. (2018). Exposure to nanoplastics disturbs the gut microbiome in the soil oligochaete Enchytraeus crypticus. Environmental Pollution, 239, 408-415.
  • Farifteh, J., Farshad, A., & George, R.J. (2006). Assessing salt-affected soils using remote sensing, solute modelling, and geophysics. Geoderma, 130(3-4), 191-206.
  • Fang, Q., Hong, H., Zhao, L., Kukolich, S., Yin, K., & Wang, C. (2018). Visible and near-infrared reflectance spectroscopy for investigating soil mineralogy: A review. Journal of Spectroscopy, 2018,1-14.
  • Gee, G.W. & Bauder J.W. (1986). Particle-size Analysis. In A. Klute (Ed.) Methods of Soil Analysis Part 1. (pp. 383-411). Soil Science Society of America Book Series 5, Madison, Wisconsin, USA..
  • Güzel, Ş.G. (2017). Harran Ovası topraklarının bazı özelliklerinin jeoistatistiksel (kriging-method) yöntemle belirlenerek haritalanması/Determination and mapping of some soil properties of Harran Plain by geostatistic method (Kriging) (Doctoral dissertation).
  • Howari, F. M., Goodell, P. C., & Miyamoto, S. (2002). Spectral properties of salt crusts formed on saline soils. Journal of Environmental Quality, 31(5), 1453-1461.
  • Jackson, M. L. (1958). Soil chemical analysis. Verlag: Prentice Hall. Inc., Englewood Cliffs, NJ. Janzen, H. H. (1993). Soluble salts. In Carter, M.R. (Ed.), Soil sampling and methods of analysis.(pp. 161–166). CRC Press Inc., Florida.
  • Keskin, M., & Görücü K. S., (2012). Hassas Tarım Teknolojileri. Hatay. Mustafa Kemal Üniversitesi Yayınları, (35), 210.
  • Kooistra, L., Wehrens, R., Leuven, R.S.E.W., & Buydens, L.M.C., (2001). Possibilities of visible?near-infrared spectroscopy for the assessment of soil contamination in river floodplains. Anal. Chim. Acta, 446, 97–105.
  • Landis, J. R., & Koch, G. G. (1977). An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics, 363-374.
  • Lazaar, A., Mouazen, A. M., Hammouti, K. E., Fullen, M., Pradhan, B., Memon, M. S., & Monir, A. (2020). The application of proximal visible and near-infrared spectroscopy to estimate soil organic matter on the Triffa Plain of Morocco. International Soil and Water Conservation Research, 8(2), 195-204.
  • Miloš, B. & Bensa, A. (2017). VIS-NIR spektroskopisi kullanılarak toprak organik karbonunun tahmini: Hırvatistan'dan Kırmızı Akdeniz topraklarına uygulama. Eurasian Journal of Soil Science, 6 (4), 365-373.
  • Miloš, B. & Bensa, A. (2018). Tarımsal topraklarda organik karbon ve kalsiyum karbonatların Vis-NIR spektroskopisi ile tahmini. Poljoprivreda , 24 (1), 45-51.
  • Nelson, D. W., & Sommers, L. E. (1982). Total Carbon, Organic Carbon, and Organic Matter, In A.L. Page (Ed.), Methods of soil analysis. Part 2 Chemical and Microbiological Properties (pp. 539-579). Second Edition. Agronomy, No. 9, Part 2, American Society of Agronomy, Soil Science Society of America, Madison, Wl.
  • Oliveira, J.F., Brossard, M., Vendrame, P.R.S., Mayi, S., Corazza, E.J., Marchao, L., & Guimaraes, M.F. (2013). Soil discrimination using diffuse reflectance Vis-NIR spectroscopy in a local toposequence. Comptes Rendus Geoscience, 345, 446-453.
  • Oliveira, M. N., Santos, T. M., Vale, H. M., Delvaux, J. C., Cordero, A. P., Ferreira, A. B., ... & Borges, A. C. (2013). Endophytic microbial diversity in coffee cherries of Coffea arabica from southeastern Brazil. Canadian journal of microbiology, 59(4), 221-230.
  • Özgüven, M. M. (2018). Hassas tarım. Akfon Yayınları, Ankara.
  • Poppiel, R.R., Lacerda, M.P.C., Oliveira Junior, M.P., Demattê, J.A.M., Romero, D.J., Sato, M.V., Almeida Júnior, L.R., & Cassol, L.F.M. (2018). Surface spectroscopy of Oxisols, Entisols and Inceptisol and relationships with selected soil properties. Rev Bras Cienc Solo, 42:e0160519.
  • Şenol, H., & Akgül, M. (2013). Farklı Sıcaklık ve Nem Rejimleri ile Farklı Jeolojik Ana Materyal Üzerindeki Toprakların Oluşumu ve Mineralojisi. Süleyman Demirel Üniversitesi Ziraat Fakültesi Dergisi, 8(1), 41-52.
  • Tekin, Y., & Tümsavaş, Z. (2012).Toprak Özelliklerinin Belirlenmesinde Spektrofotometrik Yansımalardan Yararlanma Olanakları. U.Ü. Ziraat Fakültesi Dergisi, 26 (2), 37-45.
  • Thomas, G. W. (1983). Exchangeable Cations. In A.L. Page (Ed.), Methods of Soil Analysis, Part 2, Chemical and Microbiological Properties, (pp.159-165). Second Edition. Agronomy, No. 9, Part 2, American Society of Agronomy, Soil Science Society of America, Madison, Wl.
  • Ting, H., Jing, W., Zongjian, L., & Ye,C. (2009). Spectral Features of Soil Organic Matter. Geo-spatial Information Science, 12 (1), 33-40.
  • Turgut, B., & Öztaș, T. (2012). Assessment of spatial distribution of some soil properties with geostatistics method. Ziraat Fakültesi Dergisi-Süleyman Demirel Üniversitesi, 7(2), 10-22.
  • Tümsavaş, Z., Tekin, Y., Ulusoy, Y., & Mouazen, A. M. (2017). Prediction of Soil Sand and Clay Contents via Visible and Near-Infrared (Vis-NIR) Spectroscopy. In Intelligent Environments 2017: Workshop Proceedings of the 13th International Conference on Intelligent Environments (Vol. 22, p. 29). IOS Press.
  • U.S. Salinity Laboratory Staff. (1954). Diagnosis and improvement of saline and alkali soils. USDA Agric. Handb. 60. U.S. Gov. Print. Office, Washington, DC.
  • Wetterlind, J., Stenberg, B., & Rossel, R. A. V. (2013). Soil analysis using visible and near infrared spectroscopy. Methods Mol Biol, 2013;953:95-107.
  • Zhang, X., Sun, X., Sun, Y., Sun, W., & Cen, Y. (2018). Predicting nickel concentration in soil using reflectance spectroscopy associated with organic matter and clay minerals. Geoderma, 327, 25-35.

Farklı ana materyal üzerinde oluşmuş toprakların adli bilim için VNIRS tekniği ile spektral karakterizasyonu ve özelliklerinin tahmin edilmesi

Year 2021, , 497 - 513, 25.12.2021
https://doi.org/10.29050/harranziraat.931045

Abstract

Topraklar üzerinde oluştukları ana materyale, iklim koşulları ve topoğrafik yapıya bağlı olarak önemli farklılıklar gösterirler. Adli bilimde de önemli olan bu farklılıkların belirlenmesi daha fazla arazi ve laboratuvar çalışmalarına ihtiyaç duyar bu da daha fazla iş gücü, kimyasal kullanımı ve zaman gerektirir. Öteki taraftan Görülebilir ve Yakın Kızılötesi Spektroradyometre (VNIRS) Tekniği gibi yaklaşımlar toprakların hızlıca ve birden fazla toprak özelliğinin eş zamanlı olarak karakterizasyonunda son zamanlarda yaygın bir şekilde kullanılmaya başlanmıştır. Toprak örneklerinin tahribatsız analiz edilmesine izin veren bu yöntem; orijinal örneklerin korunmasının büyük önem taşıdığı adli kullanımlar için büyük potansiyele sahiptir. 2019-2020 yılları arasında yürütülen bu çalışmada Çamur akıntıları, Kireçtaşı, Marn ve Bazalt olmak üzere Şanlıurfa’da yaygın dört farklı ana materyal üzerinde oluşmuş toprak profillerden horizon esasına göre alınan 59 toprak örneği; laboratuvar ortamında rutin fiziko kimyasal analizleri yapılmış ve 350-2500 nm dalga boyu aralığında spektral yansımaları elde edilerek Temel Bileşenler Analizi (PCA), Cluster analizi ve Kısmi En Küçük Karaler Regresyon Yöntemi (PLSR)gibi çok değişkenli istatistiksel metotlara tabi tutulmuştur. PLSR modelleri kullanarak toprak parametrelerinin tahmin edilebilirlik başarısı
çapraz doğrulama (crossvalidation) yaklaşımı ile test edilmiştir. PC1 ve PC2 toprakların yansımalarındaki değişimin % 99’ undan fazlasını açıklamış ve farklı ana meteryal üzerinde oluşmuş farklı toprakları spektral özelliklerine göre gruplandırabilmiştir. PCA analizi uygulanmış ham toprak yansımalarını kullanan Kümeleme analizi toprakları %61 başarı (Kappa istatistik = 0.62) ile geldikleri ana metaryal türüne göre sınıflandırabilmiştir. Çapraz doğrulama sonuçlarına göre CaCO3 (R2 =0.75, RPD=1.99), kil (R2= 0.72, RPD=1.87), Fe (R2=0.66, RPD=1.72), Al (R2=0.64, RPD=1.64), değişebilir Ca (R2= 0.73, RPD= 2.03), Na (R2=0.65, RPD= 1.71) ve Mg (R2=0.59, RPD= 1.55) gibi parametreler spektral yansımalara bağlı olarak orta seviyede başarılı bir şekilde tahmin edilebilmiştir.

References

  • Allison, L. E., & Moodie, C. D. (1965). Carbonate. In A.G. Norman (Ed.), Methods of Soil Analysis: Part 2 Chemical and Microbiological Properties, (pp. 1379-1396), Second Edition. Agronomy, No. 9, Part 2, American Society of Agronomy, Soil Science Society of America, Madison, Wl.
  • Başbozkurt, H., Öztaş, T., Karaibrahimoğlu , A., Gündoğan , A., & Genç, A. (2013). Toprak Özelliklerinin Mekânsal Değişim Desenlerinin Jeoistatistiksel Yöntemlerle Belirlenmesi. Atatürk Üniv. Ziraat Fak. Derg., 44 (2): 169-181.
  • Bellinaso, H., Demattê, J. A. M., & Romeiro, S. A. (2010). Soil spectral library and its use in soil classification. Revista Brasileira de Ciência do Solo, 34(3), 861-870.
  • Bendor, E., & Banin, A. (1995). Near-infrared analysis (NIRA) as a method to simultaneously evaluate spectral featureless constitutes in soils. Soil Science, 159(4), 259-270.
  • Brejda, J. J., Karlen, D. L., Smith, J. L., & Allan, D. L. (2000). Identification of regional soil quality factors and indicators II. Northern Mississippi Loess Hills and Palouse Prairie. Soil Science Society of America Journal, 64, 2125-2135.
  • Bilgili, A., Çullu, M., & Aydemir, S. (2014). Tuzdan Etkilenmiş Toprakların Yakın Kızılötesi Yansıma Spektroradyometre Ve Elektromanyetik İndüksiyon Tekniği Yardımıyla Karakterize Edilebilme Potansiyelinin Araştırılması. Harran Tarım ve Gıda Bilimleri Dergisi, 18(1), 33-46.
  • Bogrekci, İ., & Lee, W.S. (2007). Comparison of Ultraviolet, Visible, and Near Infrared Sensing for Soil Phosphorus. Biosystem Engineering, 96 (2), 293-299.
  • Brown, D.J., Shepherd, K.D., Walsh, M.G., Mays, M.D., & Reinsch, T.G. (2006). Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma, 132, 273–290.
  • Cambardella C.A., Moorman T.B., Novak J.M., Parkin T.B., Karlen D.L., Turco R.F. & Konopka, A.E. (1994). Field scale variability soil properties in central Iowa soils. Soil Science Society America Journal, 58:1501-1511.
  • Chang, C.W., Laird, D., Mausbach, M.J., & 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.
  • Clark, R.N., & Roush, T.L. (1984). Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. Journal of Geophysical Research: Solid Earth, 89(B7), 6329-6340.
  • Dalal, R.C., & Henry, R.J. (1986). Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry. Soil Science Society of America Journal, 50(1), 120-123.
  • Demattê, J.A., Campos, R.C., Alves, M.C., Fiorio, P.R., & Nanni, M R. (2004). Visible–NIR reflectance: a new approach on soil evaluation. Geoderma, 121(1-2), 95-112.
  • Dunn, A.J. (2002). Survey of Legislation, Agricultural Law. University of Arkansas at Little Rock. Law Review.
  • Fang, Y.M., Zhu, B.K., Zhu, D., Christie, P., Ke, X., & Zhu, Y. G. (2018). Exposure to nanoplastics disturbs the gut microbiome in the soil oligochaete Enchytraeus crypticus. Environmental Pollution, 239, 408-415.
  • Farifteh, J., Farshad, A., & George, R.J. (2006). Assessing salt-affected soils using remote sensing, solute modelling, and geophysics. Geoderma, 130(3-4), 191-206.
  • Fang, Q., Hong, H., Zhao, L., Kukolich, S., Yin, K., & Wang, C. (2018). Visible and near-infrared reflectance spectroscopy for investigating soil mineralogy: A review. Journal of Spectroscopy, 2018,1-14.
  • Gee, G.W. & Bauder J.W. (1986). Particle-size Analysis. In A. Klute (Ed.) Methods of Soil Analysis Part 1. (pp. 383-411). Soil Science Society of America Book Series 5, Madison, Wisconsin, USA..
  • Güzel, Ş.G. (2017). Harran Ovası topraklarının bazı özelliklerinin jeoistatistiksel (kriging-method) yöntemle belirlenerek haritalanması/Determination and mapping of some soil properties of Harran Plain by geostatistic method (Kriging) (Doctoral dissertation).
  • Howari, F. M., Goodell, P. C., & Miyamoto, S. (2002). Spectral properties of salt crusts formed on saline soils. Journal of Environmental Quality, 31(5), 1453-1461.
  • Jackson, M. L. (1958). Soil chemical analysis. Verlag: Prentice Hall. Inc., Englewood Cliffs, NJ. Janzen, H. H. (1993). Soluble salts. In Carter, M.R. (Ed.), Soil sampling and methods of analysis.(pp. 161–166). CRC Press Inc., Florida.
  • Keskin, M., & Görücü K. S., (2012). Hassas Tarım Teknolojileri. Hatay. Mustafa Kemal Üniversitesi Yayınları, (35), 210.
  • Kooistra, L., Wehrens, R., Leuven, R.S.E.W., & Buydens, L.M.C., (2001). Possibilities of visible?near-infrared spectroscopy for the assessment of soil contamination in river floodplains. Anal. Chim. Acta, 446, 97–105.
  • Landis, J. R., & Koch, G. G. (1977). An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics, 363-374.
  • Lazaar, A., Mouazen, A. M., Hammouti, K. E., Fullen, M., Pradhan, B., Memon, M. S., & Monir, A. (2020). The application of proximal visible and near-infrared spectroscopy to estimate soil organic matter on the Triffa Plain of Morocco. International Soil and Water Conservation Research, 8(2), 195-204.
  • Miloš, B. & Bensa, A. (2017). VIS-NIR spektroskopisi kullanılarak toprak organik karbonunun tahmini: Hırvatistan'dan Kırmızı Akdeniz topraklarına uygulama. Eurasian Journal of Soil Science, 6 (4), 365-373.
  • Miloš, B. & Bensa, A. (2018). Tarımsal topraklarda organik karbon ve kalsiyum karbonatların Vis-NIR spektroskopisi ile tahmini. Poljoprivreda , 24 (1), 45-51.
  • Nelson, D. W., & Sommers, L. E. (1982). Total Carbon, Organic Carbon, and Organic Matter, In A.L. Page (Ed.), Methods of soil analysis. Part 2 Chemical and Microbiological Properties (pp. 539-579). Second Edition. Agronomy, No. 9, Part 2, American Society of Agronomy, Soil Science Society of America, Madison, Wl.
  • Oliveira, J.F., Brossard, M., Vendrame, P.R.S., Mayi, S., Corazza, E.J., Marchao, L., & Guimaraes, M.F. (2013). Soil discrimination using diffuse reflectance Vis-NIR spectroscopy in a local toposequence. Comptes Rendus Geoscience, 345, 446-453.
  • Oliveira, M. N., Santos, T. M., Vale, H. M., Delvaux, J. C., Cordero, A. P., Ferreira, A. B., ... & Borges, A. C. (2013). Endophytic microbial diversity in coffee cherries of Coffea arabica from southeastern Brazil. Canadian journal of microbiology, 59(4), 221-230.
  • Özgüven, M. M. (2018). Hassas tarım. Akfon Yayınları, Ankara.
  • Poppiel, R.R., Lacerda, M.P.C., Oliveira Junior, M.P., Demattê, J.A.M., Romero, D.J., Sato, M.V., Almeida Júnior, L.R., & Cassol, L.F.M. (2018). Surface spectroscopy of Oxisols, Entisols and Inceptisol and relationships with selected soil properties. Rev Bras Cienc Solo, 42:e0160519.
  • Şenol, H., & Akgül, M. (2013). Farklı Sıcaklık ve Nem Rejimleri ile Farklı Jeolojik Ana Materyal Üzerindeki Toprakların Oluşumu ve Mineralojisi. Süleyman Demirel Üniversitesi Ziraat Fakültesi Dergisi, 8(1), 41-52.
  • Tekin, Y., & Tümsavaş, Z. (2012).Toprak Özelliklerinin Belirlenmesinde Spektrofotometrik Yansımalardan Yararlanma Olanakları. U.Ü. Ziraat Fakültesi Dergisi, 26 (2), 37-45.
  • Thomas, G. W. (1983). Exchangeable Cations. In A.L. Page (Ed.), Methods of Soil Analysis, Part 2, Chemical and Microbiological Properties, (pp.159-165). Second Edition. Agronomy, No. 9, Part 2, American Society of Agronomy, Soil Science Society of America, Madison, Wl.
  • Ting, H., Jing, W., Zongjian, L., & Ye,C. (2009). Spectral Features of Soil Organic Matter. Geo-spatial Information Science, 12 (1), 33-40.
  • Turgut, B., & Öztaș, T. (2012). Assessment of spatial distribution of some soil properties with geostatistics method. Ziraat Fakültesi Dergisi-Süleyman Demirel Üniversitesi, 7(2), 10-22.
  • Tümsavaş, Z., Tekin, Y., Ulusoy, Y., & Mouazen, A. M. (2017). Prediction of Soil Sand and Clay Contents via Visible and Near-Infrared (Vis-NIR) Spectroscopy. In Intelligent Environments 2017: Workshop Proceedings of the 13th International Conference on Intelligent Environments (Vol. 22, p. 29). IOS Press.
  • U.S. Salinity Laboratory Staff. (1954). Diagnosis and improvement of saline and alkali soils. USDA Agric. Handb. 60. U.S. Gov. Print. Office, Washington, DC.
  • Wetterlind, J., Stenberg, B., & Rossel, R. A. V. (2013). Soil analysis using visible and near infrared spectroscopy. Methods Mol Biol, 2013;953:95-107.
  • Zhang, X., Sun, X., Sun, Y., Sun, W., & Cen, Y. (2018). Predicting nickel concentration in soil using reflectance spectroscopy associated with organic matter and clay minerals. Geoderma, 327, 25-35.
There are 41 citations in total.

Details

Primary Language Turkish
Subjects Soil Sciences and Ecology
Journal Section Araştırma Makaleleri
Authors

Yüsra İnci 0000-0002-9740-0013

Ali Volkan Bilgili 0000-0002-4727-8283

Recep Gündoğan 0000-0001-8877-1130

Publication Date December 25, 2021
Submission Date May 1, 2021
Published in Issue Year 2021

Cite

APA İnci, Y., Bilgili, A. V., & Gündoğan, R. (2021). Farklı ana materyal üzerinde oluşmuş toprakların adli bilim için VNIRS tekniği ile spektral karakterizasyonu ve özelliklerinin tahmin edilmesi. Harran Tarım Ve Gıda Bilimleri Dergisi, 25(4), 497-513. https://doi.org/10.29050/harranziraat.931045

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