امکان‏سنجی برآورد شکل‏های شیمیایی آهن در خاک‌های جنگلی آکاسیاویکتوریا با روش طیف‏سنجی مرئی و مادون ‏قرمز نزدیک

Ba, Y., Liu, J., Han, J., & Zhang, X. (2020). Application of Vis-NIR spectroscopy for the determination the content of organic matter in saline-alkali soils. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 229, 117863. https://doi.org/10.1016/j.saa.2019.117863

Banaei, M. (1998). Soil moisture and temperature regime map of Iran. Soil and Water Research Institute, Ministry of Agriculture, Iran.

Coblinski, J. A., Inda, A. V., Dematte, J. A. M., Dotto, A. C., Gholizadeh, A., & Giasson, E. (2021). Identification of minerals in subtropical soils with different textural classes by VIS-NIR-SWIR reflectance spectroscopy. Catena. 203, 105334. https://doi.org/10.1016/j.catena.2021.105334

Conforti, M., Matteucci, G., & Buttafuoco, G. (2018). Using laboratory Vis-NIR spectroscopy for monitoring some forest soil properties. Journal of Soils and Sediments, 18(3), 1009–1019.

Fang, Q., Hong, H., Zhao, L., Kukolich, S., Yin, K., & Wang, C. (2018). Visible and nearinfrared reflectance spectroscopy for investigating soil mineralogy: a review. Journal of Spectroscopy. 2018, 3168974. https://doi.org/10.1155/2018/3168974

Gee, G. W., & Bauder, J. W. (1986). Particle size analysis, hydrometer methods. In A. Klute (Ed.), Methods of soil analysis: Part 1. Physical and mineralogical methods (pp. 383–411). American Society of Agronomy and Soil Science Society of America.

General Directorate of Natural Resources and Watershed Management of Fars Province. (2021). Studies on the management of desert plantation forests in the Golkoyeh region of Zarrin Dasht County, Desert Affairs Department. Fajr Water and Civil Engineering Consulting Engineers. (In Persian).

Gomez, C., Lagacherie, P., & Coulouma, G. (2012). Regional predictions of eight common soil properties and their spatial structures from hyperspectral Vis–NIR data. Geoderma, 189–190, 176–185. https://doi.org/10.1016/j.geoderma.2012.05.023

Gyeongcheol, C., Heungsun, H., & Marko, S. (2022). A comparative evaluation of factor- and component-based structural equation modelling approaches under (in)correct construct representations. British Journal of Mathematical and Statistical Psychology, 75(2), 220–251. https://doi.org/10.1111/bmsp.12255

Han, I. S., Han, C., & Chung, C. B. (2005). Melt index modeling with support vector machines, partial least squares, and artificial neural networks. Journal of Applied Polymer Science, 95(4), 967–974. https://doi.org/10.1002/app.20979

Heil, J., Haring, V., Marschner, B., & Stumpe, B. (2019). Advantages of fuzzy k-means over k-means clustering in the classification of diffuse reflectance soil spectra: A case study with West African soils. Geoderma, 337, 11–21. https://doi.org/10.1016/j.geoderma.2018.09.004

Hermansen, C., Knadel, M., Moldrup, P., Greve, M. H., Gislum, R., & de Jonge, L. W. (2016). Visible–near-infrared spectroscopy can predict the clay/organic carbon and mineral fines/organic carbon ratios. Soil Science Society of America Journal, 80(6), 1486–1495. https://doi.org/10.2136/sssaj2016.05.0159

Hobley, E. U., & Prater, I. (2019). Estimating soil texture from vis-NIR spectra. European Journal of Soil Science, 70(1), 83–95. https://doi.org/10.1111/ejss.12733

Hong, Y., Yu, L., Chen, Y., Liu, Y., Liu, Y., Liu, Y., & Cheng, H. (2017). Prediction of soil organic matter by VIS-NIR spectroscopy using normalized soil moisture index as a proxy of soil moisture. Remote Sensing, 10(1), 28. https://doi.org/10.3390/rs10010028

Hosseini, E., Zarei, M., Moosavi, A., Ghasemi Fasaei, R., Baghernejad, M., & Mozaffari, H. (2024). Feasibility of Vis-NIR spectroscopy approach to predict soil biological attributes in arid land soils. PLOS ONE, 19(9), e0311122. https://doi.org/10.1371/journal.pone.0311122

Kanchan, S. K., Ratnadeep, R. D., Pooja, V. J., & Jaypalsing, N. (2018). Hyper spectral analysis of soil iron oxide using fieldspec4 spectroradiometer. International Journal of Computer Sciences and Engineering, 6(11), 395–399. https://doi.org/10.26438/ijcse/v6i11.395399

Karami, A., Moosavi, A. A., Pourghasemi, H. R., Ronaghi, A., Ghasemi Fasaei, R., Vidal, E., & Lado, M. (2024). Proximal sensing approach for characterization of calcareous soils using multiblock data analysis. Geoderma Regional, 36, e00752. https://doi.org/10.1016/j.geodrs.2023.e00752

Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems, 12(1), 752–780. https://doi.org/10.17705/1CAIS.01250

Lijuan, M., Yanling, P., Yanfang, P., Jingqi, Z., Haoran, S., Junjie, C., Yanjiang, Q., & Zhisheng, W. (2019). Systematic discovery about NIR spectral assignment from chemical structural property to natural chemical compounds. Scientific Reports, 9, 9503. https://doi.org/10.1038/s41598-019-45945-y

Loeppert, R. H., & Suarez, D. L. (1996). Carbonate and gypsum. In D. L. Sparks, A. L. Page, P. A. Helmke, R. H. Loeppert, P. N. Soltanpour, M. A. Tabatabai, C. T. Johnston, & M. E. Sumner (Eds.), Methods of soil analysis: Part 3. Chemical and microbiological properties (pp. 417–436). American Society of Agronomy and Soil Science Society of America.

Mozaffari, H., Moosavi, A. A., & Cornelis, W. (2023). Vis-NIR-spectroscopy- and loss-on-ignition-based functions to estimate organic matter content of calcareous soils. Archives of Agronomy and Soil Science, 69(6), 962–980. https://doi.org/10.1080/03650340.2022.2047944

Mozaffari, H., Moosavi, A. A., Forouzan, S., & Abbasi, S. (2026). Innovative approach for determining polypropylene microplastics pollution in calcareous soils: Vis-NIR spectroscopy. Journal of Hazardous Materials Advances, 21, 101008. https://doi.org/10.1016/j.hazadv.2026.101008

Mozaffari, H., Moosavi, A. A., & Ostovari, Y. (2024a). Feasibility of proximal sensing for predicting soil loss tolerance. Catena, 247, 108503. https://doi.org/10.1016/j.catena.2024.108503

Mozaffari, H., Moosavi, A. A., Nematollahi, M. A., & Ostovari, Y. (2024b). Potential of spectroscopy-based approaches for predicting soil erosion-related parameters: A short review. In H. R. Pourghasemi & N. Kariminejad (Eds.), Advanced tools for studying soil erosion processes (pp. 397–411). Elsevier. https://doi.org/10.1016/B978-0-443-22262-7.00013-8

Mozaffari, H., Moosavi, A. A., Ostovari, Y., & Cornelis, W. (2022a). Comparing visible-near-infrared spectroscopy with classical regression pedotransfer functions for predicting near-saturated and saturated hydraulic conductivity of calcareous soils. Journal of Hydrology, 613, 128412. https://doi.org/10.1016/j.jhydrol.2022.128412

Mozaffari, H., Moosavi, A. A., Ostovari, Y., Nematollahi, M. A., & Rezaei, M. (2022b). Developing spectrotransfer functions (STFs) to predict basic physical and chemical properties of calcareous soils. Geoderma, 428, 116174. https://doi.org/10.1016/j.geoderma.2022.116174

Munawar, A. A., Yunus, Y., & Devianti, S. P. (2020). Calibration models database of near infrared spectroscopy to predict agricultural soil fertility properties. Data in Brief, 30, 105469. https://doi.org/10.1016/j.dib.2020.105469

Nawar, S., & Mouazen, A. M. (2017). Comparison between random forests, artificial neural networks and gradient boosted machines methods of on-line Vis-NIR spectroscopy measurements of soil total nitrogen and total carbon. Sensors, 17(10), 2428. https://doi.org/10.3390/s17102428

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. https://doi.org/10.1016/j.still.2015.07.021

Nocita, M., Stevens, A., Toth, G., Panagos, P., van Wesemael, B., & Montanarella, L. (2014). Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach. Soil Biology and Biochemistry, 68, 337–347. https://doi.org/10.1016/j.soilbio.2013.10.022

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. https://doi.org/10.1016/j.geoderma.2017.11.014

Park, T. C., Kim, U. S., Kim, L. H., Jo, B. W., & Yeo, Y. K. (2010). Heat consumption forecasting using partial least squares, artificial neural network and support vector regression techniques in district heating systems. Korean Journal of Chemical Engineering, 27(4), 1063–1071. https://doi.org/10.1007/s11814-010-0220-9

Pinheiro, E. F. M., 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. https://doi.org/10.3390/rs9040293

Pyo, J. C., Hong, S. M., Kwon, Y. S., Kim, M. S., & Cho, K. H. (2020). Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil. Science of the Total Environment, 741, 140162. https://doi.org/10.1016/j.scitotenv.2020.140162

Qi, L., Weifang, H., Linfeng, L., & Yichun, L. (2022). Interactions between organic matter and Fe oxides at soil micro-interfaces: Quantification, associations, and influencing factors. Science of the Total Environment, 855, 158710. https://doi.org/10.1016/j.scitotenv.2022.158710

Raoult, N., Delorme, B., Ottle, C., Peylin, P., Bastrikov, V., Maugis, P., & Polcher, J. (2018). Confronting soil moisture dynamics from the ORCHIDEE land surface model with the ESA-CCI product: Perspectives for data assimilation. Remote Sensing, 10, 1786. https://doi.org/10.3390/rs10111786

Rhoades, J. D. (1996). Salinity: Electrical conductivity and total dissolved salts. In D. L. Sparks, A. L. Page, P. A. Helmke, R. H. Loeppert, P. N. Soltanpour, M. A. Tabatabai, C. T. Johnston, & M. E. Sumner (Eds.), Methods of soil analysis: Part 3. Chemical and microbiological properties (pp. 417–436). American Society of Agronomy and Soil Science Society of America.

Saidi, S., Ayoubi, S., Shirvani, M., Mireei, S. A., Ge, Y., Zhao, K., & Cerda, A. (2025). Use of Vis-NIR reflectance spectroscopy for estimating soil phosphorus sorption parameters at the watershed scale. Soil and Tillage Research, 248, 106460. https://doi.org/10.1016/j.still.2025.106460

Savitzky, A., & Golay, J. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627–1639. https://doi.org/10.1021/ac60214a047

Singh, J. P., Karwasra, S. P. S., & Singh, M. (1988). Distribution and forms of copper, iron, manganese, and zinc in calcareous soils of India. Soil Science, 146(5), 359–366. https://doi.org/10.1097/00010694-198811000-00008

Singh, K., Majeed, I., Panigrahi, N., Vasava, H. B., Fidelis, C., Karunaratne, S., Bapiwai, P., Yinil, D., Sanderson, T., Snoeck, D., Das, B. S., Minasny, B., & Field, D. J. (2019). Near infrared diffuse reflectance spectroscopy for rapid and comprehensive soil condition assessment in smallholder cacao farming systems of Papua New Guinea. Catena, 183, 104185. https://doi.org/10.1016/j.catena.2019.104185

Soltanpour, P. N., Tabatabai, M. A., Johnston, C. T., & Sumner, M. E. (1996). Methods of soil analysis: Part 3. Chemical and microbiological properties (pp. 437–474). American Society of Agronomy and Soil Science Society of America. https://doi.org/10.2136/sssabookser5.3.c15

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. https://doi.org/10.1016/j.ecolind.2009.05.001

Tabatabai, M. A., & Johnston, C. T. (1996). In D. L. Sparks, A. L. Page, P. A. Helmke, R. H. Loeppert, P. N. Soltanpour, M. A. Tabatabai, C. T. Johnston, & M. E. Sumner (Eds.), Methods of soil analysis: Part 3. Chemical and microbiological properties (pp. 475–490). American Society of Agronomy and Soil Science Society of America. https://doi.org/10.2136/sssabookser5.3.c16

Thomas, G. W. (1996). Soil pH and soil acidity. In D. L. Sparks, A. L. Page, P. A. Helmke, R. H. Loeppert, P. N. Soltanpour, M. A. Tabatabai, C. T. Johnston, & M. E. Sumner (Eds.), Methods of soil analysis: Part 3. Chemical and microbiological properties (pp. 475–490). American Society of Agronomy and Soil Science Society of America.

Tsakiridis, N. L., Theocharis, J. B., Panagos, P., & Zalidis, G. C. (2019). An evolutionary fuzzy rule-based system applied to the prediction of soil organic carbon from soil spectral libraries. Applied Soft Computing, 81, 105504. https://doi.org/10.1016/j.asoc.2019.105504

Vasava, H. B., Gupta, A., Arora, R., & Das, B. S. (2019). Assessment of soil texture from spectral reflectance data of bulk soil samples and their dry-sieved aggregate size fractions. Geoderma, 337, 914–926. https://doi.org/10.1016/j.geoderma.2018.11.004

Viscarra Rossel, R. A., Cattle, S. R., Ortega, A., & Fouad, Y. (2009). In situ measurements of soil colour, mineral composition and clay content by Vis–NIR spectroscopy. Geoderma, 150(3–4), 253–266. https://doi.org/10.1016/j.geoderma.2009.01.025

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.

Wanzhu, M., Hongkui, Z., Hao, H., Zhiqing, Z., Kangying, Z., & Guangzhi, Z. (2025). Estimation of soil-free iron content using spectral reflectance and machine learning algorithms. Scientific Reports, 15, 23928. https://doi.org/10.1038/s41598-025-09301-7

Wilding, L. G. (1985). Soil spatial variability: Its documentation, accommodation and implication to soil surveys. In D. R. Nielsen & J. Bouma (Eds.), Soil spatial variability: Proceedings of a workshop of the ISSS and the SSA (pp. 166–187). PUDOC.

Xu, S., Yongcun, Z., Meiyan, W., & Xuezheng, S. (2018a). Quantification of different forms of iron from intact soil cores of paddy fields with Vis-NIR spectroscopy. Soil Science Society of America Journal, 82(6). https://doi.org/10.2136/sssaj2018.01.0014

Xu, S., Zhao, Y., Wang, M., & Shi, X. (2018b). Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–NIR spectroscopy. Geoderma, 310, 29–43. https://doi.org/10.1016/j.geoderma.2017.09.013

Xuxin, S., Pei, W., Lukas Van, Z., Nanthi, B., Hailong, W., Xiaomin, L. I., Kuan, C. H., Yang, Y., Milan, W., Tongxu, L., & Fangbai, L. (2022). Towards a better understanding of the role of Fe cycling in soil for carbon stabilization and degradation. Carbon Research, 1(5). https://doi.org/10.1007/s44246-022-00008-2

Yingtao, Q., Shu, G., Xiping, Y., Lin, H., Jiankai, H., Z., & Chengzhuo, L. (2024). Research on the quantitative inversion of soil iron oxide content using hyperspectral remote sensing and machine learning algorithms in the Lufeng annular structural area of Yunnan, China. Sensors, 24(21), 7039. https://doi.org/10.3390/s24217039

Zahedifar, M. (2017). Sequential extraction of zinc in the soils of different land use types as influenced by wheat straw-derived biochar. Journal of Geochemical Exploration, 182, 22-31. https://doi.org/10.1016/j.gexplo.2017.08.007

Zahedifar, M. (2020a). Effect of biochar on cadmium fractions in some polluted saline and sodic soils. Environmental Management, 66(6), 1133-1141. https://doi.org/10.1007/s00267-020-01371-9

Zahedifar, M. (2020b). Iron fractionation in the calcareous soils of different land uses as influenced by biochar. Waste and Biomass Valorization, 11(5), 2321-2330. https://doi.org/10.1007/s12649-018-0481-9

Zhao, L., Hong, H., Fang, G., Algeo, T. J., Wang, C., Li, M., & Yin, K. (2020). Potential of VNIR spectroscopy for prediction of clay mineralogy and magnetic properties, and its paleoclimatic application to two contrasting Quaternary soil deposits. Catena, 184, 104239. https://doi.org/10.1016/j.catena.2019.104239