نقشه برداری سه بُعدی درصد رطوبت اشباع خاک با استفاده از تلفیق روش‌های زمین آماری و متغیرهای محیطی در دشت سیستان

نوع مقاله : مقاله کامل علمی پژوهشی

نویسندگان

1 دانش‌آموخته کارشناسی‌ارشد علوم و مهندسی خاک، دانشگاه زابل، زابل، ایران

2 نویسنده مسئول، دانشیار گروه علوم و مهندسی خاک، دانشگاه زابل، زابل، ایران

3 دانشیار بخش تحقیقات خاک و آب، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی گلستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، گرگان، ایران

4 محقق بخش تحقیقات تشکیل، طبقه‌بندی و شناسایی خاک، مؤسسه تحقیقات آب و خاک کشور، کرج، ایران.

5 مربی گروه علوم و مهندسی خاک، دانشگاه زابل، زابل، ایران

چکیده

سابقه و هدف: نقشه‌های خاک یکی از نیازهای مبرم برای کاربران مختلف و تصمیم‌سازان سرزمین هستند. درصد رطوبت اشباع یکی از پارامترهای فیزیکی زودیافت خاک است که در ارتباط با سایر پارمترها می‌تواند در مدیریت اراضی مورد توجه قرار گیرد. از این رو پژوهش حاضر با هدف نقشه‌برداری رقومی درصد رطوبت اشباع خاک به صورت سه بُعدی و با استفاده از روش‌های زمین آماری به همراه متغیرهای محیطی در دشت سیستان که بر دلتای رودخانه هیرمند در اقلیمی خشک واقع شده است، انجام شد.
مواد و روش‌ها: جهت انجام این پژوهش اطلاعات 576 خاکرُخ واقع در دشت سیستان مورد استفاده قرار گرفت. درصد رطوبت اشباع خاک به روش استاندارد در عمق‌های 15-0، 30-15، 60-30 و 100-60 سانتیمتری با استفاده از روش میانگین وزنی اندازه گیری شد. تعداد 35 متغیر محیطی مستخرج از تصاویر ماهواره‌ای به عنوان متغیرهای سنجش از دور و 22 متغیر محیطی مستخرج از مدل رقومی ارتفاع (DEM) به عنوان متغیرهای زمینی ایجاد شدند و متغیر‌هایی که همبستگی معنی‌دار با درصد رطوبت اشباع خاک در هر عمق نشان دادند وارد فرآیند مدل‌سازی و تجزیه و تحلیل‌های زمین آماری شدند. روش وزن دهی معکوس فاصله (در سه حالت توان اول، دوم و سوم)، کریجینگ ساده و معمولی، کوکریجینگ ساده و معمولی به عنوان روش‌های آنالیزهای زمین‌آماری مورد استفاده قرار گرفت.
یافته‌ها: نتایج نشان داد که مقدار میانگین درصد رطوبت اشباع خاک در عمق‌ 100-60 سانتیمتری دارای بالاترین میانگین (30/39 درصد) و درعمق 15-0 سانتیمتری دارای کمترین مقدار میانگین (92/33 درصد) بود. بهترین مُدل تغییرنما برای درصد رطوبت اشباع در اعماق 15-0، 30-15، 60-30 مُدل کروی و 100-60 سانتی‌متر مُدل نمایی بود و تناسب مکانی برای کلیه عمق‌های مورد مطالعه در کلاس تناسب مکانی متوسط قرار گرفتند. نتایج همبستگی بین متغیرهای محیطی و درصد رطوبت اشباع خاک نشان داد که متغیرهای مشتق شده از سنجش از دور تنها در عمق اول و دوم که نزدیک به سطح زمین بودند با پارامتر درصد رطوبت اشباع خاک همبستگی معنی‌دار نشان دادند ولی متغیرهای مشتق شده از DEM در همه اعماق همبستگی معنی‌دار داشتند. این متغیرها عمدتا مرتبط با فعالیت-های آبرفتی و بادرفتی بودند که که بیشترین اثر را در تغییرات خاک‌ها در منطقه مورد مطالعه داشته‌اند. نتایج تخمینگرهای زمین آماری نشان داد برای عمق اول روش کوکریجینگ ساده با متغیر کمکی حوزه آبخیز، برای عمق دوم روش کوکریجینگ ساده با متغیر کمکی عمق دره و برای اعماق سوم و چهارم کوکریجینگ معمولی با متغیر کمکی حوزه آبخیز بعنوان بهترین و دقیق‌ترین روش‌ها عمل نمودند. مدل‌سازی سه بُعدی درصد رطوبت اشباع خاک نشان داد که مقدار درصد رطوبت اشباع در جنوب کمترین مقادیر و در میانه دشت مقادیر متوسط و در شمال دشت در حاشیه تالاب‌های هامون بیشترین مقادیر را دارا می‌باشد و از سطح به عمق درصد رطوبت اشباع با همین روند مکانی تکرار شده ولی از سطح به عمق مقدار میانگین درصد رطوبت اشباع افزایش می‌یابد. به نظر می‌رسد تغییرات این پارامتر همراستا با تغییرات سه بُعدی اجزاء بافت خاک در منطقه است.
نتیجه گیری: درصد رطوبت اشباع یک خصوصیت زودیافت مناسب است که برای مدیریت اراضی بخصوص در مناطق خشک می‌بایست بیشتر مورد توجه قرار گیرد. همچنین نگاه سه بُعدی به خاک و نقشه‌برداری آن می‌تواند درک کاملتری به کاربران اراضی در راستای مدبربت و برنامه ریزی دهد. روش‌های زمین آماری (کوکریجینگ) با استفاده از متغیرهای کمکی می‌توانند در تهیه نقشه‌های رقومی و سه بُعدی خصوصیات خاک کارایی لازم را داشته باشند و به کاربران مختلف اراضی جهت مدیریت بهتر آن کمک شایانی را انجام دهند. این موضوع منوط به این یافته است که متغیرهای محیطی بکارگیری شوند که منعکس کننده شرایط خاکسازی و عوامل موثر بر آن در مناطق مورد مطالعه باشند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Three-dimensional mapping of soil saturation percentage using the combination of geostatistical methods and environmental variables in the Sistan Plain

نویسندگان [English]

  • Younes Jamalzehi Samareh 1
  • Ali Shahriari 2
  • Mohammad Reza Pahlavan-Rad 3
  • Alireza Ziaie Javid 4
  • Abolfazl Bameri 5
1 M.Sc. Graduate of Soil Science and Engineering, University of Zabol, Zabol, Iran
2 Corresponding Author, Associate Prof., Dept. of Soil Science and Engineering, University of Zabol, Zabol, Iran.
3 Associate Prof., Soil and Water Research Department, Golestan Agricultural and Natural Resources Research and Education Center, AREEO, Gorgan, Iran.
4 Researcher, Division of Soil Formation, Classification and Survey Researches, Soil and Water Research Institute, Karaj, Iran.
5 Academic Staff, Dept. of Soil Science and Engineering, University of Zabol, Zabol, Iran
چکیده [English]

Soil maps are one of the important needs for different land users and decision-makers. Saturation percentage is one of the easily-available physical parameters of soil moisture, Therefore, the present study was conducted with the aim of digital mapping the saturation percentage of soil in three dimensions and using geostatistical methods along with environmental variables in the Sistan Plain, which is located on the Hirmand River delta in a dry climate.
Materials and methods: To carry out this research, the information on 576 soil profiles located in the Sistan Plain was used. The msaturation percentage of soil was measured using the standard method at depths of 0-15, 15-30, 30-60, and 60-100 cm using the weighted average method. Several 35 environmental variables extracted from satellite images as remote sensing variables and 22 environmental variables extracted from the digital elevation model (DEM) were created as land variables, and the environmental variables that showed a significant correlation with the saturation percentage at each depth were included in the modeling process and geostatistical analyses. The inverse distance weighting method (in three states of first, second, and third powers), simple and ordinary kriging, and simple and ordinary cokriging were used as geostatistical analysis methods.
The results showed that the average value of soil saturation percentage at a depth of 60-100 cm had the highest amount (39.30%) and at a depth of 0-15 cm had the lowest average value (33.92%). The best variogram model for the saturation percentage at depths of 0-15, 15-30, and 30-60 cm was the spherical model and 60-100 cm was the exponential model, and the spatial fit for all the studied depths was in the medium spatial fit class. The results of the correlation between environmental variables and the saturation percentage of soil showed that the variables derived from remote sensing had a significant correlation only in the first and second depth, which were close to the surface, but the variables derived from DEM had a significant correlation in all studied depths. These variables were mainly related to fluvial and aeolian activities, which had the greatest effect on soil changes in the studied area. The results of the geostatistical estimators showed that for the first depth, the simple cokriging method with drainage covariate, for the second depth, the simple cokriging method with valley depth covariate, and for the third and fourth depths, ordinary cokriging with drainage covariate were the best and most accurate methods. The three-dimensional modeling of soil saturation percentage showed that the value of saturation percentage is the lowest in the south and medium values in the middle of the Sistan plain, and the highest values of saturation percentage are in the north of the plain at the edge of the Hamoun wetlands. From the surface to the depth the saturation percentage is repeated with the same spatial trend, but the average value of saturation percentage increases from the surface to the depth.
The saturation percentage of soil is an easily-available soil characteristic that should be given more attention for land management, especially in dry areas. Also, a three-dimensional view of soil and its mapping can give a more complete understanding to land users in the direction of development and planning. Geostatistical (cokriging) methods using auxiliary variables can be effective in preparing digital and three-dimensional maps of soil characteristics and can help different land users for better management. This issue depends on the finding that environmental variables are used that reflect the conditions of soil formation and factors affecting it in the study areas.

کلیدواژه‌ها [English]

  • Deltaic soils
  • Aeolian activities
  • Cokriging
  • DEM

مراجع

1.Mulder, V., de Bruin, S., Schaepman, M., & Mayr, T. )2011(. The use of remote sensing in soil and terrain mapping - a review. Geoderma. 62, 1-19.
2.Wulf, H., Mulder, T., Schaepman, M., Keller, A., & Jörg, P. )2014(. Remote sensing of soils. Tech. Rep. 00.0338.
PZ/435-0501. University of Zurich. 1-171.
3.Soil Science Division Staff. )2017(. Soil survey manual. C. Ditzler, K. Scheffe, and H. C. Monger (eds.). USDA Handbook 18. Government Printing Office, Washington, D.C.
4.Ahmad Aali, K., Parsinejad, M., & Rahmani, B. )2009(. Estimation of Saturation Percentage of Soil Using Multiple Regression, ANN, and ANFIS Techniques. Computer and Information Science, 2 (3), 127-136.
5.Stivent, G. A., & Khan, M. A. (1996). Saturation Percentage as a Measure of Soil Texture in the Lower Indus Basin, Journal of SOU Sdence. 17 (1), 255-263.
6.McBratney, A. B., Mendonca Santos, M. L., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117, 3-52.
7.Ma, Y. X., Minasny, B., Malone, B. P., & McBratney, A. B. (2019). Pedology and digital soil mapping (DSM). Eur. J. Soil Sci. 70, 216-235.
8.Padarian, J., Minasny, B., & McBratney, A. B. (2019). Using deep learning for digital soil mapping. Soil-Journal,
5, 79-89.
9.Taghizadeh-Mehrjardi, R., Emadi, M., Cherati, A., Heung, B., Mosavi, A., & Scholten, T. (2021). Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions. Remote Sens. 1-23.
10.Hasani Pak, A. A. (1998). Geostatistical. Tehran University Press. 180p [In Persian]
11.Li, J., & Heap, A. D. (2011). A review of comparative studies of spatial interpolation methods in environmental sciences: performance and impact factors. Ecological Informatics, 6 (3-4), 228-241.
12.Delbari, M., Loiskandl, W., & Afrasiab, P. (2010). Uncertainty assessment of soil organic carbon content spatial distribution using geostatistical stochastic simulation. Soil Res. 48, 27-35.
13.Selmy, S. A., Abd El-Aziz, S., El-Desoky, A., & El-Sayed, M. A. (2022). Characterizing, predicting, and mapping of soil spatial variability in Gharb El-Mawhoub area of Dakhla Oasis using geostatistics and GIS approaches. Journal of the Saudi Society of Agricultural Sciences, 21, 383-396.
14.Abdel‐Rahman, M. A., Zakarya, Y. M., Metwaly, M., & Koubouris, G. (2021). Deciphering Soil Spatial Variability through Geostatistics and Interpolation Techniques. Sustainability, 13, 194.
15.Fan, X., Weng, Y., & Tao, J. (2016). Towards decadal soil salinity mapping using Landsat time series data. International Journal of Applied Earth Observation and Geo information,52, 32-41.
16.Ninerola, V. B., Navarro-Pedreno, J., Gomez Lucas, I., Melendez Pastor, I., & Jordan Vida, M. (2017). Geostatistical assessment of soil salinity and cropping systems used as soil phytoremediation strategy. Journal of Geochemical Exploration, 174, 53-58.
17.Shahriari, M., Delbari, M., Afrasiab, P., & Pahlavan-Rad, M. R. (2019). Predicting regional spatial distribution of soil texture in floodplains using remote sensing data: A case of southeastern Iran. Catena, 182, 104-149. doi:10.1016/j.catena.2019.104149.
18.Zhang, Y., Ji, W., Saurette, D. D., Easher, T. H., Li, H., Shi, Z., Adamchuk, V. I., & Biswas, A. (2020). Three-dimensional digital soil mapping of multiple soil properties at a field-scale using regression kriging. Geoderma. 366, 114253.
19.Shi, W., & Zhang, M. (2023). Progress on spatial prediction methods for soil particle-size fractions. Journal of Geographical Sciences, 33, 1553-1566.
20.Tamiru, B., Soromessa, T., Warkineh, B., & Legese, G. (2023). Mapping Soil Parameters with Environmental Covariates and Land Cover Projection in Tropical Rainforest, Hangadi Watershed, Ethiopia. Sustainability, 15, 1066.
21.Heuvelink, G. B., Kros, J., Reinds, G. J., & Vries, W. D. (2016). Geostatistical prediction and simulation of European soil property maps. Geoderma Regional, 7, 201-215.
22.Amirian Chekan, A., Taghizadeh Mehrjerdi, R., Sarmadian, F., & Heidary, A. (2017). Three-dimensional mapping of soil texture using spline depth functions and artificial neural networks. Iranian Journal of Soil and Water Research, 48 (1), 113-123. [In Persian]
23.Emami, M., Khormali, F., Pahlavan-Rad, M. R., & Ebrahimi, S. (2024). Preparation of three-dimensional maps of soil particle size fraction by combining quantile regression forest algorithm and spline functions in the north of Golestan province. Iran, Iranian Journal of Soil and Water Research,55 (1), 51-68. https://doi.org/ 10.22059/ ijswr.2023.366978.669594. [In Persian]
24.Dharumarajan, S., & Hegde, R. (2020). Digital mapping of soil texture classes using Random Forest classification algorithm. Soil Use Manage. 2022; 38, 135-149.
25.Jamshidi, M., Delavar, M. A., Taghizadehe-Mehrjerdi, R., & Brungard, C. (2019). Evaluating Digital Soil Mapping Approaches for 3D Mapping of Soil Organic Carbon. Iranian Journal of Soil Research, 33 (2), 227-239. doi: 10.22092/ijsr.2019.119764. [In Persian]
26.Mousavi, S. R., Sarmadian, F., Omid, M. K., & Bogaert, P. (2022). Three-dimensional mapping of soil organic carbon using soil and environmental covariates in an arid and semi-arid region of Iran. Measurement, 201, 111706. https://doi.org/10.1016/j.measurement.2022.111706.
27.Filippi, P., Jones, E., & Bishop, T. F. (2020). Catchment-scale 3D mapping of depth to soil sodicity constraints through combining public and on-farm soil databases – A potential tool for on-farm management. Geoderma, 374, 114396.
28.Emami, M., Khormali, F., Pahlavan-Rad, M., & Ebrahimi, S. (2024). Digital modeling of surface and subsurface soil salinity in Golestan Province, Iran. Geoderma Regional, 37, e00800. https://doi.org/10.1016/j.geodrs.2024. e00800.
29.Zinck, J. A., Metternicht, G., Valle, H. F., & Angelini, M. (2023). Geopedology: An Integration of Geomorphology and Pedology for Soil and Landscape Studies. 2nd edition. Springer Nature Switzerland. 582.
30.Heydari Nesab, M. (2016). The role of wind in the creation of wind landforms in the Sistan region, master's thesis in climatology, University of Sistan and Baluchistan. [In Persian]
31.Mirakzehi, K., Pahlavan-Rad, M. R., Shahriari, A., & Bameri, A. (2018). Digital soil mapping of deltaic soils: A case of study from Hirmand (Helmand) river delta. Geoderma, 313, 233-240.
32.Delbari, M., Afrasiab, P., Gharabaghi, B.,  Amiri, M., & Salehian, A. (2019). Spatial variability analysis and mapping of soil physical and chemical attributes in a salt-affected soil. Arabian Journal of Geosciences, 12 (3), 68-86. doi:10. 1007/s12517-018-4207-x.
33.Jamalzehi Samrah, Y. (2022). Three-dimensional spatial modeling texture components and saturation percentage of soil in the Sistan plain. MSc's thesis. Faculty of Water and Soil, University of Zabol. 186 p.
34.Bishop, T. F. A., McBratney, A. B., & Laslett, G. M. (1999). Modelling soil attribute depth functions with equal-area quadratic smoothing splines, Geoderma, 91, 27-45.
35.Malone, B. P., McBratney, A. B., Minasny, B., & Laslett, G. M. (2009). Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma, 154, 138-152.
36.Agbu, P., Fehrenbacher, D., & Jansen, I. J. (1990). Soil property relationships with SPOT satellite digital data in east central Illinois. Soil Sci. Soc. Am. J.  54, 807-812.
37.Ben-Dor, E., Chabrillat, S., Dematt, J., Taylor, G., Hill, J., Whiting, M., & Sommer, S. (2009). Using imaging spectroscopy to study soil properties. Remote Sens. Environ., 113 (1), 38-55. Imaging Spectroscopy Special Issue.
38.Aksoy, E., Panagos, P., & Montanarella, L. (2012). Spatial prediction of soil organic carbon of Crete by using geostatistics. In: Minasny, B., Malone, B. P., McBratney, A. B. (Eds.), Digital Soil Assessments and Beyond. CRC Press, London. 149-159.
39.Zeraatpisheh, M., Ayoubi, S., Jafari, A., Tajik, S., & Finke, P. (2019). Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran. Geoderma,338, 445-452.
40.Wei, Y., Ding, J., Yang, S., Yang, X., & Wang, F. (2021). Comparisons of random forest and stochastic gradient treeboost algorithms for mapping soil electrical conductivity with multiple subsets using Landsat OLI and DEM/GIS-based data at a type oasis in Xinjiang, China. European Journal of Remote Sensing., 54 (1), 158-181. doi:10.1080/22797254.2021.1888657.
41.Bameri, A., Khormali, F., Kiani, F.,
& Dehghani, A. A. (2015). Spatial variability of soil organic carbon in different hillslope positions in Toshan area, Golestan Province, Iran: Geostatistical approaches. Journal of Mountain Science, 12 (6), 1422-1433. doi:10.1007/s11629-014-3213-z.
42.Metinfar, H., Maqsoodi, Z., Mousavi, S., & Jalali, M. (2019). Evaluation of machine learning methods in digital organic carbon mapping of agricultural soils (part of Khorram Abad plain). Water and soil journal. 24 (4), 343-327. [In Persian]
43.Colombo, R., Bellingeri, D., FasoUni, D., & Marino, C. M. (2003). Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Sens. Environ. 86 (1), 120-131. doi:10.1016/S0034- 4257(03)00094-4.
44.Lymburner, L., Beggs, P. J., & Jacobson, C. R. (2000). Estimation of canopy-average surface-specific leaf area using Landsat TM data. Photogramm. Eng. Remote Sens.66, 183-191.
45.Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sens Environ. 25, 295-309.
46.Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27 (14), 3025-3033.
47.Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the raidometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ. 83, 195-213.
48.Boettinger, J. L., Ramsey, R. D., Bodily, J. M., Cole, N. J., Kienast-Brown, S., Nield, S. J., Saundes, A. M., & Stum,
A. K. (2008). Landsat spectral data for digital soil mapping. In: Hartemink, A. E., McBratney, A. B., Mendonca Santos, M. L. (Eds.), Digital Soil Mapping With Limited Data. Springer science, Australia. 193-203.
49.Rodiguez, F., Maire, E., Courjault-Rade, D., & Darrozes, J. (2002). The Black Top Hat fanction applied to a DEM: A toolto estimate recent incision in a mountainous water shed. Geophys. Res. Lett. 29, 91-94.
50.Boehner, J., & Selige, T. (2006). Spatial Prediction of soil Attibutes Using Terrain Analysis and Climate Regionalisation. In SAGA-Analysis and Modelling Application; Boehner, J., McCloy, K. R., Strobl, J., Eds., Geographisehe Abhandlungen: Goettinger, Germany. 13-27.
51.Hom, B. K. (1981). Hill shading and the relectance map. Proc. IEEE. 69, 14-47.
52.Karimi Nezhad, M. T., Tabatabaii, S. M., & Gholami, A. (2015). Geochemical assessment of steel smelter-impacted urban soils, Ahvaz, Iran. J. Geochem. Explor. 152, 91-109.
53.Gholamallizade Ahangar, A., Sarani, F., Hashemi, M., & shabani, A. (2015). Comparison of Linear Regression Methods, Geostatistical and Artificial Neural Network Modeling of Organic Carbonin Dry Land of Sistan Plain, Journal of Water and Soil. 6 (28), 1250-1260. [In Persian]
54.Hashemi, M., Gholamalizadeh Ahangar, A., Bameri, A., Sarani, F., & Hejazizadeh, A. (2016). Survey and Zoning of Soil Physical and Chemical Properties Using Geostatistical Methods in GIS (Case Study: Miankangi Region in Sistan), Journal of Water and Soil.30 (2), 443-458. [In Persian]
55.Jamalzehi Samareh, Y., Shahriari, A., Pahlavan Rad, M., Ziaei Javaid, A., & Bameri, A. (2021). Preparation of three-dimensional maps of the size of soil particles in the floodplain of Sistan. The seventeenth Iran Soil Science Congress and 4th National Farm Water Management Conference, Soil and Water Research Institute, Karaj. [In Persian]
56.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 of soil properties in central Iowa soils. Soil Science Society of American Journal, 58, 1501-1511.
57.Yaghmaeian Mahabadi, N., Samiei, K., Zavvareh, M., & Ramezanpour, H. (2019). Spatial variation of some soil properties and their relationship to tea yield in Fouman region, Guilan. Applied Soil Research. 7 (2), 82-96. [In Persian]
58.Jiang, H. L., Liu, G. S., Liu, S. D., Li, E. H., Wang, R., Yang, Y. F., & Hu, H. C. (2012). Delineation of sitespecific management zones based on soil properties for a hillside field in central China. Archives of Agronomy and Soil Science, 58 (10), 1075-1090.
59.Marzvan, S., Asadi, H., & Davatgar, N. (2015). The effect of tillage management on spatial variation of some soil properties in steep lands. Journal of Soil Management and Sustainable Production, 5 (1), 97-111. [In Persian]
60.Jafarnia, S., & Akbarinia, M. (2014). Investigation of spatial distribution of soil and water properties by use of geostatistical in Mangrove forest of Qeshm Island. Iranian Journal of Forest and Poplar Research, 22 (4), 673-686.
[In Persian]
61.Vieira, S. R., & Paz Gonzalez, A. (2003). Analysis of the spatial variability of crop yield and soil properties in small agricultural plots. Bragantia. 62 (1), 127-138.
62.Pahlavan-Rad, M. R., & Akbarimoghaddam, A. (2018). Spatial variability of soil texture fractions and pH in a flood plain (case study from eastern Iran). Catena, 160, 275-281.
63.Taghizadeh Mehrjardi, R., Minasny, B., Sarmadian, F., & Malone, P. B. (2014). Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma, 213, 15-28.
64.Akpa, S. I. C., Odeh, I. O. A., & Bishop, T. F. A. (2014). Digital mapping of soil particle-size fractions for Nigeria. Soil Science Society of America Journal, 78, 1953-1966.
65.Asadzadeh, F., Khosravi Aghdam, K., Zawareh, M., Yaghmaian Mahabadi, N., & Ramzanpour, H. (2017). Spatial changes of soil mineral particles using geostatistics and remote sensing for soil texture zoning. Water and Soil Journal (Agricultural Sciences and Industries), 32 (7), 1222-1207. [In Persian]
66.Behnam, V., Gholamalizadeh Ahangar, A., Rahmanian, M., & Bameri, A. (2019). Spatial Distribution of Some Physical and Chemical Properties of Soil using Geostatistic Methods (Case study: Zabol to Zahedan Route). Environment and Water Engineering, 5 (3), 251-263. [In Persian]
67.Owliaie, H., Salehi, A., & Zareian, G. (2024). Assessment of geostatistical models for zoning spatial distribution of some soil properties in Darengan region with different land uses, Fars province. Iranian Journal of Soil and Water Research, 55 (1), 97-116. [In Persian]
مجله پژوهش‌های حفاظت آب و خاک
دوره 31، شماره 4
دی 1403
صفحه 89-111

فایل ها

هم رسانی

ارجاع به این مقاله

آمار