برآورد فرسایش‌پذیری خاک در ایران با استفاده از پایگاه‌های داده‌مکانی SoilGrids و HWSD

نوع مقاله : پژوهشی

نویسندگان

1 استادیار گروه سنجش از دور و GIS دانشکده ی علوم زمین، دانشگاه شهید چمران اهواز

2 استاد گروه سنجش از دور و GIS دانشکده ی علوم زمین، دانشگاه شهید چمران اهواز

3 دانشجوی دکتری گروه سنجش از دور و GIS دانشکده ی علوم زمین، دانشگاه شهید چمران اهواز

چکیده

مقدمه و هدف
ایران از جمله کشورهایی است که فرسایش خاک در حال تبدیل‌شدن به یکی از مشکل‌های حاد زیست‌محیطی است و هر سال میلیون‌ها تن از خاک غنی و حاصلخیز به علت‌نبودن مدیریت درست و مناسب از کشور خارج و غیرقابل ‌استفاده می‌شود. به‌منظور حفاظت مؤثر و جلوگیری از آثار نامطلوب فرسایش خاک نیاز است که عامل‌های تأثیرگذار در فرسایش خاک شناسایی و برآورد مناسبی از مقدار آن‌ها در سطح کشور گزارش شود. در این راستا پژوهش حاضر باهدف برآورد فرسایش‌پذیری خاک (عامل K) برای ژرفای صفر تا 30 سانتی‌متری خاک در سطح کشور ایران انجام گرفت.
مواد و روش‌ها
برای این منظور از دو گروه داده شامل پایگاه‌ داده هماهنگ خاک جهان (HWSD) و اطلاعات خاک جهانی شبکه‌بندی شده (SoilGrids) و همچنین نرم‌افزارهای RStudio و ArcGIS استفاده شد. برای محاسبه‌ی فرسایش‌پذیری خاک، ابتدا داده‌های جهانی خاک SoilGrids در چهار ژرفای صفر، پنج، 15 و 30 سانتی‌متری تهیه گردید و میانگین‌گیری انجام شد. از طرف دیگر پایگاه‌ داده HWSD به‌صورت تصویربرداری برای ژرفای صفر تا 30 سانتی‌متری به‌صورت یک ‌لایه‌ یکپارچه دریافت شد. سپس براساس مقدار کربن آلی، رس، شن و لای خاک از این داده‌ها با استفاده از معادله‌ی EPIC برای برآورد عامل فرسایش‌پذیری استفاده گردید و در نهایت برای ارزیابی اندازه‌ی اختلاف این دو پایگاه‌ داده در برآورد عامل فرسایش‌پذیری، از شاخص‌های مجذور خطای نسبی (RE)، میانگین مطلق خطا (MAD) و جذر میانگین مربعات خطا (RMSE) استفاده شد.
نتایج و بحث
نتایج نشان داد که مقدار متوسط درصد ذرات رس در سطح زیر آبخیزهای ایران بین 15 تا 32 % متغیر و میانگین آن برای کل کشور 23 % است که در بین زیر آبخیزها، زیر آبخیزهای کرخه و کویر لوت به‌ترتیب بیش‌ترین و کم‌ترین درصد رس را دارند. مقدار متوسط درصد ذرات لای در سطح زیر آبخیزهای ایران بین 19 تا 45 % متغیر و میانگین آن برای کل کشور 32% است که در زیر آبخیزهای قره‌سو-گرگان رود و کویر لوت به‌ترتیب بیشینه و کمینه‌ی درصد ذرات لای گزارش شد. متوسط درصد ذرات شن در سطح زیر آبخیزهای ایران بین 28 تا 65 متغیر میانگین آن برای کل کشور 44 % است که زیر آبخیز کرخه و زیر آبخیز کویر لوت به‌ترتیب دارای کمینه‌ی و بیشینه‌ی درصد ذرات شن می‌باشند. علت پایین‌بودن عامل فرسایش‌پذیری خاک در آبخیزهای گاوخونی و کویر لوت، بالا‌بودن درصد شن است، این در حالی است که درصد شن در آبخیزهای کویر لوت و گاوخونی به‌ترتیب 65 و 47 % از کل ذرات خاک را شامل می‌شود. متوسط درصد کربن آلی در سطح آبخیزهای ایران بین 0/3 تا 3/9 متغیر است که به‌ترتیب این اندازه‌ها مربوط به زیر آبخیزهای هامون-هیرمند و رودخانه‌های بین سفیدرود و هراز می‌باشد، بنابراین، می‌توان بیان کرد که غالب آبخیزهای کشور از نظر درصد ماده آلی در شرایط نامناسبی قرار دارند. نتایج نشان داد که بخش‌های جنوب‌غربی، غرب و شمال‌شرقی کشور دارای بیشینه‌ی مقدار عامل فرسایش‌پذیری خاک بود و مناطق ایران مرکزی و بخش‌های بیابانی و کویری ایران به‌واسطه‌ی دارابودن درصد بیشتری از ذرات شن، فرسایش‌پذیری کمتری را دارند. در سطح آبخیز با استفاده از داده‌های SoilGrids، کم‌ترین اندازه‌ی متوسط فرسایش‌پذیری خاک 0/033 تن ساعت بر مگاژول میلی‌متر، مربوط به کویر لوت و بیش‌ترین مقدار آن 0/045 تن ساعت بر مگاژول میلی‌متر مربوط به زیر آبخیز حله بود. در زیر آبخیزهای درجه‎‌ی‌ دو ایران، بیشینه و کمینه‌ی میانگین شاخص فرسایش‌پذیری خاک با داده‌های HWSD به‌ترتیب مربوط به آبخیزهای مند و گاوخونی با مقدار 0/042 و 0/033 تن ساعت بر مگاژول میلی‌متر بود. همچنین نتایج نشان داد که میانگین عامل فرسایش‌پذیری خاک در ایران با استفاده از دو پایگاه‌ داده‌مکانی HWSD و SoilGrids به‌ترتیب 0/036 و 0/038 تن ساعت بر مگاژول میلی‌متر است.
نتیجه­گیری و پیشنهادها
بررسی فرسایش‌پذیری خاک با داده‌های SoilGrids و HWSD در سطح زیر آبخیزها نشان داد که بیشینه و کمینه‌ی اندازه‌ی خطای نسبی به‌ترتیب در آبخیزهای اترک و بلوچستان جنوبی با مقدار 21 و یک درصد می‌باشد و مقدار این خطا برای میانگین کشوری حدوداً پنج درصد است؛ بنابراین می‌توان این‌گونه استنباط نمود که اگرچه خطای بین دو پایگاه‌ داده زیاد نیست اما داده‌های SoilGrids به‌دلیل پیوستگی و قدرت تفکیک مکانی بهتر، منبع مناسب‌تری برای مدل‌سازی‌های وابسته به منابع خاک و آب می‌باشند. این پایگاه ‌داده با استفاده از نیم‌رخ‌های بیش‌تری مدل‌سازی شده (حدوداً 150 هزار نیم‌رخ خاک در سطح جهان)، بنابراین دارای دقت مناسب می‌باشد. بیان این نکته ضروری است که با هدف بهبود نتایج این پایگاه ‌داده به‌وسیله‌ی داده‌های زمینی در بخش‌های مختلف کشور، بررسی نبودن قطعیت این داده‌ها به پژوهشگران متخصص در این زمینه توصیه می‌شود. قابل‌توجه است که برآورد فرسایش‌پذیری خاک در این پژوهش با استفاده از مدل EPIC انجام شد، درحالی‌که ارزیابی و مقایسه‌ی آن با دیگر مدل‌های برآورد فرسایش‌پذیری خاک در سطح کشور به دیگر پژوهشگران پیشنهاد می‌شود.

کلیدواژه‌ها

عنوان مقاله [English]

Estimation of Soil Erodibility (K-factor) in Iran Using SoilGrids and HWSD Spatial Databases

نویسندگان [English]

  • Mostafa Kabolizadeh 1
  • Kazem Rangzan 2
  • shahin Mohammadi 3
1 Assistant Professor of Department of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz
2 Professor of Department of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz
3 Ph.D. Student of Department of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz
چکیده [English]

Introduction and Objective
Iran is one of the countries that soil erodibility is becoming one of the acute environmental problems and every year millions of tons of fertile soil are left unusable due to lack of proper management.  In order to effectively protect and prevent the adverse effects of soil erosion, it is necessary to identify the factors affecting soil erosion and provide an appropriate estimate of their amount in the country. In this regard, the present study was conducted to estimate soil erodibility (K-factor) for a soil depth of 0-30 cm in Iran.
Materials and Methods
For this purpose, two database were used, including the Harmonized World Soil Database (HWSD) and global gridded soil information (SoilGrids), as well as RStudio and ArcGIS software. First, SoilGrids data was prepared at four depths of 0, 5, 15 and 30 cm and averaging was done. Also, the HWSD database was received in vector format for a depth of 0 to 30 cm. Finally, these data have been used to estimate the erodibility factor based on the soil content of organic carbon, clay, sand and silt using the EPIC equation. Finally, Relative Error (RE), Median Absolute Deviation (MAD) and Root Mean Square Error (RMSE) were calculated to compare two databases.
Results and Discussion
Assessments indicate that the average percentage of clay particles in the sub-basins of Iran varies between 15 and 32% and the average for the whole country is 23%. On the other hand, the average percentage of silt particles in the sub-basins of Iran varies between 19 and 45%, and the average for the whole country is 32%, among which the maximum and minimum percentage of silt particles are in the sub-basins of Qarasu-GorganRoud and Kavir Lut, respectively. Also, the average percentage of sand particles in the sub-basins of Iran varies between 28 and 65, and on the other hand, the average for the whole country is 44%, the minimum of which is related to the sub-basin of Karkheh and the maximum is related to the sub-basin of Kavir Lut. In Gavkhouni and Kavir Lut sub-basins, the reason the low soil erodibility factor is the high percentage of sand in these sub-basins, so that the percentage of sand in the Lut and Gavkhouni basins is 65 and 47% of the total soil particles, respectively. Considering that the average percentage of organic carbon in the sub-basins of Iran varies between 0.3 and 3.9, respectively, these values are related to the sub-basins of Hamun-e-Hirmand and Sefidroud-Haraz, so it can be said that the majority of the country's sub-basins are in poor conditions in terms of the percentage of organic matter. The results show that the southwestern, western and northeastern parts of the country have the maximum amount of soil erodibility factor, and the central Iran and desert parts of Iran have lower erodibility due to having a higher percentage of sand particles. Also, the results show that the lowest average amount of soil erodibility at the sub-basin scale using SoilGrids data with a value of 0.033 (ton*h/Mj*mm) is related to Lut Desert and also its maximum value is 0.045 (ton*h/ Mj*mm) related to it is the sub-basin of Haleh. In addition, the maximum and minimum values of the erodibility index with the HWSD data as an average of the basins of Iran are corresponding to the Mand and Gavkhouni sub-basins, respectively, with values of 0.042 and 0.033 (ton*h/ Mj*mm). So, the results showed that the average soil erodibility factor in Iran using two the HWSD and SoilGrids databases was 0.036 and 0.038 (ton*h/ Mj*mm) respectively.
Conclusion and Suggestions
The study of soil erodibility with the data of SoilGrids and HWSD at the sub-basins scale showed that the maximum and minimum RE in Atrak and South Balochestan sub-basins are 21 and 1 percent, respectively, and the amount of RE is about 5% for the country average; Therefore, it can be concluded that although the RE between the two databases is not high, SoilGrids data is a more suitable source for soil and water resource modeling due to its continuity and better spatial resolution. Finally, it should be said that although this database is modeled using a larger number of profiles (about 150,000 soil profiles in the world), so they have appropriate accuracy. However, it is necessary to state that investigating the uncertainty of these data in order to improve the results of this database in order to improve the results of that in different parts of the country is recommended to researchers and researchers. Also, it is noteworthy that the EPIC model was used to estimate soil erodibility in this study, while its evaluation and comparison with other soil erodibility estimation models in the country is suggested to other researchers and experts.

کلیدواژه‌ها [English]

  • Remote sensing
  • soil conversation
  • soil erosion
  • watershed management

مراجع

Aksoy E, Yigini Y, Montanarella L. 2016. Combining soil databases for topsoil organic carbon mapping in europe. PLoS ONE, 11(3):1–17.
Baskan O. 2021. Analysis of spatial and temporal changes of RUSLE-K soil erodibility factor in semi-arid areas in two different periods by conditional simulation. Archives of Agronomy and Soil Science, pp. 1–13.
Benavidez R, Jackson B, Maxwell D, Norton K. 2018. A review of the (Revised) universal soil loss equation ((R) USLE): with a view to increasing its global applicability and improving soil loss estimates. Hydrology and Earth System Sciences, 22(11): 6059–6086.‏
Bonilla CA, Johnson OI. 2012. Soil erodibility mapping and its correlation with soil properties in Central Chile. Geoderma, 189(11): 116–123.‏
Bybordi M. 2001. Soil physics. Tehran University Press, 671 p. (In Persian).
Dami M, Khaledi Darvishan A. 2021. Evaluation of runoff components in laboratory plots with straw conservation treatment. Watershed Management Research Journal, 34(1): 112–125. (In Persian).
DE Faria Godoi R, Rodrigues DB, Borrelli P, Oliveira PTS. 2021. High-resolution soil erodibility map of Brazil. Science of the Total Environment,781(10) : 1–10.‏
Deshmukh K, Aher S. 2014. Particle size analysis of soils and its interpolation using GIS technique from Sangamner area, Maharashtra, India. Int J Environ Sci, 3(10): 32–37.
Efthimiou N. 2020. The new assessment of soil erodibility in Greece. Soil and Tillage Research, 204(10): 1–17.‏
Ehbrecht M, Seidel D, Annighofer P, Kreft H, Kohler M, Zemp DC, Puettmann K, Nilus R, Babweteera F, Willim K, Stiers M, Soto D, Boehmer HJ, Fisichelli N, Burnett M, Juday G, Stephens SL, Ammer C. 2021.
Global patterns and climatic controls of forest structural complexity. Nature Communications, 12(1):1–12.
Esmaeelnejad L, Ramezanpour H, Seyedmohammadi J. 2015. Various erosional forms in marly lands with different physical properties ‎and clay mineral variations in southern Guilan Province. Watershed Engineering and Management, 7(4): 523–535 .(In Persian).
Fluhrer A, Jagdhuber T, Akbar R, Neill PE, Entekhabi D. 2020. Simultaneous retrieval of surface roughness parameters for bare soils from combined active–passive microwave SMAP observations. IEEE Transactions on Geoscience and Remote Sensing, 59(10): 8182–8194.‏
Folberth C, Skalský R, Moltchanova E, Balkovič J, Azevedo L,  Obersteiner M, Van Der M. 2016. Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nature Communications, 7(1): 1–13.‏
Fullen MA, Catt JA. 2004. Soil management: Problems and Solutions. Arnold, London, 269 p.
Gayen A, Haque SM. 2022. Soil erodibility assessment of laterite dominant sub-basin watersheds in the humid tropical region of India. CATENA, 213(6): 1–15.
Griffiths RI, Thomson BC, Plassart P, Gweon HS, Stone D, Creamer RE, ... & Bailey MJ. 2016. Mapping and validating predictions of soil bacterial biodiversity using european and national scale datasets. Applied Soil Ecology, 97(1): 61–68.‏
Hengl T, Mendes de Jesus J, Heuvelink GB, Ruiperez Gonzalez M, Kilibarda M, Blagotić A, ... & Kempen B. 2017. Soil grids 250 m: Global Gridded Soil Information Based on Machine Learning. PLoS one, 12(2): 1–40.‏
Hengl T, Mendes de Jesus J, MacMillan RA, Batjes NH, Heuvelink GBM, Ribeiro E, Alessandro Samuel-Rosa A, Kempen B, Leenaars J, Walsh M, Gonzalez M. 2014. Soil grids1km global soil information based on automated mapping. PLoS ONE, 9(8):1–17.
Ippolito TA, Herrick JE, Dossa EL, Garba M, Ouattara M, Singh U, Neff JC. 2021. A comparison of approaches to regional land-use capability analysis for agricultural land-planning. Land, 10(5): 1–23.‏
Jafari  M, Azarnivand H, Souri M, Sardar Abadi M. 2007. Study of organic matter variation agricultural lands (Case study : Kermanshah Province). Pajouhesh & Sazamdegi, 19(2): 19–24 .(In Persian).
Jahandideh M, Amirian-Chakan A, Faraji M, Jafarizadeh M. 2021. Soil erodibility and its spatial variation in areas under erosion control measures in Behbahan Region. Applied Soil Research, 9(3): 73–88. (In Persian).
Jankauskas B, Jankauskiene G, Fullen M. 2008. Soil erosion and changes in the physical properties of Lithuanian Eutric Albeluvisols under different land use systems. Soil and Plant Science Journal, 58(1): 66–76.
Kamali K, Ardakani AJ, Eslami M. 2015. The relationship determine between measured soil erodibility using simulator and wischmeier nomograph and bisal method. Watershed Management Research Journal, 28(2): 66–72  .(In Persian).
Karami E, Ghorbani Dashtaki S, Khalilimoghadam B. 2018. Effects of land management on soil erodibility-A case study in part of Zayandeh-Rood watershed. Journal of Agricultural Engineering Soil Science and Agricultural Mechanization (Scientific Journal of Agriculture), 40(2): 105–119. (In Persian).
Khaledi Darvishan A, Sadeghi SHR, Homaee M, Arabkhedri M. 2021. Sediment budgeting in laboratory plots under rainfall simulation. Watershed Management Research Journal, 34(2): 15–31 .(In Persian).
Khanchoul K, Boubehziz S. 2019. Spatial variability of soil erodibility at El Hammam catchment, northeast of Algeria. Environment & Ecosystem Science, 3(1), 17–25.‏
Khosraviaqdam K, Momtaz H, Asadzadeh F. 2019. Estimation of soil erodibility factor of USLE model and its relationship with landscape features in some parts of Nazzlo-Chay basin, Iran. Applied Soil Research, 7(1): 31–43. (In Persian).
Kiani Harchegani M, Sadeghi S, Falahatkar S. 2019. Comparative analysis of soil erodibility factor in Shazand Watershed. Iranian Journal of Ecohydrology, 6(1): 153–163. (In Persian).
Lado M, Paz A, Ben-Hur M. 2004. Organic matter and aggregate size interactions in infiltration, seal formation, and soil loss. Soil Science Society of America Journal, 68(5): 935–942.
Lal R. 2015. Sequestering carbon and increasing productivity by conservation agriculture. Journal of Soil and Water Conservation, 70(3): 55–62.‏
Lee S, Chu ML, Guzman JA, Flanagan D. C. 2022. Modeling soil erodibility and critical shear stress parameters for soil loss estimation. Soil and Tillage Research, 218(4):1–18.
Liang Z, Chen S, Yang Y, Zhou Y, Shi Z. 2019. High-resolution three-dimensional mapping of soil organic carbon in China: Effects of soilgrids products on national modeling. Science of the Total Environment, 685 (10): 480–489.‏
Liu M, Han G, Li X, Zhang S, Zhou W, Zhang Q. 2020. Effects of soil properties on K factor in the granite and limestone regions of China. International Journal of Environmental Research and Public Health, 17(3): 1–13.‏
Mammadli N, Gojamanov M. 2021. High-resolution soil erodibility K-factor estimation using machine learning generated soil dataset and soil pH levels. Geodesy and Cartography, 70(1):1–12.
Maquaire O, Malet J, Remaıtre A, Locat J, Klotz S, Guillon J. 2003. Instability conditions of marly hillslopes: towards landsliding or gullying? The case of the Barcelonnette Basin, South East France. Engineering Geology, 70(1): 109–130.‏
Meghraoui M, Habi M, Morsli B, Regagba M, Seladji A. 2017. Mapping of soil erodibility and assessment of soil losses using the RUSLE model in the Sebaa Chioukh Mountains (northwest of Algeria). Journal of Water and Land Development, 34(1): 205–214.
Millward A, Mersey J. 1999. Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed. Catena, 38(2): 109–129.
Mirzaee S, Ghorbani-Dashtaki S, Kerry R. 2019. Comparison of a spatial, spatial and hybrid methods for predicting inter-rill and rill soil sensitivity to erosion at the field scale. Catena, 188(5): 1–10.
Mirzashahi K. 2017. Periodic study of soil organic carbon in plains of Khuzestan and providing extensions. Land Management Journal, 5(1): 1–12.
Mohammadi Sh, Karimzadeh H, Alizadeh M. 2018. Spatial estimation of soil erosion in Iran using RUSLE model. Iranian Journal of Ecohydrology, 5(2): 551–569. (In Persian).
Montanarella L, Vargas R. 2012. Global governance of soil resources as a necessary condition for sustainable development. Current Opinion in Environmental Sustainability, 4(5):559–564.
Mulder V, Lacoste M, Richer-de Forges A, Martin M, Arrouays D. 2016. National versus global modelling the 3D distribution of soil organic carbon in mainland France. Geoderma, 263(3):16–34.
Nikseresht F, Honarbakhsh A, Ostovari Y, Afzali SF. 2019. Model development to predict CEC using the intelligence data mining approaches. Commun. Soil Sci. Plant Anal, 50(17): 2178–2189.
Nyawade S, Karanja N, Gachene C, Parker M, Schulte-Geldermann E. 2018. Susceptibility of soil organic matter fractions to soil erosion under potato-legume intercropping systems in central Kenya. Journal of Soil and Water Conservation, 73(5): 567–576.‏
Nzeyimana I, Hartemink AE, Ritsema C, Stroosnijder L, Lwanga EH, Geissen V. 2017. Mulching as a strategy to improve soil properties and reduce soil erodibility in coffee farming systems of Rwanda. Catena, 149(1): 43–51.‏
Ostovari Y, Moosavi AA, Mozaffari H, Poppiel RR. Tayebi M, Demattê JA. 2022. Soil erodibility and its influential factors in the Middle East. In Computers in Earth and Environmental Sciences,  Elsevier, ‏pp: 441–454.
Phinzi K, Ngetar N. S. 2019. The assessment of water-borne erosion at catchment level using GIS-based RUSLE and remote sensing: A review. International Soil and Water Conservation Research, 7(1): 27–46.
Poggio L, De Sousa LM, Batjes NH, Heuvelink G, Kempen B, Ribeiro E, Rossiter D. 2021. Soil grids 2.0: Producing soil information for the globe with quantified spatial uncertainty. Soil, 7(1): 217–240.‏
Pogson M, Hastings A, Smith P. 2012. Sensitivity of crop model predictions to entire meteorological and soil input datasets highlights vulnerability to drought. Environmental Modelling- Software, 29(1):37– 43.
Prasannakumar V, Vijith H, Abinod S, Geetha N. 2012. Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using revised universal soil loss equation (RUSLE) and geo-information technology. Geoscience Frontiers, 3(2): 209–215.
Radziuk H, Świtoniak M, Nowak M. 2021. Microscale spatial variation of soil erodibility factor (K) in a young hummocky moraine landscape in Northern Poland. Bulletin of Geography. Physical Geography Series, 21(1): 5–16.
Radziuk H, Świtoniak M. 2021. Soil erodibility factor (K) in soils under varying stages of truncation. Soil Sci Ann, 72(1):1–8.‏
Rangzan K, Kabolizade M, Rahshidian M, Delfan H. 2019. Modeling and zoning water quality parameters using Sentinel-2 satellite images and computational intelligence. Journal of RS and GIS for Natural Resources, 10(4): 21–37. (In Persian).
Refahi HG. 1996. Soil erosion by water and conservation.Tehran University Press, pp. 141–147. (In Persian).
Refahi HG. 2000. Water erosion and conservation. Tehran University Press, 551 p. (In Persian).
Salehi-Varnousfaderani B, Honarbakhsh A, Tahmoures M, Akbari M. 2022. Soil erodibility prediction by Vis-NIR spectra and environmental covariates coupled with GIS, regression and PLSR in a watershed scale, Iran. Geoderma Regional, 28(2): 1–9.
Scharlemann JPW, Tanner EVJ, Hiederer R, Kapos V. 2014. Global soil carbon: Understanding and managing the largest terrestrial carbon pool. Carbon Management, 5(1):81–91.
Sedighi F, Khaledi Darvishan A, Zare M. 2020. Assessment of the slop gradient on the estimated erosion and sediment delivery ratio by using Cs137 in the Khamsan representative watershed, Watershed Management Research Journal. 33(3): 2–19. (In Persian).
Sepuru T, Dube T. 2018. An appraisal on the progress of remote sensing applications in soil erosion mapping and monitoring. Remote Sensing Applications: Society and Environment, 9(1): 1–9.
Shademanpour A, Taghizadeh R, Tazeh M, Nabiollahi K. 2018. Spatial variability prediction of soil erodibility index using digital soil mapping technique in Baneh city Kanisef region.  Applied Soil Research, 6(2): 15–26. (In Persian).
Sheidaye Karkaj E, Mofidi Chelan M, Akbarlou M, Motamedi J. 2014. Investigation on changes in soil organic matter and nutrient elements under various grazing intensities (Case study: Chaharbagh mountain rangelands of Golestan Province). Iranian Journal of Range and Desert Research, 20(4): 720–732 .(In Persian).
Shepherd A, Littleton E, Clifton-Brown J, Martin M, Hastings A. 2020. Projections of global and UK bioenergy potential from miscanthus x giganteus-feedstock yield, carbon cycling and electricity generation in the 21st century. GCB Bioenergy, 12(4):287–305.
Shepherd KD, Shepherd G, Walsh MG. 2015. Land health surveillance and response: A framework for evidence-informed land management. Agricultural Systems, 132(1):93–106.
Silatsa FB, Yemefack M, Tabi FO, Heuvelink GB, Leenaars JG. 2020. Assessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon. Geoderma, 367: 1–13.‏
Sokouti Xing YZ, Yue Y, Xu D, Kai M, Herbert S. 2007. Spatial variability of nutrient properties In block soil Of Northeast China. Pedospher, 17(1): 19–29.
Stockmann U, Padarian J, McBratney A, Minasny B, De Brogniez D, Montanarella L. 2015. Global soil organic carbon assessment. Global Food Security, 6(3):9–16.
Teimurian T, Nazari Samani A, Feiznia S, Ahmadaali K, Soleimanpour S. 2021. Determining the spatial distribution of gully erosion probability using the maxEnt model in Fars Province. Watershed Management Research Journal, 35(2):2–15 .(In Persian).
Tian Z, Liu F, Liang Y, Zhu X. 2022. Mapping soil erodibility in southeast China at 250 m resolution: Using environmental variables and random forest regression with limited samples. International Soil and Water Conservation Research, 10(1): 62–74.‏
Toubal AK, Achite M, Ouillon S, Dehni A. 2018. Soil erodibility mapping using the RUSLE model to prioritize erosion control in the Wadi Sahouat Basin, North-West of Algeria. Environmental Monitoring and Assessment, 190(4):1–22.‏
Vaezi AR, Bahrami H, Sadeghi SHR, Mahdian MH. 2008. Study of factors affecting erodibility based on the universal soil loss equation in calcareous soils, Journal of Agric. Science Natural Resource, 14(5):1–14. (In Persian).
Wakindiki I, Ben-hur M. 2002. Soil mineralogy and texture effects on crust micromorphology, infiltration and erosion. Soil Science Society of America Journal, 66)3):597–605.
Wang L, Yan M, Zhang Q, Zhikaun J. 2012. Effects of vegetation restoration on soil physical properties in the wind–water rosion region of the northern loess Plateau of China. Soil, Air and Water, 40(1): 7–15.
Wieder WR, Boehnert J, Bonan GB. 2014. Evaluating soil biogeochemistry parameterizations in earth system models with observations, Global Biogeochem Cycles, 28(3): 211–222.
Williams J. 1989. EPIC-the erosion-productivity impact calculator.‏ USDA, National Agricultural Library.
Yang X, Gray J, Chapman G, Zhu Q, Tulau M, McInnes-Clarke S. 2017. Digital mapping of soil erodibility for water erosion in New South Wales, Australia. Soil Research, 56(2): 158–170.‏
Yang Y, Zha R, Shi Z, Rossel R, Wan D, Liang Z. 2018. Integrating multi-source data to improve water erosion mapping in Tibet, China. Catena, 169(10): 31–45.

فایل ها

سابقه مقاله

  • تاریخ دریافت: 23 اسفند 1400
  • تاریخ بازنگری: 19 اردیبهشت 1401
  • تاریخ پذیرش: 31 خرداد 1401

هم رسانی

ارجاع به این مقاله

آمار