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نقشه‌برداری رقومی هدایت هیدرولیکی اشباع خاک در حوزه آبخیز کیلانه، استان کردستان | ||
| نشریه علمی - پژوهشی مرتع و آبخیزداری | ||
| دوره 78، شماره 3، مهر 1404، صفحه 323-340 اصل مقاله (1.08 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jrwm.2025.389637.1801 | ||
| نویسندگان | ||
| فرزانه پارسایی1؛ احمد فرخیان فیروزی* 1؛ مسعود داوری2؛ روح الله تقی زاده مهرجردی3 | ||
| 1گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، اهواز، ایران | ||
| 2گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه کردستان، سنندج، ایران | ||
| 3گروه علوم زمین، علوم خاک و ژئومرفولوژی، دانشگاه توبینگن، توبینگن، آلمان | ||
| چکیده | ||
| هدایت هیدرولیکی اشباع خاک سطحی (Ks) به‌عنوان یکی از مهم‌ترین ویژگی‌های فیزیکی خاک، نقشی کلیدی در توزیع آب و مواد مغذی در محیط خاک ایفا می‌کند و در مدیریت منابع آب و خاک اهمیت ویژه‌ای دارد. این پژوهش با هدف مدل‌سازی رقومی هدایت هیدرولیکی اشباع خاک سطحی با استفاده از رویکردهای یادگیری ماشین در حوزه آبخیز کیلانه واقع در استان کردستان با مساحت 12 هزار هکتار انجام شد. سه الگوریتم یادگیری ماشین شامل: درخت تصمیم تقویت شده با گرادیان (XGBoost)، جنگل تصادفی (RF) و مدل نزدیکترین -k همسایگی (k-NN) با بهره‌گیری از تعدادی متغیرهای محیطی از مدل رقومی ارتفاع و تصاویر ماهواره سنتینل-2 شامل فاصله از کانال آبراهه، عمق دره، موقعیت نسبی شیب، سطح پایه کانال آبراهه، شاخص روشنایی، شاخص اثر باد، شاخص نرمال شده تفاوت پوشش گیاهی، باند 12، شاخص سبزینگی، انحنای سطح و پارامترهای خاک شامل ماده آلی، آهک، جرم مخصوص ظاهری، میانگین هندسی قطر ذرات، بافت خاک و داده‌های طیف سنجی نزدیک خاک در طول موج 2450-400 نانومتر به عنوان نمایندگان عوامل خاکسازی برای مدل‌سازی Ks مورد استفاده قرار گرفتند. نتایج نشان داد که مدل XGBoost برای پیش‌بینی Ks با R2 برابر 65/0 و nRMSE برابر 25/0 نسبت به سایر مدل‌ها دارای صحت بالاتری بودند. داده‌های طیفی، متغیرهای توپوگرافی و پارامترهای خاک، به‌عنوان ورودی مدل، نقش مهمی در پیش‌بینی تغییرپذیری مکانی Ks داشتند و مدل XGBoost با استفاده از این داده‌ها توانست پیش‌بینی دقیقی ارائه دهد. نتایج نشان داد که Ks تحت تأثیر متغیرهای توپوگرافی، فیزیکی و طیفی قرار دارد؛ ماده آلی، بافت خاک و شاخص‌های توپوگرافی مانند شیب و موقعیت نسبی بیشترین تأثیر را داشتند. نقشه‌های تولیدشده از این رویکرد تغییرپذیری مکانی می‌توانند در مدیریت منابع آب و خاک و مدل‌های هیدرولوژیکی مورد استفاده قرار گیرند. | ||
| کلیدواژه‌ها | ||
| تغییر پذیری مکانی؛ متغیرهای محیطی؛ سنجش خاک؛ یادگیری ماشین | ||
| عنوان مقاله [English] | ||
| Digital Mapping of Soil Saturated Hydraulic Conductivity in the Kilanah Watershed, Kurdistan | ||
| نویسندگان [English] | ||
| Farzaneh Parsaie1؛ Ahmad Farrokhian Firouzi1؛ Masoud Davari2؛ Ruhollah Taghizadeh-Mehrjardi3 | ||
| 1Department of Soil Science and Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran | ||
| 2Department of Soil Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran | ||
| 3Department of Geosciences, Soil Science and Geomorphology, University of Tübingen, Tübingen, Germany | ||
| چکیده [English] | ||
| Surface soil saturated hydraulic conductivity (Ks), as one of the most important physical properties of soil, plays a key role in the distribution of water and nutrients within the soil environment and holds particular significance in water and soil resource management. This study aimed to digitally model Ks using machine learning approaches in the Kilanah watershed, located in Kurdistan Province, covering an area of 12,000 hectares. Three machine learning algorithms, including Gradient Boosted Decision Tree (XGBoost), Random Forest (RF), and k-Nearest Neighbors (k-NN), were utilized, incorporating various environmental variables derived from the digital elevation model and Sentinel-2 satellite imagery. These variables included distance from the drainage channel, valley depth, relative slope position, channel base level, brightness index, wind effect index, Normalized Difference Vegetation Index (NDVI), Band 12, greenness index, and surface curvature. Additionally, soil parameters such as organic matter, lime content, bulk density, geometric mean particle diameter, soil texture, and near-soil spectroscopic data (Latent Variable) within the wavelength range of 400–2450 nm were used as proxies for pedogenic factors to model saturated hydraulic conductivity. The results indicated that the XGBoost model exhibited the highest accuracy for predicting Ks, with an R² value of 0.65 and an nRMSE of 0.25, outperforming the other models. Spectral data, topographic variables, and soil parameters, as model inputs, played a significant role in predicting the spatial variability of Ks. The XGBoost model was able to provide highly accurate predictions. The results demonstrated that topographic, physical, and spectral variables influence Ks; organic matter, soil texture, and topographic indices such as slope and relative position had the most substantial impact. The generated maps can be utilized for water and soil resource management and hydrological models. | ||
| کلیدواژه‌ها [English] | ||
| environmental variables, machine learning, Spatial variability, soil sensing | ||
| مراجع | ||
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Aguilera, H., Guardiola-Albert, G., Merino, L. M., Baquedano, C., Díaz-Losada, E., Ardila, P. A. R., & Valsero, J. D. )2022(. Building inexpensive topsoil saturated hydraulic conductivity maps for land planning based on machine learning and geostatistics. Catena, 208(2), 105788. https://doi.org/10.1016/j.catena.2021.105788. Babaeian, E., Homaee, M., Vereecken, H., Montzka, C., Norouzi, A. A., & van Genuchten, M. T. (2015). A comparative study of multiple approaches for predicting the soil–water retention curve: hyperspectral information vs. basic soil properties. Soil Science Society of America Journal, 79, 1043-8501. https://doi.org/10.2136/sssaj2014.09.0355. Baltensweiler, A., Walthert, L., Hanewinkel, M., Zimmermann, S., & Nussbaum, M. (2021). Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland. Geoderma Regional, 27, e00437. https://doi.org/10.1016/j.geodrs.2021.e00437. Banaie, M. H. (1998). Soil moisture and temperature regimes map of Iran. Soil and Water Research Institute. Ministry of Agriculture, Tehran, Iran, 1sheet. (In Persian). Cheng, Y. Y., Gao, X. G., Liu, T. H., Li, L. X., Du, W., Hamad, A., & Wang, J. P. (2022). Effect of water content on strength of alluvial silt in The Lower Yellow River. Water, 14(20), 3231. https://doi.org/10.3390/w14203231. Dharumarajan, S., Lalitha, M., Gomez, C., Vasundhara, R., Kalaiselvi, B., & Hegde, R. (2022). Prediction of soil hydraulic properties using VIS-NIR spectral data in semiarid region of Northern Karnataka Plateau. Geoderma Regional, 28. e00475. https://doi.org/10.1016/j.geodrs.2021.e00475. Demattê, J., Sousa, A. A., Alves, M. C., Nanni, M. R., Fiorio, P. R., & Campos, R. C. (2008). Determining soil water status and other soil characteristics by spectral proximal sensing. Geoderma, 135, 179-195. https://doi.org/10.1016/j.geoderma.2005.12.002. Espeby, B. (1990). An analysis of saturated hydraulic conductivity in a forested glacial till slope. Soil Science, 150(2), 485-494. Farasati, M., Seyedian, M., & Fathaabadi, A. (2024). Predicting soil hydraulic conductivity using random forest, SVM, and LSSVM models. Natural Resource Modeling, 37(4), e12407. Gao, X. S., Yi, X. I. A. O., Deng, L. J., Li, Q. Q., Wang, C. Q., Bing, L. I., & Min, Z .E .N .G. (2019). Spatial variability of soil total nitrogen, phosphorus and potassium in Renshou County of Sichuan Basin. China. Journal of Integrative Agriculture, 18(2), 279–289. https://doi.org/10.1016/S2095-3119(18)62069-6. Gomez, C., Philippe, L., & Guillaume, C. (2008). Continuum moval versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements. Geoderma. 148, 14148. https://doi.org/10.1016/j.geoderma.2008.09.016. Hengl, T., Nussbaum, M., Wright, M. N., & Heuvelink, G .B .M. (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ, 6, e5518. https://doi.org/10.7287/peerj.preprints.26693v3. Homaee, M. & Farrokhian Firouzi, A. (2008). Deriving point and parametric pedotransfer functions of some gypsiferous soils. Australian Journal of Soil Research, 46, 219-227. https://doi.org/10.1071/SR07161. Hosseini, F. S., Seo, M. B., Razavi-Termeh, S. V., Sadeghi-Niaraki, A., Jamshidi, M., & Choi, S. M. (2023). Geospatial Artificial Intelligence (GeoAI) and Satellite Imagery Fusion for Soil Physical Property Predicting. Sustainability, 15(19), 14125. Janik, L. J., Merry, R. H. Forrester, S. T. Lanyon D. M. & Rawson, A. (2009). Rapid prediction of soil water retention using mid infrared spectroscopy. Soil Science Society of America Journal, 71(2), 507-514. https://doi.org/10.2136/sssaj2005.0391. Khosravani, P., Baghernejad, M., Moosavi, A. A. & Rezaei, M. (2023). Digital mapping and spatial modeling of some soil physical and mechanical properties in a semi-arid region of Iran. Environmental Monitoring and Assessment, 195(11), 1367. https://doi.org/10.1007/s10661-023-11980-6. Kim, I., Pullanagari, R. R., Deurer, M., Singh, R., Huh, K. Y., & Clothier, B. E. (2014). The use of visible and near-infrared spectroscopy for the analysis of soil water repellency. European Journal of Soil Science, 65, 360-368. https://doi.org/10.1111/ejss.12138. Klute, A. & Dirksen, C. (1986). Hydraulic Conductivity and Diffusivity: Laboratory Methods. In: Klute, A., Ed., Methods of Soil Analysis. Part 1: Physical and Miner Alogical Methods, 2nd Edition, Agronomy Monograph No. 9, ASA, Madison, 687-734. https://doi.org/10.2136/sssabookser5.1.2ed.c28. Knotters, M., van Egmond, F. M., Bakker, G., Walvoort, D. J. J., & Brouwer, F. (2017). A selection of sensing techniques for mapping soil hydraulic properties. Wageningen Environmental Research, part of Wageningen UR, Wageningen. Kubiak, K., Spiralski, M., Pompeu, J., Levavasseur, V., & Wawer, R. (2024). Advances in remote sensing for monitoring soil conditions in forest ecosystems: techniques, challenges, and applications. Transactions on Aerospace Research. 277(34), 1-13. https://doi.org/10.2478/tar-2024-0019. Lalitha, M., Dharumarajan, S., Suputhra, A., Kalaiselvi, B., Hegde, R., Reddy, R. S., & Dwivedi, B. S. (2021). Spatial prediction of soil depth using environmental covariates by quantile regression forest model. Environmental Monitoring and Assessment, 193(10), 1-10. Li, H., Zhang, J., Yang, X., Ye, M., Jiang, W., Gong, J. & Xu, Z. (2024). Bayesian optimization based extreme gradient boosting and GPR time-frequency features for the recognition of moisture damage in asphalt pavement. Construction and Building Materials, 434, 136675. https://doi.org/10.1016/j.conbuildmat.2024.136675. Ma, D., Zhang, C., Li, T., & Feng, H. (2024). Novel calibration method for fine soil electrical resistivity based on van der Pauw configuration. Measurement Science and Technology, 36(1), 017007. https://doi.org/10.1088/1361-6501/ad9626. Mahmoudzadeh, H., Matinfar, H. R., Taghizadeh-Mehrjardi, R., & Kerry, R. (2020). Spatial prediction of soil organic carbon using machine learning techniques in western Iran. Geoderma Regional, 21, e00260. https://doi.org/10.1016/j.geodrs.2020.e00260. Mendes, W. D. S., Demattê, J. A. M., Barros, A. S .E., Salazar, D. F. U., & Amorim, M. T. A. (2020). Geostatistics or machine learning for mapping soil attributes and agricultural practices. Revista Ceres, 67(4), 330-336. https://doi.org/10.1590/0034-737X202067040010. Minasny, B., & McBratney, A. B. (2016). Digital soil mapping: A brief history and some lessons. Geoderma, 264, 301-311. https://doi.org/10.1016/j.geoderma.2015.07.017. Mousavi, S. R., Sarmadian, F., & Omid, M. (2022). Three-dimensional mapping of soil organic carbon using soil and environmental covariates in an arid and semi-arid region of Iran, Measurement. 201(13):111706. https://doi.org/10.1016/j.measurement.2022.111706. Mousavi, S. R., Sarmadian, F., Omid, M., & Bogaert, P. (2021). Digital Modeling of Three-Dimensional Soil Salinity Variation Using Machine Learning Algorithms in Arid and Semi-Arid lands of Qazvin Plain. Iranian Journal of Soil and Water Research, 52(7), 1915-1929. https://doi.org/10.22059/ijswr.2021.323030.668957. (In Persian). Mzid, N., Castaldi, F., Tolomio, M., Pascucci, S., Casa, R., & Pignatti, S. (2022). Evaluation of agricultural bare soil properties retrieval from Landsat 8, Sentinel-2 and PRISMA satellite data. Remote Sensing, 14(3), 714. https://doi.org/10.3390/rs14030714. Nanni, M. R., & Demattê, J. A. M. (2006). Spectral reflectance methodology in comparison to traditional soil analysis. Soil Science Society of America Journal, 70(2), 393-407. https://doi.org/10.2136/sssaj2003.0285. Nasta P. & Szabó, B. (2021). Evaluation of pedotransfer functions for predicting soil hydraulic properties: A voyage from regional to field scales across Europe. Journal of Hydrology: Regional Studies. 37:100903. Nazari, R., Ramezani Etedali, H., Nazari, B., & Collins, B. (2020). The impact of climate variability on water footprint components of rainfed wheat and barley in the Qazvin province of Iran. Irrigation and Drainage, 69(4), 826-843. https://doi.org/10.1002/ird.2487. Nguyen, T.T, (2021). Predicting agricultural soil carbon using machine learning. Nature Reviews Earth & Environment, 2(12), 825-825. https://doi.org/10.1038/s43017-021-00243-y. Ng, W., Minasny, B., Mendes, W.D.S., Demattˆe, J. A. M. (2020). The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data. Soil, 6, 565–578. Niede, R., & Benbi, D. K. (2022). Integrated review of the nexus between toxic elements in the environment and human health. AIMS Public Health, 9(4), 758. https://doi.org/10.3934/publichealth.2022052. Ottoni, M. V., Teixeira, W. G., Reis, A. M. H. D., Pimentel, L. G., Souza, L. R., Albuquerque, J. A., ... & Curi, N. (2025). Saturated hydraulic conductivity and steady-state infiltration rate database for Brazilian soils. Revista Brasileira de Ciência do Solo, 49, e0240003. https://doi.org/10.36783/18069657rbcs20240003. Padarian, J., Minasny, B., & McBratney, A. B. (2020). Machine learning and soil sciences: a review aided by machine learning tools. Soil, 6(1), 35-52. https://doi.org/10.5194/soil-6-35-2020, 2020. Páez-Bimos, S., Molina, A., Calispa, M., Delmelle, P., Lahuatte, B., Villacís, M., ... & Vanacker, V. (2023). Soil–vegetation–water interactions controlling solute flow and chemical weathering in volcanic ash soils of the high Andes. Hydrology and Earth System Sciences, 27(7), 1507-1529. Parsaie, F., Farrokhian Firouzi, A., Mousavi, S. R., Rahmani, A., Sedri, M. H., & Homaee, M. (2021). Large-scale digital mapping of topsoil total nitrogen using machine learning models and associated uncertainty map. Environmental Monitoring and Assessment, 193, 1-15. https://doi.org/10.1007/s10661-021-08947-w. Rahmani, A., Sarmadian, F., & Arefi, H. (2022). Digital mapping of surface soil thickness and its associated uncertainty using machine learning approach in a part of arid and semi-arid lands of Qazvin Plain. Iranian Journal of Soil and Water Research, 53(3), 585-602. 10.22059/ijswr.2022.338007.669195. (In Persian). Rezaei, M., Mousavi, S. R., Rahmani, A., Zeraatpisheh, M., Rahmati, M., Pakparvar, M. & Cornelis, W. (2023). Incorporating machine learning models and remote sensing to assess the spatial distribution of saturated hydraulic conductivity in a light-textured soil. Computers and Electronics in Agriculture, 209, 107821. https://doi.org/10.1016/j.compag.2023.107821. Sabetizade, M., Gorji, M., Roudier, P., & Zolfaghari, A. A. (2021). Combination of MIR spectroscopy and environmental covariates to predict soil organic carbon in a semi-arid region. Catena, 196. 1-12. https://doi.org/10.1016/j.catena.2020.104844. Santra, P., Sahoo, R. N., Das, B. S., Samal, R. N., Pattanaik, A. K., & Gupta, V. K. (2009). Estimation of soil hydraulic properties using proximal spectral reflectance in visible, near-infrared, and shortwave-infrared (VIS–NIR–SWIR) region. Geoderma, 152(3-4), 338-349. https://doi.org/10.1016/j.geoderma.2009.07.001. Schneider, W. E., & Young, R. (1997). Spectroradiometry methods. Handbook of Applied Photometry, ed. Casimer De Cusatis, 252. Soil Survey Staff, (2022). Keys to soil Taxonomy. In: U.S. Department of Agriculture, Natural Resources Conservation Service, thirteenth ed. 18-77. Washington, DC. Taghizadeh-Mehrjardi, R., Minasny, B., Sarmadian, F., & Malone, B. P. (2014). Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma, 213, 15–28. https://doi.org/10.1016/j.geoderma.2013.07.020. Viscarra Rossel, R .A .V. (2008). ParLeS: Software for chemometric analysis of spectroscopic data. Chemometrics and Intelligent Laboratory Systems, 90, 72–83. https://doi.org/10.1016/j.chemolab.2007.06.006. Wang, Z., Wu, W., & Liu, H. (2024). Spatial estimation of soil organic carbon content utilizing PlanetScope, Sentinel-2, and Sentinel-1 data. Remote Sensing, 16(17), 3268. https://doi.org/10.3390/rs16173268. Wilding, L. P., & Dress, L. R. (1983). In Application of geostatistics to spatial studies of soil. Eds. BB Trangmar, RS Yost and G Uehara. Advances in Agr, 38. https://doi.org/10.1016/S0065-2113(08)60673-2. Xu, C. Xu, X., Liu, M. Liu, W. Yang, J., Luo, W., Zhang, R., & Kiely, G. (2017). Enhancing pedotransfer functions (PTFs) using soil spectral reflectance data for estimating saturated hydraulic conductivity in southwestern China. Catena, 158: 350–356. https://doi.org/10.1016/j.catena.2017.07.014. Zarei, A., Hasanlou, M., & Mahdianpari, M. (2021). A comparison of machine learning models for soil salinity estimation using multi-spectral earth observation data. ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, 3, 257-263. https://doi.org/10.5194/isprs-annals-V-3-2021-257-2021. Zeitfogel, H., Feigl, M., & Schulz, K. (2022). Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity. Geoderma, 433, 116418. https://doi.org/10.1016/j.geoderma.2023.116418. | ||
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