Digital Mapping of Soil Salinity Using Auxiliary Data and Machine Learning Models in Badr Watershed, Kurdistan Province

Document Type : Research Paper

Authors

1 Agriculture and Natural Resources Research and Education Center of Tehran Province, Agriculture Research, Education and Extension Organization (TAT), Tehran, Iran.

2 Faculty member of the Research and Education Center for Agriculture and Natural Resources of Tehran Province, Organization for Research, Education and Promotion of Agriculture (TAT), Tehran, Iran.

Abstract

Use of remote sensing and machine learning techniques are increasingly recognized as cost-effective methods for displaying soil salinity maps. In this study, Landsat 8 satellite data and sophisticated machine learning techniques were used to map and evaluate soil salinity levels in the Badr Watershed. In this study, several Machine Learning techniques were used to predict salinity values in Badr Watershed. These algorithms included K-nearest neighbor (KNN), decision tree analysis (DTA), artificial neural network (ANN), random forest (RF) and mixed multivariate linear regression (MLR). In the first stage, auxiliary data such as Landsat 8 satellite images of the region and a digital elevation model with a spatial resolution of 10 meters were prepared from the country's Mapping Organization. The geological map of Qorveh was prepared from the geological site of the country, and the geological map of the Badr Watershed was extracted from it and digitized in the environment of the geographic information system. The geomorphological map was drawn and the location of the observation points was determined. Then, modeling was done, digital maps of soil classes and characteristics were prepared and the models were evaluated. Based on the Latin Supercube Technique, 125 outcrops were selected and excavated in the study area. After air-drying in the laboratory, the soil samples were pounded and passed through a 2 mm sieve. Then, soil salinity was measured. In order to estimate soil characteristics, two different conditions were investigated in this study. In the first case, ANN models, DTA and linear MLR were used for prediction. Also, to combine the results of the models, the nearest KNN was used. The results showed that the important auxiliary variables in predicting soil salinity, in order of importance, were geomorphology, depth of the valley, smoothness index of the ridge with a high degree of resolution, wetness index, slope direction, digital height model, basin slope, relative position of the slope, slope amount and slope length. Also, the results of the evaluation showed that among the models used to predict salinity, the combined MLR model with a coefficient of determination of 0.611 and a square root mean square error of 0.032 had the highest accuracy for prediction.

Keywords

Main Subjects

References

  1. ایوبی، ش.، تقی زاده، ر.، نمازی، ر.، ذوالفقاری، ع.، و روستایی صدرآبادی، ف. 1395. مقایسه­ روش­های K نزدیکترین همسایگی و شبکه عصبی مصنوعی برای پهنه­بندی رقومی شوری خاک در منطقه­ی چاه افضل اردکان. نشریه علوم آب و خاک (علوم و فنون کشاورزی و منابع طبیعی). سال بیستم. شماره 76. صص. 71-59.
  2. تقی­زاده مهرجردی، ر.، سرمدیان، م.، امید، م.، تومانیان، ن.، روستا، م.، و رحیمیان، م.، 1393. نقشه برداری رقومی کلاس­های خاک با استفاده از انواع روش­های داده­کاوی در منطقه­ی اردکان استان یزد. مجله مهندسی زراعی(مجله علمی کشاورزی)، جلد 37، شماره2. صص. 115-101.
  3. جعفری، ا.، خادمی، ح.، و ایوبی، ش. 1391. نقشه­برداری رقومی افق­های مشخصه و گروه­های بزرگ خاک در منطقه زرند کرمان. مجله علوم و فنون کشاورزی و منابع طبیعی، علوم آب و خاک، شماره 62. صص. 177 تا 191.
  4. حریری، ع. 1374. نگرشی بر خاستگاه گروهی از سنگ های دگرگونه گستره قروه. پایان نامه کارشناسی ارشد، دانشگاه شهید بهشتی، تهران.
  5. حسینی، م. 1376. شرح نقشه زمین­شناسی 1:100000 چهار گوش قروه (پیوست نقشه)، سازمان زمین شناسی و اکتشاف معدنی کشور.
  6. صالحی، م. ح.، و خادمی، ح. 1387. مبانی نقشه­برداری خاک. انتشارات جهاد دانشگاهی اصفهان. 210 صفحه.
  7. گیوی، ج. 1376. ارزیابی کیفی تناسب اراضی برای نباتات زراعی و باغی، مؤسسه تحقیقات خاک و آب. نشریه فنی شماره 1015، 100صفحه.
  8. Anguilli, F. 2005. Fast condensed nearest neighbor rule. Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany.
  9. Asfaw, E.; Suryabhagavan, K.; Argaw, M. Soil salinity modeling and mapping using remote sensing and GIS: The case ofWonji sugar cane irrigation farm, Ethiopia. Saudi Soc. Agric. Sci. 2018, 17, pp. 250–258.
  10. Barbouchi, M.; Abdelfattah, R.; Chokmani, K.; Aissa, N.B.; Lhissou, R.; El Harti, A. Soil salinity characterization using polarimetric InSAR coherence: Case studies in Tunisia and Morocco. IEEE J. Sel. Top. Earth Obs. Remote Sens. 2014, 8,pp. 3823–3832.
  11. Behrens, T. Forster, H. Scholten, T. Steninrucken, U. Spies, E. and Goldschmitt, M. 2005. Digital soil mapping using artificial neural networks. Journal of Plant Nutrition and Soil Science. 168: pp. 21-33.
  12. Bouksila, F.; Bahri, A.; Berndtsson, R.; Persson, M.; Rozema, J.; Van der Zee, S.E. Assessment of soil salinization risks under irrigation with brackish water in semiarid Tunisia. Exp. Bot. 2013, 92, pp.176–185.
  13. Breiman, L. 2001. "Random forests" Machine learning. 45, pp. 5-32.
  14. Brovelli, M.A.; Sun, Y.; Yordanov, V. 2020. Monitoring forest change in the amazon using multi-temporal remote sensing data and machine learning classification on Google Earth Engine. ISPRS Int. J. Geo-Inf., 9, 580.
  15. Camera, C. Z. Zomeni, J.S. Noller, A.M. Zissimos, I.C. Christoforou, B. and A. Bruggeman. 2017. A high resolution map of soil types and physical properties for Cyprus: A digital soil mapping optimization. Geoderma 285, pp.35-49
  16. Cañedo-Argüelles, M.; Kefford, B.J.; Piscart, C.; Prat, N.; Schäfer, R.B.; Schulz, C.-J. 2013. Salinisation of rivers: An urgent ecological issue. Pollut., 173, pp.157–167.
  17. Dehaan, R.; Taylor, G. Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization. Remote Sens. Environ. 2002, 80, pp. 406–417.
  18. Dent, D.; Young, A. Soil Survey and Land Evaluation; George Allen & Unwin: Sydney, NSW, Australia, 1981.
  19. Ding, J.-L.;Wu, M.-C.; Liu, H.-X.; Li, Z.-G. Study on the soil salinization monitoring based on synthetical hyperspectral index. Spectrosc. Anal. 2012, 32, pp. 1918–1922.
  20. Dong, J.; Xiao, X.; Menarguez, M.A.; Zhang, G.; Qin, Y.; Thau, D.; Biradar, C.; Moore III, B. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sens. Environ. 2016, 185, pp. 142–154.
  21. El Harti, A.; Lhissou, R.; Chokmani, K.; Ouzemou, J.-E.; Hassouna, M.; Bachaoui, E.M.; El Ghmari, A. Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices. J. Appl. Earth Obs. Geoinf. 2016, 50, pp. 64–73.
  22. Elshewy, M.A., Mohamed, M.H.A. & Refaat, M. 2024. Developing a Soil Salinity Model from Landsat 8 Satellite Bands Based on Advanced Machine Learning Algorithms. J Indian Soc Remote Sens52, pp.617–632.
  23. Gorji, T.; Sertel, E.; Tanik, A. 2017. Monitoring soil salinity via remote sensing technology under data scarce conditions: A case study from Turkey. Indic., 74, pp. 384–391.
  24. Hengl, T. Rossiter, D.G. and Husnjak, S. 2002. Mapping soil properties from an existing national soil data set using freely available ancillary data. 17th World Congress of Soil Science. Thailand.
  25. Hengl, T. Toomanian, N. Reuter, H. and Malakouti, M. J. 2007. Methods to interpolate soil categorical variables from profile observations: Lessons from Iran. Geoderma 140: pp. 417-427.
  26. Hengl, T.; Heuvelink, G.B.M.; Kempen, B.; Leenaars, J.G.B.; Walsh, M.G.; Shepherd, K.D.; Sila, A.; MacMillan, R.A.; Jesus, J.; Tamene, L.; et al. 2015. Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions. PLoS ONE, 10, 0125814.
  27. Heung, B. Bulmer, C.E. and Schmidt, M.G. 2014. Predictive soil parent material mapping at a regional-scale: a random forest approach. Geoderma. 214, pp. 141-154.
  28. Heung, B. H.C. Ho, J. Zhang, A. Knudby, C.E. Bulmer, and Schmidt, M.G. 2016. An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping. Geoderma. 265, pp. 62-77
  29. Hosmer, D.W. and Lemeshow, S. (2000). Applied logistic regression. John Wiley & Sons. New York. pp 392.
  30. Jiang, H.; Shu, H. 2019. Optical remote-sensing data based research on detecting soil salinity at different depth in an arid-area oasis, Xinjiang, China. Earth Sci. Inform. 12,pp. 43–56.
  31. Li, J.; Pu, L.; Han, M.; Zhu, M.; Zhang, R.; Xiang, Y. 2014. Soil salinization research in China: Advances and prospects. J. Geogr. Sci. 24, pp. 943–960.
  32. Liu, J. Pattey, E. Nolin, M.C. Miller, J.R. and Ka, O. 2008. Mapping within-field soil drainage using remote sensing, DEM and apparent soil electrical conductivity. Geoderma,143, pp. 261–272.
  33. Ma, S.; He, B.; Ge, X.; Luo, X. 2023. Spatial prediction of soil salinity based on the Google Earth Engine platform with multitemporal synthetic remote sensing images. Ecol. Inform. 75, 102111.
  34. Mehnatkesh, A. Ayoubi, S. Jalalian, A. and Sahrawat, K. 2013. Relationships between Soil depth and terrain attributes in a semi arid hilly region in western Iran. Journal of Mountain Science 10, pp.163-172.
  35. Minasny, B. and McBratney, A. B. 2006. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers and Geosciences 32, pp.1378-1388.
  36. Moore, I. D. Grayson, R.B. and Ladson, A.R. 1991. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological processes. 5, pp. 3-30.
  37. Mougenot, B.; Pouget, M.; Epema, G. 1993. Remote sensing of salt affected soils. Remote Sens. Rev. 7, pp. 241–259.
  38. Nanni, M.R.; Demattê, J.A.M. 2006. Spectral reflectance methodology in comparison to traditional soil analysis. Soil Sci. Soc. Am. J. 70,pp. 393–407.
  39. Nemes, A. Rawls, W.J. and Pachepsky, Y.A. 2006. Use of the nonparametric nearest neighbor approach to estimate soil hydraulic properties. Soil Science Society of America Journal, 70,pp. 327–336.
  40. Pachepsky, Y.A. Timlin, D.J. and Rawls, W.J. 2001. Soil water retention as related to topographic variables. Soil Science Society of America Journal 65,pp. 1787–1795.
  41. Pahlavan Rad, M. R. Khormali, F. Toomanian, N. Brungard, C.W. Kiani, F. Komaki, C.B. and Bogaert, P. 2016. Legacy soil maps as a covariate in digital soil mapping: A case study from Northern Iran. Geoderma. 279, pp.141-148.
  42. Parastatidis, D.; Mitraka, Z.; Chrysoulakis, N.; Abrams, M. 2017. Online global land surface temperature estimation from Landsat. Remote Sens., 9, 1208.
  43. Schoeneberger, P.J. Wysocki, D.A. Benham, E.C. Soil Survey Staff. 2012. Field book for describing and sampling soils. 3nd Version. Natural Resources Conservation Service. National Soil Survey Center. Lincoln, NE.
  44. Scudiero, E.; Skaggs, T.H.; Corwin, D.L. 2017. Simplifying field-scale assessment of spatiotemporal changes of soil salinity. Sci. Total Environ. 587,pp. 273–281.
  45. Scull, P. Franklin, J. and Chadwick, O.A. 2005. The application of classification of tree analysis to soil type prediction in a desert landscape. Model. 181,pp. 1-15.
  46. Shi, H.; Hellwich, O.; Luo, G.; Chen, C.; He, H.; Ochege, F.U.; Van de Voorde, T.; Kurban, A.; De Maeyer, P. A. 2021. global meta-analysis of soil salinity prediction integrating satellite remote sensing, soil sampling, and machine learning. IEEE Trans. Remote Sens., 60, 4505815.
  47. Sreenivas, K. Dadhwal, V.K. Kumar, Harsha, G.S. Mitran, T. Sujatha, G. and Ravisankar, T. 2016. Digital mapping of soil organic and inorganic carbon status in India. Geoderma. 269, pp.160-173.
  48. Tian, A.; Fu, C.; Yau, H.-T.; Su, X.-Y.; Xiong, H. 2019. A new methodology of soil salinization degree classification by probability neural network model based on centroid of fractional lorenz chaos self-synchronization error dynamics. IEEE Trans. Remote Sens., 58,pp. 799–810.
  49. Triki Fourati, H.; Bouaziz, M.; Benzina, M.; Bouaziz, S. 2017. Detection of terrain indices related to soil salinity and mapping salt-affected soils using remote sensing and geostatistical techniques. Monit. Assess. 189, 177.
  50. Valavi, R. Elith, J. José, J. Lahoz, M. Gurutzeta, G. 2018. Block CV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models.Biorxiv.
  51. Wang, H.; Jia, G. 2012. Satellite-based monitoring of decadal soil salinization and climate effects in a semi-arid region of China. Atmos. Sci. 29,pp. 1089–1099.
  52. Wang, Q.; Li, P.; Chen, X. 2012. Retrieval of soil salt content from an integrated approach of combining inversed reflectance model and regressions: An experimental study. IEEE Trans. Remote Sens. 50,pp. 3950–3957.
  53. Wang, Z.; Zhang, F.; Zhang, X.; Chan, N.W.; Ariken, M.; Zhou, X.; Wang, Y. 2021. Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index. Total Environ. 775, 145807.
  54. Wu, D.; Jia, K.; Zhang, X.; Zhang, J.; Abd El-Hamid, H.T. 2021. Remote sensing inversion for simulation of soil salinization based on hyperspectral data and ground analysis in Yinchuan, China. Resour. Res. 30, pp. 4641–4656.
  55. Xiong, J.; Thenkabail, P.S.; Tilton, J.C.; Gumma, M.K.; Teluguntla, P.; Oliphant, A.; Congalton, R.G.; Yadav, K.; Gorelick, N. 2017. Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine. Remote Sens., 9, 1065.
  56. Zhang, Q.; Li, L.; Sun, R.; Zhu, D.; Zhang, C.; Chen, Q. 2020. Retrieval of the soil salinity from Sentinel-1 Dual-Polarized SAR data based on deep neural network regression. IEEE Geosci. Remote Sens. Lett., 19, 4006905.
  57. Zhang, Z.; Fan, Y.; Zhang, A.; Jiao, Z. 2022. Baseline-Based Soil Salinity Index (BSSI): A Novel Remote Sensing Monitoring Method of Soil Salinization. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 16,pp. 202–214.
  58. Zinck, J.K. 1989. Physiography and soil. Lecture notes for K6 course. Soil Division. Enschede, The Netherlands. 156 p.
  59. Zolfaghari, A. A. Tirgar Soltani, M. T. Dyck, M. and Weldeyohannes, A. 2013. Comparison of K-nearest neighbor and artificial neural network methods for predicting cation exchange capacity of soil. 50th anniversary Alberta soil science workshop. Book of Abstracts. p. 48.
Iranian Journal of Soil Research
Volume 38, Issue 2
October 2024
Pages 147-163

Files

Share

How to cite

Statistics