Generating a Digital Map of Field Capacity and Permanent Wilting Point of Agricultural Soils in the Southern Part of Sistan Plain

Document Type : Research Paper

Authors

1 Department of Water Engineering, Faculty of Water and Soil, University of Zabol

2 Soil and Water Research Institute, agricultural research, education and extension organization (AREEO), Karaj, Iran

3 Soil and Water Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran

10.22092/ijsr.2023.361678.699

Abstract

This study aimed to estimate the spatial distribution of soil properties including field capacity (FC), permanent wilting point (PWP) and  total available water (TAW) using ordinary kriging (OK) and random forest (RF) in agricultural lands of the southern Sistan Plain, covering an area of approximtely 147000 hectars. FC, PWP, and TAW and soil texture components were measured for a total of 200 surface soil samples (0-30 cm). Performance evaluation of the two methods based on the percentage of normalized root mean square error (nRMSE) revealed that the conventional OK with 5% less error in estimating FC, 3% less error in estimating PWP, and 5% less error in estimating TAW performed slightly better than RF. Comparing Bias values showed that OK underestimates both FC and PWP and overestimates TAW, while RF overestimates all three parameters. Spatial distribution maps of FC, PWP, and TAW produced by OK model showed that the highest amount of FC (23%) and TAW (14.4%) were in the west and northeast of the region, which had heavier texture and lower altitude from the sea level. In the southern and southeastern regions, which have lighter soil texture, the amount of available water was less compared to the western and northeastern regions. In the RF model, the most important variable extracted from satellite images was Digital Elevation Model  (DEM), and all three features had higher values in areas where DEM was lower. It seems that the flatness of the study area and the inadequacy of auxiliary variables caused the lower accuracy of the RF method.

Keywords

Main Subjects

References

  • آقا نباتی، س. ع. 1385. زمین‌شناسی و توان معدنی استان سیستان و بلوچستان. نشریه رشد آموزش علوم زمین، شماره 45.ص 11-4.
  • احمدی، ک.، عبادزاده، ح. ر.، حاتمی، ف،. محمدنیا افروزی، ش.، اسفندیاری، ا و طاقانی، ر.ع. 1400. آمارنامه کشاورزی سال 1399. مرکز فناوری اطلاعات و ارتباطات وزرات جهاد کشاورزی. 92 ص.
  • ادب، ح. 1396. برآورد رطوبت لحظه‌ای سطح خاک در فصل سرد با استفاده از داده‌های سنجش از دور نوری و حرارتی در شرایط بدون نمناکی. نشریه علوم آب و خاک (علوم و فنون کشاورزی و منابع طبیعی). 21 (2):ص 191-175.
  • پهلوان راد، م. ر. 1398. بررسی تغییرات مکانی نفوذپذیری و هدایت هیدرولیکی اشباع خاک دراراضی دشت سیستان با استفاده از زمین آمار و درختان تصمیم‌گیری تصادفی. گزارش نهایی موسسه خاک و آب. 58 ص.
  • تازه، م.، اسدی، م. و کلانتری، س. 1394. ارزیابی قابلیت شاخص های ژئومورفومتری در استخراج نقشه شبکه آبراهه (مطالعه موردی، حوزه سه قلعه- همبوسرایان)، نشریه علمی پژوهش های ژئومورفولوژی کمی، سال چهارم. شماره1، ص 144-134.
  • رفاهی، ح. ق. 1388. فرسایش آبی و کنترل آن. انتشارات دانشگاه تهران. تهران. 633 ص.
  • صحرایی، ن.، لندی، ا و حجتی، س.1401. نقشه‌برداری رقومی اجزا بافت خاک در بخشی از اراضی دشت خوزستان با استفاده از برخی مدل‌های یادگیری ماشین. مجله تحقیقات خاک و آب ایران (دانشگاه تهران شماره 10. ص 2276-2261.
  • فاضلی سنگانی، م.، شرفا، م. و سرمدیان، ف. 1389. درون­یابی و پهنه­بندی میزان رطوبت حد ظرفیت مزرعه و نقطه پژمردگی دائم. مجله آبیاری و زهکشی ایران. دوره چهارم. شماره 2. ص 262-251.
  • علی احیایی، م. و بهبهانی زاده،ع.ا. 1372. نشریه فنی. شماره 892. 129 ص.
  • Aitkenhead, M. J., and M. C. 2016. Mapping soil carbon stocks across Scotland using a neural network model. Geoderma. 262: 187-198.‏
  • Allbed, A. and L. Kumar. 2013. Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. Advances in Remote Sensing. 2(4) : 373-385, DOI: 4236/ars.2013.24040
  • Arnold, J. G. and J.R.Williams. 1987. Validation of SWRRB: simulator for water resources in rural basins. Journal of Water Resources Planning and Management. 113(2): 243–256.
  • Arnold, J.G. and N. Fohrer. 2005. SWAT2000: current capabilities and research opportunities in applied watershed modeling. Hydrological Processes. 19(3): 563–572.
  • Babaeian, E., M. Sadeghi, S.B. Jones, C. Montzka, H.Vereecken and M. Tuller. 2019. Ground, proximal, and satellite remote sensing of soil moisture. Rev. Geophys. 57: 530–616.
  • Baig, M.H.A., L. Zhang, T. Shuai and Q. Tong. 2014. Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters. 5(5): 423-431.‏
  • Baret, F. and G. Guyot.1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment. 35(2-3): 161-173.‏
  • Berry, W.D. 1993. Understanding regression assumptions. quantitative applications in the social sciences. Sage Publications. 92:104P.
  • Betz, F., J. Rauschenberger, M. Lauermann,and B. Cyffka. Using GIS and remote sensing for assessing riparian ecosystems along the Naryn River Kyrgyzstan. International Journal of Geoinformatics. 12 (4): 25-30.‏
  • Brisson, N., B. Mary, D. Ripoche, M.H. Jeuffroy, F. Ruget, and B. Nicoullaud. 1998. STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomy Journal. 18(5–6): 311–346.
  • Böhner, J. and T. Selige. Spatial prediction of soil attributes using terrain analysis and climate regionalisation. Göttinger Geographische Abhandlungen. 115:13-28.
  • Cambardella, C.A., A.T. Moorman, J.M. Novak, T.B. Parkin, D.L. Karlen, R.F. Turco, and A.E. Konopka. 1994. Field-scale heterogeneity of soil properties in central Iowa soils. Soil Science Society of America Journal. 58: 1501–1511.
  • Datta, S., S. Taghvaeian, and J. Stivers. 2017.Understanding soil water content and thresholds for irrigation management. Oklahoma Cooperative Extension Fact Sheets. BAE-1537. http://osufacts.okstate.edu
  • Djurovic, Z., B. Kovacevic and V. Barroso. 2000. QQ-plot based probability density function estimation. In Proceedings of the Tenth IEEE Workshop on Statistical Signal and Array Processing . 243-247.‏
  • Hengl, T., B.M. Heuvelink, B. Kempen, J.G.B. Leenaars, M. G.Walsh, K.D. Shepherd, A. Sila, R.A. MacMillan, J. M. Jesus, L.Tamene and J.E. Tondoh. 2015. Mapping soil properties of Africa at 250 m resolution: random forests significantly improve current predictions. PLOS ONE. 10 (6): doi: 10.1371/journal.pone.0125814.
  • Hounkpatin, O.K., F.O. de Hipt, A. Y. Bossa, G. Welp, and W. Amelung. 2018. Soil organic carbon stocks and their determining factors in the Dano catchment (Southwest Burkina Faso). Catena. 166: 298-309.‏
  • Mishra, U., R. Lal, D. Liu and M.Van Meirvenne. 2010. Predicting the spatial variation of the soil organic carbon pool at a regional scale. Soil Science Society of America Journal. 74(3): 906-914.
  • Mohammadi, J. 2006. Pedometer: 2, spatial statistics (Geostatistics). Pelk Publication, Tehran, 453 p. (In Persian).
  • Navidi, M.N., J. Seyedmohammadi, and S.A. Seyed Jalali. 2022. Predicting soil water content using support vector machines improved by meta-heuristic algorithms and remotely sensed data. Geomechanics and Geoengineering. 17(3): 712-726.
  • Moriasi, D.N., J.G. Arnold, M.W. Van Liew, R. L., Bingner, R.D. Harmel , and T. L.Veith. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. American Society of Agricultural and Biological Engineers. 50(3): 885−900.
  • Ottoy, S., B. De Vos, A. Sindayihebura, M. Hermy, and J.Van Orshoven. 2017. Assessing soil organic carbon stocks under current and potential forest cover using digital soil mapping and spatial generalisation. Ecological Indicators. 77: 139-150.‏
  • Parton, W.J., D.S. Schimel, C.V. Coleand D.S. Ojima. 1987. Analysis of factors controlling soil organic matter levels in Great Plains Grasslands. Soil Science Society of America 51(5): 1173–1179.
  • Qiao, J., Y. Zhu,, X. Jia, L. Huang and M.A. Shao.2019. Pedotransfer functions for estimating the field capacity and permanent wilting point in the critical zone of the Loess Plateau, China. Journal of Soils and Sediments, 19: 140-147.
  • Rouse, J.W., R.H. Haas, J.A. Schell, D.W. Deering, and C. Harlan. 1974. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation (No. E75-10354).
  • Shahriari, M., M. Delbari, P. Afrasiab, and M.R. Pahlavan-Rad. 2019. Predicting regional spatial distribution of soil texture in floodplains using remote sensing data: A case of southeastern Iran. Catena. 182:104-
  • Skentos, A. 2018. Topographic position index based landform analysis of Messaria (Ikaria Island, Greece). Acta Geobalcanica. 4(1):7-15
  • Szabo, B., G. Szatmari, K. Takacs, A. Laborczi, A. Mako, K. Rajkai and I. Pasztor. 2019. Mapping soil hydraulic properties using random-forest-based pedotransfer functions and geostatistics. Hydrology and Earth System Sciences. 23: 2615-2635.
  • Taghizadeh-Mehrjardi, R., B. Minasny, F. Sarmadian, and B.P. Malone. 2014. Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma. 213: 15-28.‏
  • Terribile, F., A. Coppola, G. Langella, M. Martinaand A. Basile. 2011. Potential and limitations of using soil mapping information to understand landscape hydrology. Hydrology and Earth System Sciences. 15: 3895–3933.
  • Wilding, L. and L.R. Drees. 1983. Spatial variability and pedology. In Developments in Soil Science. Elsevier. 11: 83-116 ‏
  • Zhang, H., P. Wu,A.Yin , X. Yang, M. Zhangand C. Gao. 2017. Prediction of soil organic carbon in an intensively managed reclamation zone of eastern China: A comparison of multiple linear regressions and the random forest model. Science of the Total Environment. 592: 704-713.
Iranian Journal of Soil Research
Volume 37, Issue 2
September 2023
Pages 183-198

Files

Share

How to cite

Statistics