مدل سازی آماری شوری خاک در پهنه های گسترده

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه تربیت مدرس

2 سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران

چکیده

شور شدن خاک‌ها در جهان به گونه‌ای روزافزون روبه گسترش است و درنتیجه تولید محصولات کشاورزی در مواجهه با این تنش کاهش می‌یابد. سیاست‌گذاران و تصمیم‌سازان در راستای برنامه‌ریزی برای تطبیق با تغییرات اقلیمی و افزایش نیاز به غذا نیازمند پایش کمی مستمر شوری خاک می-باشند. شاخص‌های طیفی حاصل از سنجنده‌های ماهواره‌ای و یا سنجنده‌های نزدیک به سطح زمین به‌طور روزافزونی برای پایش شوری خاک مورداستفاده قرار می‌گیرند به‌نحوی‌که تا کنون تعداد زیادی شاخص برای پایش شوری خاک معرفی شده‌اند. برای مدل‌سازی و سنجش اعتبار مدل حاصله روش‌های رگرسیونی مختلفی مورداستفاده قرار گرفته که مهم‌ترین آن‌ها رگرسیون خطی چندگانه (شامل رگرسیون گام‌به‌گام، انتخاب رو به جلو و حذف رو به عقب) و رگرسیون حداقل مربعات جزئی است. در این پژوهش به‌منظور ارزیابی این دو روش در مدل‌سازی تغییرات شوری خاک از اندازه-گیری‌های آزمایشگاهی و الکترومغناطیسی شوری خاک مربوط به 97 نقطه در سال 1392 و 225 نقطه در سال 1393 در بخشی از دشت سبزوار- داورزن به مساحت حدود 50 هزار هکتار استفاده شد. تعداد 23 شاخص طیفی از تصاویر ماهواره لندست 8 مربوط به تاریخ‌های نمونه‌برداری استخراج و به همراه مدل رقومی ارتفاع به‌عنوان متغیر مستقل مورداستفاده قرار گرفت. روش‌های مختلف رگرسیون خطی چندمتغیره با استفاده از داده‌های سال اول به‌عنوان آموزش و سال دوم به‌عنوان آزمون و بالعکس هرچند ضریب تبیین بین حدود 22 تا 88 درصد ایجاد کرد، ولی این همبستگی در دسته اعتبار سنجی از 29 درصد تجاوز نکرد. به علت وجود هم‌راستایی خطی چندگانه در بین متغیرهای مستقل روش رگرسیون خطی چندگانه برای تمام متغیر‌ها قابل کاربرد نبود. حذف متغیرهای دارای هم‌راستایی خطی، تبدیل لگاریتمی و تصادفی کردن کل داده‌ها در دو دسته آموزش و آزمون، ضریب رگرسیون مدل و اعتبار آن را به‌طور قابل قبولی افزایش داد. استفاده از رگرسیون حداقل مربعات جزئی با استفاده از داده‌های اصلی و تبدیل لگاریتمی شده سال اول و دوم به‌عنوان آموزش و آزمون و بالعکس نیز در دسته آموزش ضریب تبیین بین 39 تا 85 درصد ایجاد کرد، ولی از برآورد در دسته آزمون ناتوان بود. تصادفی کردن داده‌ها و تقسیم مجدد آن‌ها به دو دسته آموزش و آزمون موجب ارتقای چشمگیر ضریب تعیین در دسته اعتبارسنجی شد. تکرار عملیات تصادفی کردن نشان داد که روش از ثبات لازم برای برآورد ضرایب متغیرها برخوردار است.

کلیدواژه‌ها


عنوان مقاله [English]

Statistical Modeling of Soil Salinity on Large Scale

نویسندگان [English]

  • Yousef Hasheminejhad 1
  • Mehdi Homaee 1
  • Ali Akbar Noroozi 2
1 Tarbiat Modares University
2 Agricultural Research, Education and Extension Organization (AREEO), Tehran
چکیده [English]

Introduction: Soil salinization is increasing across developing world countries and agricultural production is decreasing as a result of this stress. Climate change could adversely affect soil salinization trend through the decrease in rainfall and increased evapotranspiration in arid regions. Policy and decision makers require continuous and quantitative monitoring of soil salinity to adapt with the adverse effects of climate change and increasing need for food. Indices derived from near surface or satellite based sensors are increasingly applied for monitoring of soil salinity so a considerable number of these indices are introduced already for soil salinity monitoring. Different regression methods have been already used for modeling and verification of developed models amongst them multiple linear regression (including stepwise, forward selection and backward elimination) and partial least square regression are the most important methods.
Materials and Methods: To evaluate different approaches for modeling soil salinity against remotely sensed data, an area of about 50000 ha was selected in Sabzevar- Davarzan plain during 2013 and 2014 years. The locations of sampling points were determined using Latin Hypercube Sampling (LHS) strategy. Sampling density was 97 points for 2013 and 25 points for 2014. All points were sampled down to 90 cm depth in 30 cm increments. Totally 366 soil samples were analyzed in the laboratory for electrical conductivity of saturated extract. Electromagnetic induction device (EM38) was also used to measure bulk soil electrical conductivity for the sampling points at the first year and sampling points and 8 points around it at the second year. Totally 97 and 225 EM measurements were also recorded for first and second years respectively. Mean measured soil EC data were calibrated against the EM measurements. Finding the fair correlations, the EM and EC data could be converted to each other. 23 spectral indices derived from Landsat 8 images in the sampling dates along with DEM were used as independent variables. Multiple Linear Regression (MLR) and Partial Least Square Regression (PLSR) methods were evaluated for their fitness in predicting soil salinity from independent variables in different calibration and verification datasets.
Results and Discussion: Different multiple linear regression approaches using the first year data for training and second year data for testing the models and vice versa were evaluated which produced determination coefficients of about 22 to 88 percent in the training dataset but this regression did not reach to 29 percent in the test dataset. Due to the multiple co-linearity amongst the independent variables the multiple linear regression methods were not applicable to all variables. Excluding the co-linear variables, log- transforming and randomizing them into train and test datasets improved the determination coefficient of model and its validation at an acceptable level. Application of partial least square regression using the original and log- transformed data of first and second years as train and test datasets and vice versa introduced determination coefficients of about 39 to 85 percent in the training dataset but were not able to predict in the test dataset. Random dividing of all data into train and test datasets considerably increased the determination coefficient in the verification dataset. Repeating the randomization showed that the approach has the required consistency for predicting the coefficients of variables.
Conclusions: Wide range of independent variable could be used for predicting soil salinity from remotely sensed data and indices. On the other hand the independent variables generally show multi-colinearity amongst themselves. Correlation matrix, variance inflation factor and tolerance indices could be used to identify multi-colinearity. Removing or scaling the variable with high colinearity could improve the regression. Different data transformation methods including log- transformation could also significantly improve the strength of regression. In this research EM data showed more significant correlations with spectral indices in comparison with laboratorial measured EC data. As the EM38 device measures the reflectance in special range of spectrum this higher correlation could be expected. Such models should be calibrated and verified against ground truth data. Generally a part of data set is used for calibrating (making the model) and the remained for verifying (testing the model). Random dividing of the total data of 2 years into calibration (2/3 of data) and verification (1/3 of data) could significantly improve the regression in the verification data set. This procedure increases the range of variability for data used for calibration and verification and prevents outlier predictions.

کلیدواژه‌ها [English]

  • Multiple linear regression
  • Partial least square regression
  • Remote sensing
  • spectral indices
  • Verification
1. Aldabaa A.A., Weindorf D.C., Chakraborty S., Sharma A. and Lid B. 2015. Combination of proximal and remote sensing methods for rapid soil salinity quantification. Geoderma, 239–240: 34–46.
2. Brunner P., Li H.T., Kinzelbach W. and Li W.P. 2007. Generating soil electrical conductivity maps at regional level by integrating measurements on the ground and remote sensing data. International Journal of Remote Sensing, 28, 3341–3361.
3. Corwin D.L. and Lesch S.M. 2014. A simplified regional-scale electromagnetic induction — Salinity calibration model using ANOCOVA modeling techniques. Geoderma, 230–231: 288–295
4. Douaoui A.E.K., Nicolas H. and Walter C. 2006. Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data. Geoderma, 134: 217–230.
5. Driessen P.M. and Schoorl R. 1973. Mineralogy and morphology of salt efflorescences on saline soils in the Great Konya Basin. Turkey Journal of Soil Science, 24: 436–442.
6. Eldeiry A.A. and Garcia L.A. 2010. Comparison of ordinary kriging, regression kriging, and cokriging techniques to estimate soil salinity using Landsat images. Journal of Irrigation and Drainage Engineering, 136: 355–364.
7. Farifteh J., Farshad A. and George R.J. 2006. Assessing salt-affected soils using remote sensing, solute modelling, and geophysics. Geoderma, 130: 191–206.
8. Farifteh J., van der Meer F., Atzberger C. and Carranza E. 2007. Quantitative analysis of salt affected soil reflectance spectra: a comparison of two adaptive methods (PLSR and ANN). Remote Sensing and Environment, 110: 59–78.
9. Fernandez-Buces N., Siebe C., Cram S. and Palacio J.L. 2006. Mapping soil salinity using a combined spectral response index for bare soil and vegetation: a case study in the former lake Texcoco, Mexico. Journal of Arid Environments, 65: 644–667.
10. Furby S., Caccetta P. and Wallace J. 2010. Salinity monitoring in Western Australia using remotely sensed and other spatial data. Journal of Environmental Quality, 39: 16–25.
11. Ghassemi F., Jakeman A.J. and Nix H.A. 1995. Salinisation of land and water resources: Human causes, management and case studies. University of New South Wales Press, Sydney, Australia.
12. Ghorbani Dashtaki S., Homaee M. and Khodaverdiloo H. 2010. Derivation and validation of pedotransfer functions for estimating soil water retention curve using a variety of soil data. Soil Use and Management. 26(1): 68-74.
13. Golovina N.N., Minskiy D., Pankova Y. and Solovyev D.A. 1992. Automated air photo interpretation in the mapping of soil salinization in cotton-growing zones. Mapping Sciences and Remote Sensing, 29: 262–268.
14. Homaee M., Feddes R.A. and Dirksen C. 2002. A macroscopic water extraction model for nonuniform transient salinity and water stress. Soil Science Society of America Journal, 66 (6): 1764- 1772.
15. Homaee M. and Schmidhalter U. 2008. Water integration by plants root under non-uniform soil salinity. Irrigation Science, 27(1):83-95.
16. Khodaverdiloo H., Homaee M. van Genuchten M.T. and Ghorbani Dashtaki S. 2011. Deriving and validating pedotransfer functions for some calcareous soils. Journal of Hydrology, 399(1): 93-99.
17. Klute A. 1986. Methods of soil analysis. Part 1. Physical and mineralogical methods. CAB Direct. 1188 pp.
18. Lal R., Iivari T. and Kimble J.M. 2004. Soil Degradation in the United States: Extent, Severity, and Trends. CRC Press, Boca Raton, FL, USA.
19. Lobell D.B. 2010. Remote sensing of soil degradation: introduction. Journal of Environmental Quality, 39: 1-4.
20. Metternicht G. 1998. Analysing the relationship between ground based reflectance and environmental indicators of salinity processes in the Cochabamba Valleys (Bolivia). International Journal of Ecology and Environmental Sciences 24: 359–370.
21. Metternicht G.I. and Zinck J.A. 2003. Remote sensing of soil salinity: potentials and constraints. Remote Sensing and Environment, 85: 1–20.
22. Minasny B. and McBratney A.B. 2006. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers and Geosciences, 32: 1378–1388.
23. Mougenot B., Pouget M. and Epema G. 1993. Remote sensing of salt-affected soils. Remote Sensing Reviews, 7: 241–259.
24. Nawar S., Buddenbaum H. Hill J. and Kozak J. 2014. Modeling and Mapping of Soil Salinity with Reflectance Spectroscopy and Landsat Data Using Two Quantitative Methods (PLSR and MARS). Remote Sensing, 6(11): 10813-10834.
25. Noroozi A.A., Homaee M. and Farshad A. 2012. Integrated Application of Remote Sensing and Spatial Statistical Models to the Identification of Soil Salinity: A Case Study from Garmsar Plain, Iran. Environmental Sciences, 9(1): 59-74.
26. Qadir M., Qurshi A.S. and Cheraghi S.A.M. 2007. Extent and characterization of salt-affected soils in Iran and strategies for their amelioration and management. Land Degradation and Development, 19: 214-227.
27. Rao B., Sankar T., Dwivedi R., Thammappa S., Venkataratnam L., Sharma R. and Das S. 1995. Spectral behaviour of salt-affected soils. International Journal of Remote Sensing, 16: 2125–2136.
28. Richards L.A. 1954. Diagnosis and improvement of saline and alkali soils. Agricultural Handbook no. 60, USDA.
29. Rodriguez P.G., Gonzalez M.E.P. and Zaballos A.G. 2007. Mapping of salt affected soils using TM images. International Journal of Remote Sensing, 28: 2713–2722.
30. Scudiero E., Skaggs T.H. and Corwin D.L. 2014. Regional scale soil salinity evaluation using Landsat 7, western San Joaquin Valley, California, USA. Geoderma Regional. 2-3: 82-90.
31. Shao Y., Hu Q., Guo H., Lu Y., Dong Q. and Han C. 2003. Effect of dielectric properties of moist salinized soils on backscattering coefficients extracted from RADARSAT image. IEEE Trans. Geosciences and Remote Sensing, 41: 1879–1888.
32. Taghizadeh-Mehrjardi R., Minasny B., Sarmadian F. and Malone B. 2014. Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma 213: 15–28.
33. Tanji K.K. and Wallender W.W. 2012. Nature and extent of agricultural salinity and sodicity. In: Wallender W.W., Tanji K.K. (eds.) Agricultural Salinity Assessment and Management. ASCE Manuals and Reports on Engineering Practices No. 71. ASCE, Reston. VA, USA, pp. 10-25.
34. Udelhoven T., Emmerling C. and Jarmer T. 2003. Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: A feasibility study. Plant and Soil 251: 319–329.
35. Wold S., Sjostrom M. and Eriksson L. 2001. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58: 109–130
36. Wu W., Mhaimeed A.S., Al-Shafie W.M., Ziadat F., Dhehibi B., Nangia V. and De Pauwa E. 2014. Mapping soil salinity changes using remote sensing in Central Iraq. Geoderma Regional, 2–3: 21–31.