آنالیز حساسیت روش القای الکترومغناطیسی برای تعیین شوری خاک در مقیاس وسیع

نوع مقاله : مقالات پژوهشی

نویسندگان

• یوسف هاشمی نژاد ¹
• مهدی همایی ¹
• علی اکبر نوروزی ²

¹ دانشگاه تربیت مدرس

² پژوهشکده حفاظت خاک و آبخیزداری، تهران

https://doi.org/10.22067/jsw.v30i4.50288

چکیده

پایش و مدیریت خاک های شور مستلزم اندازه گیری دقیق و روزآمد هدایت الکتریکی خاک است. روش مستقیم اندازه گیری در مقیاس وسیع نه-تنها مستلزم صرف هزینه زیاد بلکه زمان بر نیز می باشد. لذا استفاده از حسگرهای نزدیک سطح زمین که شوری خاک را با دقتی قابل قبول تعیین کنند، از جهت صرفه جویی در وقت و هزینه حایز اهمیت فراوان می باشند. یکی از این شیوه های نسبتاً نوین روش القای الکترومغناطیس می باشد. هدایت الکتریکی ظاهری توده خاک اندازه گیری شده به روش القای الکترومغناطیس علاوه بر شوری خاک تحت تاثیر چند ویژگی کلیدی دیگر از جمله رطوبت و مقدار رس خاک می باشد. برای ارزیابی حساسیت القای الکترومغناطیس، رطوبت، مقدار رس خاک، هدایت الکتریکی عصاره اشباع خاک و هدایت الکتریکی ظاهری خاک مربوط به 2 سال در منطقه سبزوار اندازه گیری شد. داده های نخست که از آن ها برای برازش مدل استفاده شد، شامل 82 نقطه اندازه گیری هدایت الکتریکی ظاهری و نمونه برداری خاک بود که در نمونه های اخذ شده هدایت الکتریکی عصاره اشباع، بافت و رطوبت خاک نیز اندازه گیری شد. نمونه های سال دوم که از آن ها برای آزمون مدل استفاده شد، شامل 25 نقطه نمونه برداری و 9 نقطه قرائت هدایت الکتریکی ظاهری در اطراف هر نمونه بود. در نمونه های سال دوم تنها هدایت الکتریکی عصاره اشباع خاک اندازه گیری شد. نتایج سال اول نشان داد که بین قرائت دستگاه و هدایت الکتریکی عصاره اشباع خاک همبستگی معنی داری با ضریب تعیین 78 درصد برقرار است. هر چند رگرسیون چندگانه خطی با در نظر گرفتن رطوبت و مقدار رس خاک موجب ارتقای ضریب تعیین به حدود 80 درصد شد لیکن تاثیر مقدار رس در این مدل چندگانه معنی دار نبود. آنالیز حساسیت نیز نشان دهنده حساسیت بیشتر مدل نسبت به رطوبت خاک در مقایسه با مقدار رس خاک بود. از آن جا که خطا در برآورد رطوبت خاک منجر به خطای قابل ملاحظه ای در برآورد شوری خاک با استفاده از مدل رگرسیون چندگانه می شد، در نتیجه در سال دوم تحقیق مدل رگرسیون خطی ساده آزمون گردید. نتایج نشان داد که استفاده از میانگین قرائت 9 نقطه به جای یک نقطه می تواند همبستگی بین هدایت الکتریکی ظاهری و هدایت الکتریکی عصاره اشباع خاک را تا 98 درصد افزایش دهد. در نتیجه با توجه به حساسیت روش به رطوبت خاک دو روش برای افزایش دقت واسنجی پیشنهاد شد:- اندازه گیری و واسنجی در شرایط رطوبتی یکسان؛ - تخمین صحرایی رطوبت خاک و تقسیم بندی خاک ها به دو دسته مرطوب و خشک و اشتقاق دو دسته روابط واسنجی متفاوت.

کلیدواژه‌ها

• رگرسیون چندگانه
• رطوبت خاک
• شوری خاک
• هدایت الکتریکی ظاهری خاک

20.1001.1.20084757.1395.30.4.5.1

عنوان مقاله [English]

Sensitivity Analysis of Electromagnetic Induction Technique to Determine Soil Salinity in Large –Scale

نویسندگان [English]

• Yousef Hasheminejhad ¹
• Mahdi Homaee ¹
• Ali Akbar Noroozi ²

¹ Tarbiat Modares University

² Soil Conservation and Watershed Management Research Center

چکیده [English]

Introduction: Monitoring and management of saline soils depends on exact and updatable measurements of soil electrical conductivity. Large scale direct measurements are not only expensive but also time consuming. Therefore application of near ground surface sensors could be considered as acceptable time- and cost-saving methods with high accuracy in soil salinity detection. . One of these relatively innovative methods is electromagnetic induction technique. Apparent soil electrical conductivity measurement by electromagnetic induction technique is affected by several key properties of soils including soil moisture and clay content.
Materials and Methods: Soil salinity and apparent soil electrical conductivity data of two years of 50000 ha area in Sabzevar- Davarzan plain were used to evaluate the sensitivity of electromagnetic induction to soil moisture and clay content. Locations of the sampling points were determined by the Latin Hypercube Sampling strategy, based on 100 sampling points were selected for the first year and 25 sampling points for the second year. Regarding to difficulties in finding and sampling the points 97 sampling points were found in the area for the first year out of which 82 points were sampled down to 90 cm depth in 30 cm intervals and all of them were measured with electromagnetic induction device at horizontal orientation. The first year data were used for training the model which included 82 points measurement of bulk conductivity and laboratory determination of electrical conductivity of saturated extract, soil texture and moisture content in soil samples. On the other hand, the second year data which were used for testing the model integrated by 25 sampling points and 9 bulk conductivity measurements around each point. Electrical conductivity of saturated extract was just measured as the only parameter in the laboratory for the second year samples.
Results and Discussion: Results of the first year showed a significant correlation between electrical conductivity and apparent conductivity with a regression coefficient of 0.78. Although multiple linear regression by inclusion of soil moisture and clay content as independent variables improved the regression coefficient to 0.80 but the effect of clay content was not significant in this multiple model. Sensitivity analysis by maintaining one variable at its average value and changing the second variable also showed greater sensitivity of the model to soil moisture in comparison with soil clay content. Generally under estimation of soil moisture and over estimation of soil clay content produced about 63 to 65 percent Mean Bias Error (MBE) while over estimation of soil moisture and under estimation of soil clay content produced about 35- 37 percent of MBE. So the model is quite sensitive to both parameters and they cannot be estimated in the field by feeling and the other field methods. Simple linear regression model between ECe and EMh was tested on the second year because the errors in estimating soil moisture could be imposed a significant error on estimating soil salinity. Once the model was tested for estimation of soil salinity in the central point based on EMh reading at the center and then it was tested for estimation of soil salinity based on the average EMh of 9 points in each location. Results showed that the correlation between soil salinity and apparent soil electrical conductivity could be improved to 0.98 using the average of 9 measurements instead of 1 measurement.
Conclusion: Based on the results the electromagnetic induction device is sensitive to soil moisture. Although its sensitivity to clay content is less than the sensitivity to moisture content, but the total model error as a result of over estimating soil moisture is about equal to its error resulted from under estimating clay content and vice versa. So the field and feeling methods could not be implemented as inputs for the multiple regression models but these methods have enough accuracy to divide soil samples into two groups of dry and wet soils or sandy or clayey soils, on the other hand measurements of these parameters imposes more cost and time to soil salinity surveys. Results also showed that the repeated EM measurements around each sampling point could improve the strength of the regression. Therefore regarding to the sensitivity of the technique to soil moisture three methods are suggested to improve accuracy of calibration: a)- measurement and calibration under the same moisture conditions; b)- field approximation of soil moisture and dividing soil samples into two groups of dry and moist soils and deriving two different groups of calibration equations.

کلیدواژه‌ها [English]

• Bulk soil electrical conductivity
• Multiple regression
• Soil Moisture
• Soil salinity

مراجع

1- Brevik E.C., Fenton T.E., and Lazari A. 2006. Soil electrical conductivity as a function of soil water content and implication for soil mapping.Precision Agric. 7: 393- 404.

2- Corwin D.L., and Lesch S.M. 2005. Apparent soil electrical conductivity measurements in agriculture. Computers and Electronics in Agriculture. 46: 11– 43

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- Corwin D.L., Lesch S.M., Shouse P.J., Soppe R., and Ayars J.E. 2003. Identifying soil properties that influence cotton yield using soil sampling directed by apparent soil electrical conductivity. Agronomy Journal. 95 (2), 352– 364.

5- Doolittle J. A., and Brevik E.C. 2014. The use of electromagnetic induction techniques in soils studies, Geoderma, 223– 225: 33- 45.

6- Friedman S.P. 2005. Soil properties influencing apparent electrical conductivity: a review. Computer Electronic Agriculture. 46, 45– 70.

7- 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.

8- Homaee M., and Schmidhalter U. 2008. Water integration by plants root under non- uniform soil salinity. Irrigation Science. 27 (1): 83- 95.

9- Homaee M. 2002. Plants Response to Salinity. Iranian National Committee on Irrigation and Drainage (IRNCID). 105 pp. (in Persian).

10- Kachanoski R.G., De Jong E., and Van Wesenbeck I.J. 1990. Field scale patterns of soil water storage from non-contacting measurements of bulk electrical conductivity. Canadian Journal of Soil Science. 70, 537– 541.

11- Kachanoski R.G., Gregorich E.G., and Van Wesenbeck I.J. 1988. Estimating spatial variations of soil water content using non contacting electromagnetic inductive methods. Canadian Journal of Soil Science. 68, 715– 722.

12- Khorsandi F., Vaziri J., and Azizi Zohan A.K. 2010. Haloculture: Sustainable Use of Saline Soil and Water Resources in Agriculture. Iranian National Committee on Irrigation and Drainage, Tehran, Iran. 320 pp. (in Persian).

13- Klute A. 1986. Methods of Soil Analysis. Part 1. Physical and Mineralogical Methods. CAB Direct. 1188 pp.

14- Lesch S.M., Strauss D.J., and Rhoades J.D. 1995. Spatial prediction of soil salinity using electromagnetic induction techniques: 1. Statistical prediction models. Comparison of multiple linear regression and cokriging. Water Resources Research. 31, 373– 386.

15- Lobell D.B, Lesch S.M., Corwin D.L., Ulmer M.G., Anderson K.A., Potts D.J., Doolittle J. A., Matos M.R. and Baltes M.J. 2010. Regional scale assessment of soil salinity in the Red River valley using multi- year MODIS EVI and NDVI. Journal of Environmental Quality. 39 (1): 35- 41.

16- McNeill J.D. 1980. Electrical conductivity of soil and rocks. Technical Note TN-5, Geonics Ltd., Mississauga, Ontario, Canada.

17- Meshkat M.A., Rahimian M.H., and Taghizadeh-Mehrjardi R.H. 2013. Investigation of EM38 accurassy for monitoring of soil salinity and its comparing with traditional method (Case study: Yazd-Ardakan plain). Arid Biom Scientific and Research Journal. 3 (2): 72- 83. (in Persian with English abstract).

18- 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.

19- Misra R.K., and Padhi J. 2014. Assessing field-scale soil water distribution with electromagnetic induction method. Journal of Hydrology. 516: 200– 209.

20- Moameni A. 2011. Geographical Distribution and Salinity Levels of Soil Resources of Iran. Soil Research (Salinity Issue). 24 (3): 203- 216. (in Persian with English abstract).

21- 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.

22- Padhi J., and Misra R.K. 2011. Sensitivity of EM38 in determining soil water distribution in an irrigated wheat field. Soil and Tillage Research. 117, 93– 102.

23- 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. International Water Management Institute. 30p. (IWMI Working Paper 125).

24- Rahimian M.H., and Hasheminejhad Y. 2011. Calibration of Electromagnetic Induction Device (EM38) for Soil Salinity Assessment. 24 (3): 243- 252. (in Persian with English abstract).

25- Rhoades J. D., Waggoner B. L., Shouse P.J., and Alves W.J. 1989. Determining soil salinity from soil and soil-paste electrical conductivities: Sensitivity analysis of models. Soil Science Society of America. 53 (5): 1368- 1374.

26- Rhoades J.D. 1992. Instrumental field methods of salinity appraisal. In: Advances in Measurements of Soil Physical Properties: Bringing Theory into Practice, SSSA Special Publication 30. ASA, CSSA, SSSA, Madison, WI, USA, pp. 231– 248.

27- Rhoades J.D., Chanduvi F., and Lesch S. 1999. Soil salinity assessment, methods and interpretation of electrical conductivity measurements. FAO Irrigation and Drainage Paper 57.

28- Richards, L.A. 1954. Diagnosis and Improvement of Saline and Alkali Soils. Agric. Handbook no. 60, USDA.

29- 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.

30- Suarez D.L., and Simunek J. 1993. Modeling carbon dioxide transport and production in soil. 2. Parameter selection, sensitivity analysis and comparison of model predictions to field data. Water Resources Research. 29 (2): 499- 513.

31- Tromp-van Meerveld H.J., and Mc Donnell J.J. 2009. Assessment of multi-frequency electromagnetic induction for determining soil moisture patterns at the hillslope scale. J. Hydrol. 368, 56– 67.