آناليز حساسيت روش القاي الكترومغناطيسي براي تعيين شوري خاك در مقياس وسيع

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

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

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.