تحلیل روش طیف‌سنجی مادون‌قرمز و روش‌های پیش‌پردازش در پیش‌بینی شوری خاک

میزان شوری خاک یکی از عوامل تاثیرگذار بر مدیریت آبیاری و مزرعه می‌باشد اما اندازه‌گیری شوری با روش‌های معمول آزمایشگاهی امری وقت‌گیر وپر هزینه می‌باشد. لذا استفاده از روش طیف‌سنجی مادون‌قرمز در اندازه‌گیری میزان شوری خاک به‌عنوان روشی سریع و غیرمخرب ارزشمند خواهد بود. در این تحقیق طیف‌های بازتابی، جذبی و مشتق اول طیف بازتابی خاک‌های با شوری طبیعی و مصنوعی در سطوح مختلف شوری  (ds.m1 1.2 تا 307.5) در بافت‌های خاک سبک تا سنگین توسط روش‌های مختلف هموارسازی پردازش‌شده و جهت پیش‌بینی شوری خاک از مدل رگرسیونی حداقل مربعات جزئی (plsr) استفاده شد، با توجه به ضرایب تبیین (r2) و مجذور میانگین مربعات خطا (rmse) استفاده از طیف جذبی نسبت به طیف‌های بازتابی و مشتق اول طیف بازتابی نتایج دقیق‌تری را ارائه نمود. همچنین پیش‌بینی شوری خاک با استفاده از طیف جذبی در هر دو دسته خاک سنگین و سبک روشی کارآمد و موفق بود، هرچند این روش در خاک‌های با بافت سنگین (0.836 =r2) نتایج دقیق‌تری را در پیش‌بینی شوری خاک نسبت به خاک سبک (0.756 =r2) ارائه کرد. همچنین مشخص گردید که روش‌های پردازش میانگین متحرک و فیلتر ساویتزکی گلای بیشترین تاثیر را بر بهبود نتایج پیش‌بینی شوری خاک ارائه نمودند.

Analysis of Infrared Spectroscopy and Preprocessing Methods in Soil Salinity Prediction

Introduction: Although the soil salinity as an effective factor on soil and water management is typically assessed by measuring the soil electrical conductivity (ECe), this conventional laboratory method is timeconsuming and costly. Therefore, nearinfrared spectroscopy (NIR) as a fast, cheap and nondestructive method to assess soil salinity level can be considered as a valuable alternative method. Reviews of literature on the application of NIR spectroscopy for soil salinity prediction have shown that there is no sufficient information about the effect of soil texture on results accuracy; therefore, in this study the soil salinity was predicted under different soil salinity levels and various soil textures. The effect of different preprocessing methods was also investigated to improve the predicted soil salinity.;Materials and Methods: Twenty three surface soil samples were collected from different places in Fars province, then; some soil properties such as percentage of particles size and ECe were measured. These samples were artificially salted by adding the water in different salinity levels to the soil samples. The ECe of these soils were between 2.1 to 307.5 dS/m and then all samples dried to reach the field capacity level. Soil reflectance spectra were obtained in 3502500 nm wavelength range. The absorbance and derivative of reflectance spectra were calculated based on the reflectance spectra. In order to determine the effect of smoothing technique, as a preprocessing method, 4 various methods (moving average, Gaussian, median and SavitzkyGolay filters) in 12 different segment sizes (3,5,7,9,11,13,15,17,19,21,23 and 25) were applied and the processed spectra introduced to Partial Least Square Regression (PLSR) model to predict soil salinity in two calibration and validation steps. At the first step, the soil salinity was predicted for all samples using of reflectance, absorbance and derivative of reflectance spectra under 4 preprocessing methods and 12 segment sizes. According to the R2 and RMSE indices, the best type of spectra, the effect of various preprocessing methods and the best segment size in prediction of soil salinity were determined as absorbance spectra, moving average and SavitzkyGolay filters for segment size of 25 and 15, respectively. In the second step, the effect of soil texture on prediction accuracy was investigated. For this purpose, soil samples were divided into the coarse and fine textures and soil salinity was predicted for each of these groups using different preprocessing methods and different segment sizes.;Results and Discussion: In prediction of soil salinity by absorbance, reflectance and derivative of reflectance spectra, the R2 values in validation step were 0.742, 0.706 and 0.670; and RMSE values were 29.92, 31.96 and 33.9 (dS.m1), respectively. The absorbance spectra were the best spectra type in prediction of soil salinity. Therefore, in next step, absorbance spectra were used only for predicting the salinity in fine and coarse soil textures. Results showed that the prediction in coarse texture was better than that of the fine texture (R2= 0.836 and R2=0.756, respectively). It was also revealed that the highest R2 occurred in coarse texture and the accuracy of prediction was reduced in fine textures. The results showed that the performance of different preprocessing methods is related to the spectrum type. Although the preprocessing methods had no positive effect in using of reflectance spectra, but it improved the predicted values which were obtained using of absorbance and derivative of reflectance spectra. The best results were occurred when the absorbance spectra were used. Moving average method increased the accuracy of prediction more than the other preprocessing methods, and according to the results this method, for the segment size of 25, was the best technique in soil salinity prediction.;Conclusion: According to the R2 and RMSE indices, the prediction of soil salinity by absorbance spectra was more accurate than the prediction using reflectance and derivative of reflectance spectra (R2= 0.742, 0.706 and 0.670, respectively). Although the predicted soil salinity in coarse soils were more accurate than that in fine soils. Using of absorbance spectra to predict the soil salinity in all soil textures was efficient. The results showed that using of preprocessing methods improved the soil salinity prediction by absorbance and derivative of reflectance spectra, and the moving average and SavitzkyGolay filter were the best preprocessing methods.