برآورد درصد ذرات خاک با استفاده از روش طیف‌سنجی مرئی-مادون قرمز نزدیک در منطقه سمیرم اصفهان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه علوم و مهندسی خاک،دانشکده کشاورزی،دانشگاه شهید چمران اهواز،اهواز،خوزستان،ایران

2 عضو هیئت علمی گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، خوزستان، ایران

3 عضو هیئت علمی گروه سنجش از دور و GIS ،دانشکده علوم زمین ، دانشگاه شهید چمرا ن اهواز،اهواز ،خوزستان،ایران

4 عضو هیئت علمی گروه علوم و مهندسی خاک ،دانشکده کشاورزی،دانشگاه شهید چمران اهواز،اهواز،خوزستان،ایران

چکیده

مطالعه حاضر با هدف برآورد درصد ذرات خاک با استفاده از روش طیف‌سنجی مرئی و مادون قرمز نزدیک در منطقه سمیرم استان اصفهان انجام بود. تعداد 200 نمونه خاک سطحی (10 سانتی‌متری) از منطقه سمیرم اصفهان (طول جغرافیایی΄ 17 ˚ 51 تا΄3˚ 52 شرقی وعرض جغرافیایی΄ 42 ˚30 تا ΄51˚ 31 شمالی) جمع‌آوری گردید. نمونه‌ها هواخشک شدند و از الک دو میلی‌متری عبور داده شدند و درصد ذرات خاک در آزمایشگاه با روش هیدرومتری تعیین شد. همچنین طیف‌سنجی نمونه‌های خاک با استفاده از دستگاه طیف‌سنج زمینی انجام گرفت. سپس روش‌های پیش‌پردازش مشتق اول با فیلتر ساویتزکی گلای، تصحیح پخشیده چندگانه و متغیر نرمال استاندارد بر روی طیف‌ها انجام شدند. برای برقراری ارتباط بین درصد ذرات خاک با ویژگی‌های طیفی آن از مدل‌های رگرسیون حداقل مربعات جزئی، ماشین بردار پشتیبان و شبکه عصبی استفاده گردید. بهترین نتیجه برای برآورد سیلت با استفاده از شبکه عصبی مصنوعی با روش پیش‌پردازش تصحیح پخشیده چندگانه با RPD (نسبت انحراف معیار به RMSE) بیشتر از 2، 98/0=R2 و کمترین مقدار g/Kg 08/1=RMSE به‌دست آمد. نتایج مطلوبی نیز برای مدل شبکه عصبی مصنوعی به ترتیب با روش‌های پیش‌پردازش تصحیح پخشیده چندگانه و متغیر نرمال استاندارد برای مقادیر رس (RPD بیشتر از 2، 94/0=R2 و کمترین مقدار  g/Kg 21/1=RMSE-) و شن (انحراف پیش‌بینی باقی‌مانده بیشتر از 2، 84/0=R2 و کمترین مقدار g/Kg08/1=RMSE) به‌دست آمد. به طور کلی، براساس نتایج این مطالعه، طیف‌سنجی مرئی مادون قرمز نزدیک در برآورد درصد ذرات خاک موفق بوده است و قابلیت جانشینی با روش‌های آزمایشگاهی را دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Estmiating of Soil Particles Percentage Using Visible-Near Infra-Red (NIR) spectrometry in Semirom area, Isfahan

نویسندگان [English]

  • Fateme Rahmati 1
  • Saeid Hojati 2
  • Kazem Rangzan 3
  • Ahmad Landi 4
1 Ph.D. Student, Department of Soil Science, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran,.
2 Associate Professor, Department of Soil Science, College of Agriculture, Shahid Chamran University of Ahvaz
3 Professor, Department of Remote Sensing and GIS, Faculty of Earth Science, Shahid Chamran University of Ahvaz,
4 Professor, Department of Soil Science, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran,
چکیده [English]

 
The present research performed to estimate soil texture using visible near-infrared spectrometry in Semirom, Isfahan. A total number of 200 soil samples (0-10 cm) were collected from the Semirom area (51º 17' - 52º 3' E; 30º 42' - 31º 51' N), Isfahan. The samples were air dried and passed through a 2 mm sieve, and soil particles percentage was determined in the laboratory using hydrometry method. Reflectance spectra of all samples were measured using an ASD field spectrometer. Different pre-processing methods i.e., First Derivatives and Savitzky-Golay Filter, Multiplicative Scatter Correction and Standard Normal Variable were applied and performed on spectral data. The Partial Least Squares Regression, Support Vector Machine Regression and Artificial Neural Network models were used to estimate soil texture. The best result was obtained for Silt estimation, with excellent values of RPD >2, R2 =0.98 and RMSE=1.08 using Artificial Neural Network model with MSC pre-processing technique. The results indicated the desirable capability of Artificial Neural Network model with MSC and SNV pre-processing techniques in estimating the Clay (RPD >2, R2=0.94 and RMSE=1.21) and Sand (RPD >2, R2=0.84 and RMSE=6.24) contents of the soils, respectively. In general, based on the results of this study, VNIR spectroscopy was successful in estimating soil particles percentage and showed its potential for substituting laboratory analyses.

کلیدواژه‌ها [English]

  • Keywords: Artificial Neural Network
  • Partial Least Squares Regression (PLSR)
  • Pre-processing methods
  • Spectroscopy
  • Support Vector Machine Regression

Estmiating of Soil Particles Percentage Using Visible-Near Infra-Red (NIR) spectrometry in Semirom area, Isfahan

EXTENDED ABSTRACT

Introduction

Soil texture, describing the relative proportion of sand, silt and clay in the mineral phase of soils is a major determinant of its water storage capacity and permeability, aeration, bulk density, aggregate stability and carbon storage capacity. Soil texture measurement on large scales using experimental methods can be extremely time-consuming and expensive, especially when dealing with a high spatial sampling density. Soil Visible and Near-Infra Red (V-NIR) reflectance spectroscopy has proven to be a fast, cost-effective, nondestructive, environmental-friendly, repeatable, and reproducible analytical technique. V-NIR reflectance spectroscopy has been used more than 30 years to predict an extensive variety of soil properties like organic and inorganic carbon, nitrogen, organic carbon, moisture, texture and salinity. The objective of this study was to estimate soil texture using visible near-infrared and short-wave Infrared (SWIR) reflectance spectroscopy (350-2500 nm) in Semirom, Isfahan. In this study, the best predictions of all the soil particles percentage, model and pre-processing technique were also determined. The Partial Least Squares Regression (PLSR), Support Vector Machine Regression and Artificial Neural Network models were also compared to estimate soil texture.

 

Materials and Methods

A total number of 200 soil samples (0-10 cm) were collected from the Semirom area (51º 17' - 52º 3' E; 30º 42' - 31º 51' N), Isfahan. The samples were air dried and passed through a 2 mm sieve, and soil particles percentage was determined in the laboratory. Reflectance spectra of all samples were measured using an ASD field spectrometer. Different pre-processing methods i.e., First (FD) Derivatives and Savitzky-Golay Filter, Multiplicative Scatter Correction (MSC) and Standard Normal Variable (SNV) were applied and were performed on spectral data. The Partial Least Squares Regression (PLSR), Support Vector Machine Regression and Artificial Neural Network models were used to estimate soil texture. The selection of the best model was done according to the value of the Residual Prediction Deviation (RPD), the coefficient of determination (R2), and the Root MeanSquare Error (RMSE).

 

Results and Discussion

Coefficient of Variation (CV) values indicated that the variability of clay and silt were medium. However, the variability of Sand was high. The soil property of best Result was Silt, with excellent values of RPD >2, R2 =0.98 and RMSE=1.08 using Artificial Neural Network model with MSC pre-processing technique. The results indicated the desirable capability of Artificial Neural Network model with MSC and SNV pre-processing techniques in estimating the Clay (RPD >2, R2=0.94 and RMSE=1.21) and Sand (RPD >2, R2=0.84 and RMSE=6.24) contents of the soils, respectively.

 

Conclusions

In general, based on the results of this study, VNIR spectroscopy was successful in estimating soil particles percentage and showed its potential for substituting laboratory analyses. Further, spectroscopy could be considered as a simple, fast, and low-cost method in predicting soil properties.

 

 

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تحقیقات آب و خاک ایران
دوره 54، شماره 6
شهریور 1402
صفحه 915-931

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