بررسی کارایی روش طیف‌سنجی مرئی-مادون قرمز نزدیک در تخمین برخی ویژگی‌‌های خاک منطقه‌ی سمیرم اصفهان

نوع مقاله : مقالات پژوهشی

نویسندگان

• فاطمه رحمتی ¹
• سعید حجتی ¹
• کاظم رنگزن ²
• احمد لندی ¹

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

² گروه سنجش از دور و GIS، دانشکده علوم زمین، دانشگاه شهید چمران اهواز، اهواز، ایران

https://doi.org/10.22067/jsw.2022.75858.1150

چکیده

اندازه‌گیری ویژگی‌های خاک در یک مقیاس وسیع به دلیل حجم بالای نمونه‌برداری و تجزیه‌های آزمایشگاهی، زمان‌بر و گران است. بنابراین استفاده از روش‌های ساده، سریع، ارزان و پیشرفته مانند طیف‌سنجی خاک می‌تواند مفید باشد. این مطالعه با هدف بررسی کارایی روش طیف‌سنجی در پیش‌بینی برخی از ویژگی‌های خاک در منطقه سمیرم استان اصفهان انجام شد. به این منظور تعداد200 نمونه خاک سطحی (10 سانتی‌متری) جمع‌آوری گردید. مقادیر کربن آلی، pH، EC وکربنات کلسیم معادل در آزمایشگاه اندازه‌گیری شدند. همچنین، طیف‌سنجی نمونه‌های خاک با استفاده از دستگاه طیف‌سنج زمینی FieldSpec3 درمحدوده طول موج 350 تا 2500 نانومتر انجام گرفت. سپس روش‌های پیش‌پردازش مشتق اول و مشتق دوم با فیلتر ساویتزکی گلای و متغیر نرمال استاندارد بر روی طیف‌ها انجام شدند. برای برقراری ارتباط بین ویژگی‌های خاک با ویژگی‌های طیفی آن از مدل‌های حداقل مربعات جزئی (PLSR)، رگرسیون مؤلفه اصلی (PCR)، شبکه عصبی مصنوعی (ANN) و رگرسیون ماشین بردار پشتیبان (SVMR) استفاده گردید. بهترین مدل در برآورد هدایت الکتریکی خاک، کربنات کلسیم و کربن آلی مدل PLSR و برای واکنش خاک مدل SVMR و بهترین روش‌های پیش‌پردازش، روش‌های مشتق‌گیری بودند که ضرایب تبیین آن‌ها به ترتیب 94/0، 88/0، 9/0 و 79/0 بودند و تمام برآوردها، کمترین RMSE را نسبت به روش‌های دیگر و 2 RPD> داشتند. به طور کلی نتایج این مطالعه بر قابلیت روش طیف‌سنجی مرئی مادون قرمز نزدیک در برآورد مکانی چندین ویژگی خاک به صورت همزمان، دلالت دارد. بنابراین، این روش می‌تواند به عنوان روشی جایگزین برای روش‌های مرسوم آزمایشگاهی در تعیین ویژگی‌های خاک مورد استفاده قرار گیرد.

کلیدواژه‌ها

• رگرسیون حداقل مربعات جزئی (PLSR)
• رگرسیون ماشین بردار پشتیبان (SVMR)
• رگرسیون مؤلفه اصلی (PCR)
• شبکه عصبی مصنوعی (ANN)
• طیف‌سنجی

20.1001.1.20084757.1401.36.2.9.3

موضوعات

• علوم خاک

عنوان مقاله [English]

Investigating the Efficiency of Visible-Near Infra-Red (NIR) Spectrometry to Estimate Selected Soil Properties in Semirom Area, Isfahan

نویسندگان [English]

• F. Rahmati ¹
• S. Hojati ¹
• K. Rangzan ²
• A. Landi ¹

¹ Department of Soil Science, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Khuzestan, Iran

² Department of GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran

چکیده [English]

Introduction
 Estimating soil properties on large scales using experimental methods requires specialized equipments and can be extremely time-consuming and expensive, especially when dealing with a high spatial sampling density. Soil Visible and Near-InfraRed (V-NIR) reflectance spectroscopy has proven to be a fast, cost-effective, non-destructive, environmental-friendly, repeatable, and reproducible analytical technique. V-NIR reflectance spectroscopy has been used for 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 objectives of this study were to estimate soil properties (carbonate calcium equivalent (CCE), electrical conductivity (EC), pH, and organic carbon (OC)) using visible near-infrared and short-wave Infrared (SWIR) reflectance spectroscopy (350-2500 nm). In this study, the best predictions of all the soil properties, model and pre-processing technique were also determined. The Partial Least Squares Regression (PLSR), Artificial Neural Network, Support Vector Machine Regression and Principal Component Regression (PCR) models were also compared to estimate soil properties.
Materials and Methods
 A total number of 200 surface soil samples (0-10 cm) were collected from the Semirom region (51º 17' - 52º 3' E; 30º 42' - 31º 51' N), Isfahan, Iran. The samples were air dried and passed through a 2 mm sieve, and using standard procedures soil properties were determined in the laboratory. Accordingly, soil pH and the EC contents of soil samples were determined in saturated pastes and extracts, respectively. The CCE content of the soils were measured using back titration, and the OC contents of the samples were measured using Walkley-Black method. The Reflectance spectra of all samples were measured using an ASD field spectrometer. The selection of the best model was done according to the value of the Ratio of Performance to Deviation (RPD), the coefficient of determination (R²), and the Root Mean Square Eerror (RMSE).
Results and Discussion
 Once the models were constructed using PLSR, ANN, SVMR and PCR approaches, descriptive analysis was carried out for each property, for the data measured in the laboratory. The parameters calculated for the properties were mean, coefficient of variation (CV), minimum and maximum, standard deviation and range. Coefficient of variation for the organic carbon, CCE, pH, and EC values were 21.7, 12.4, 1.34, and 28.74, respectively. Wilding (1985) proposed low, medium, and high variability for the CV values less than 15%, 15-35%, and greater than 35%, respectively. Accordingly, the organic carbon and EC of soils could be classified in the group with moderate variability. However, the calcium carbonate equivalent and pH are in the group with low variability. Since spectral data preprocessing has an effective role on improving the calibration, in order to perform spectral preprocessing, two first nodes at the first (350-400 nm) and the end (2450-2500 nm) of each spectrum were removed. In addition, two interruptions were eliminated, due to the change in the detector in the range of 900 to 1700 nm. Different preprocessing methods i.e., Standard Normal Variable (SNV) and First (FD) and Second Derivatives (SD) and Savitzky-Golay preprocessing techniques were performed on spectral data. Then, using PLSR, the cross‐validation method was used to evaluate soil properties calibration and validation. According to Stenberg (2002), for agricultural applications, The values of RPD greater than 2 indicate that the models provide precise predictions, the values of RPD between 1.5 and 2 are considered to be reasonably representative, and the values of RPD less than 1.5 indicate poor predictive performance. The results indicated the desirable capability of the PLSR method in estimating the EC (RPD > 2, R² = 0.94), CCE (RPD > 2, R² = 0.88), and OC (RPD > 2, R² = 0.89). The best results of the pH (RPD > 2, R² = 0.79) were estimated by the SVMR method. In this study the best methods of preprocessing techniques were First (FD) and Second Derivatives (SD) and Savitzky-Golay filter.
Conclusion
 In general, based on the results of this study, VNIR spectroscopy was successful in estimating soil properties and showed its potential for substituting laboratory analyses. Moreover, spectroscopy could be considered as a simple, fast, and low-cost method in predicting soil properties. The PLSR model with First and Second derivatives and Savitzky-Golay pre-processing techniques seems to be more robust algorithm for estimating EC, OC, and CCE. The best results of the pH were estimated by the SVMR method with First and Second derivatives and Savitzky-Golay pre-processing techniques.

کلیدواژه‌ها [English]

• Artificial Neural Network (ANN)
• Partial Least Squares Regression (PLSR)
• Principal Component Regression (PCR)
• Spectroscopy
• Support Vector Machine Regression (SVMR)

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