برآورد برخی خصوصیات خاک با استفاده از تحلیل داده‌های طیفی (Vis-NIR) و انواع روش‌های پیش‌پردازش

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید باهنر کرمان، کرمان، ایران

2 استاد گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید باهنر کرمان، کرمان، ایران

10.22059/ijswr.2021.320713.668918

چکیده

طیف‌سنجی خاک به‌دلیل سرعت و دقت بالا، هزینه پایین و غیرمخرب بودن بر بسیاری از محدودیت­های روش­های سنتی تجزیه خاک غلبه کرده است. مطالعه حاضر با هدف امکان­سنجی استفاده از اطلاعات طیفی خاک به‌منظور برآورد برخی از ویژگی­های کلیدی خاک و مقایسه انواع روش­های پیش­پردازش طیفی در تعیین عملکرد مدل رگرسیون حداقل مربعات جزئی (PLSR) انجام گردید. بدین منظور 100 نمونه خاک سطحی از اراضی واقع در حدفاصل شهرستان­های نی­ریز تا استهبان در شرق استان فارس جمع­آوری و مقادیر کربن ­آلی، قابلیت هدایت الکتریکی، کربنات کلسیم معادل و گچ با استفاده از روش­های ­استاندارد آزمایشگاهی اندازه­گیری گردید. سپس بازتاب طیفی نمونه­های خاک در محدوده 2500-350 نانومتر ثبت و روش­های مختلف پیش­پردازش طیفی بر روی داده­ها اعمال گردید. در ادامه، برآورد خصوصیات خاک با استفاده از روش PLSR انجام شد. نتایج حاکی از توانایی مطلوب روش PLSR در تخمین میزان گچ (2< RPD، 81/0 = R2، 87/3 = RMSE) و توانایی قابل قبول آن برای سایر ویژگی­ها نظیر کربنات ­کلسیم معادل، کربن آلی و قابلیت هدایت الکتریکی خاک (2>RPD> 4/1) بود. همچنین، نتایج نشان داد که روش ­پیش­پردازش مشتق اول به همراه فیلتر ساویتزکی و گلای، بهترین مدل­سازی را برای کربن آلی، روش متغیر نرمال استاندارد (SNV) برای گچ و روش مشتق دوم به همراه فیلتر ساویتزکی و گلای برای قابلیت هدایت الکتریکی خاک ارائه کردند. از طرفی، برآورد کربنات کلسیم خاک با داده­های بدون پیش­پردازش، تخمین بهتری نسبت به استفاده از انواع روش­های پیش­پردازش ارائه داد. به‌طور کلی، نتایج نشان داد که محدوده طیف مرئی برای برآورد کربن آلی و قابلیت هدایت الکتریکی و محدوده مادون قرمز نزدیک برای کربنات کلسیم معادل و گچ کارایی بهتری ارائه دادند.

کلیدواژه‌ها


عنوان مقاله [English]

Estimation of Some Soil Properties Using Spectral Data Analysis (Vis-NIR) and Various Pre-Processing Methods

نویسندگان [English]

  • Sahar Taghdis 1
  • Mohammad Hady Farpoor 2
  • Majid Fekri 2
  • Majid Mahmoodabadi 2
1 PhD Student, Department of Soil Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
2 Department of Soil Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
چکیده [English]

Soil spectroscopy has overcome many limitations of conventional soil analysis methods due to its rapid, accurate, cost-effective, and non-destructive nature. This study was aimed to investigate the capability of soil spectral data in estimating some key soil properties and comparing different spectral preprocessing methods in determining the performance of the partial least squares regression (PLSR) model. For this purpose, 100 soil surface samples were collected from the study area which was located between Neyriz and Estahban regions in the east of Fars Province. The samples were analyzed for organic carbon (OC), electrical conductivity (EC), calcium carbonate equivalent (CaCO3) and gypsum using standard laboratory methods. Then, the spectral reflectance of the soil samples was recorded in the range of 350-2500 nm and various spectral pre-processing methods were applied to the data. Afterwards, the soil properties were estimated using PLSR. The results indicated the desirable capability of PLSR method in estimating the amount of gypsum (RPD >2, R2 = 0.81, RMSE = 3.87) and its acceptable ability for OC, EC and CaCO3 (2< RPD < 1.4). Also, the best modeling systems for OC, gypsum and EC were obtained as the first derivative with Savitzky-Golay smoothing method (FD-SG), the standard normal variate method (SNV), and the second derivative with SG smoothing method (SD-SG), respectively. Besides, non-preprocessing data of soil CaCO3 provided better estimations than various pre-processing methods. Overall, the results revealed that the visible spectrum range provided the best performance for estimating of OC and EC, and the NIR range for CaCO3 and gypsum.

کلیدواژه‌ها [English]

  • Absorption bands
  • Soil spectral behavior
  • VIS-NIR spectroscopy
  • PLSR
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