برآورد مقدار مادة آلی خاک، با استفاده از داده‌های طیفی و مدل‌های آماری رگرسیون حداقل مربعات جزئی و رگرسیون مؤلفة اصلی

نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشیار گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه لرستان

2 دانشجوی دکتری علوم خاک دانشگاه لرستان

3 دانشجوی دکتری مدیریت منابع خاک، دانشکده کشاورزی، دانشگاه لرستان

چکیده

مادة آلی خاک از مهم‌ترین ویژ‌گی‌های فیزیکوشیمیایی خاک است که در تعیین کیفیت و مدیریت آن نقش بسزایی دارد. مقدار کربن آلی خاک دچار تغییرات زمانی و مکانی بالایی می‌شود و بنابراین، تعیین آن در آزمایشگاه هزینه‌بر، مشکل و وقت‌گیر است. طیف‌سنجی مرئی‌ـ مادون قرمز نزدیک، از نظر کاهش زمان و هزینه، روش توجیه‌پذیری است که برای بررسی کربن آلی خاک پیشنهاد شده است. هدف این پژوهش بررسی کربن آلی خاک ازطریق طیف‌‌سنجی مرئی‌ـ مادون قرمز نزدیک و برآورد آن با استفاده از مدل‌های آماری، PCA، PLSR و PCR است. برای این منظور، چهل نمونه خاک از عمق صفر تا 30 سانتی‌متری، به‌روش سیستماتیک تصادفی، برمبنای مطالعات پیشین و تعیین طبقات متفاوت خاک‌های منطقه، برداشت شد. تجزیة شیمیایی خاک‌ها طبق روش‌های استاندارد صورت گرفت. بازتاب طیفی نمونه‌های خاک در محدودة طیفی ٣٥٠ تا 2500 نانومتر اندازه‌گیری شد و پس از اعمال روش‌های پیش‌‌پردازش فیلتر ساویتزکی‌ـ گلای، به‌کمک تحلیل مؤلفه‌های اصلی (PCA)، رگرسیون حداقل مربعات جزئی(PLSR)  و رگرسیون مؤلفة اصلی (PCR)، کربن آلی خاک برآورد شد. نتایج این پژوهش نشان داد که فیلتر ساویتزکی‌ـ ‌گلای قوی‌ترین روش پیش‌پردازش داده‌های طیفی بوده است. نتایج مدل‌‌سازی به‌روش PLSR  نشان داد مقادیر R2، RMSE و RPD در مرحلة اعتبارسنجی، برای پیش‌بینی مادة آلی، به‌ترتیب 85/0، 14/0 و 78/2 بوده درحالی‌که نتایج مدل‌سازی PCR، برای پارامترهای آماری یادشده، به‌ترتیب 78/0، 19/0 و 05/2 است و دقت بیشتر روش آماری PLSR را، در مقایسه با روش PCR، برای مدل‌سازی برآورد کربن آلی خاک، می‌رساند. بنابراین، به‌نظر می‌رسد مدل PLSR، برای پیش‌بینی سریع کربن آلی خاک‌های مناطق خشک و نیمه‌خشک، کارآیی و دقت بیشتری داشته باشد.

کلیدواژه‌ها

عنوان مقاله [English]

Estimation Soil Organic Matter (SOM) Content Using Visible and Near Infrared Spectral data, PLSR and PCR Statistical Models

نویسندگان [English]

  • Hamidreza Matinfar 1
  • H Mahmodzadeh 2
  • A Fariabi 3
1 Associate Professor Soil Science, remote sensing and GIS
2 Ph.D. Student of Soil Science, Lorestan University, Soil Science, Genesis and Classification
3 Ph.D. Student of Soil Science Department, Lorestan University, Soil Science, Genesis and Classification
چکیده [English]

Soil organic matter is one of the most important Physical and chemical properties of soil that it iscritical in determining the quality and management of soils. Quantify of soil organic carbon due to thehigh spatial variability and changes over time is difficult. Near-infrared-visible spectroscopy is afeasible method to reduce the time and cost to check the organic carbon. The aim of this study was toevaluate soil organic carbon through near-infrared and visible spectroscopy with the statistical modelsPLSR and PCR. For this purpose, 40 soil samples from depths of 0 to 30 cm were collected bysystematic random method based on previous studies and determination of different classes of soils inthe region. Chemical analysis of soils was performed according to standard methods. Spectralreflectance of soil samples in the range of 350 to 2500 nm was measured then after applying thepreprocessing methods such as Savitzky and Golay filter, Soil organic carbon were calculated byprincipal component analysis (PCA), regression partial least squares (PLSR) and principal componentregression (PCR) models. The results of this study showed that the Savitzky and Golay filter was thestrongest preprocessing method for spectral data. Coefficients of determination (R2), root mean squareerror of Prediction (RMSE) and ratio of prediction to deviation (RPD) in the calibration andvalidation to predict organic matter, respectively, 0.97, 0.05, 5.09 and 0.85, 0.14, 2.78 respectively.Therefore, for dry and semi-arid soils of the PLSR model, it is more efficient to predict the organiccarbon of the soil. The results showed that the PLSR model has better performance than the PCRmodel in soil organic carbon estimation.

کلیدواژه‌ها [English]

  • organic matter
  • spectroscopy
  • PLSR
  • PCR
  • Principal Component Analysis

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