مقایسه تخمین شوری خاک با استفاده از روش‌های طیف سنجی، القاءگر الکترومغناطیس و سنجش از دور

نوع مقاله : مقاله کامل علمی پژوهشی

نویسندگان

1 استادیار گروه علوم و مهندسی خاک، دانشگاه کردستان

2 دانش آموخته کارشناسی ارشد گروه علوم و مهندسی خاک، دانشگاه کردستان

3 استادیار گروه علوم و مهندسی خاک، دانشگاه کردستان،

چکیده

سابقه و هدف: شوری خاک یکی از خصوصیات بسیار مهم خاک بوده و بررسی تغییرات مکانی آن، جهت مدیریت زراعی، تخریب اراضی و مطالعات زیست محیطی حائز اهمیت می‌باشد. شوری خاک با استفاده از هدایت الکتریکی (EC) اندازه‌گیری می‌شود و تخمین مقادیر شوری خاک با استفاده از این روش‌های آزمایشگاهی گران و زمان‌بر است. بنابراین، جمع آوری اطلاعات در مورد توزیع مکانی شوری خاک در مناطق گسترده نیاز به تکنیک‌های جدید ارزان دارد. اخیراً تکنیک‌های جدیدی از قبیل طیف‌سنجی مرئی-مادون قرمز نزدیک، القاءگر الکترومغناطیس و سنجش از دور برای اندازه گیری شوری خاک به کاربرده شده است. هدف از این پژوهش تخمین شوری خاک با استفاده از روش‌های طیف‌سنجی مرئی - مادون قرمز نزدیک، القاءگر الکترومغناطیس و سنجش از دور می‌باشد.
مواد و روش‌ها: منطقه مورد مطالعه در 20 کیلومتری شمال شرقی شهرستان قروه در استان کردستان واقع شده و سطحی معادل 26000 هکتار را در بر‌می‌گیرد. 100 نمونه خاک (عمق 30-0 سانتی‌متری) جمع آوری و هدایت الکتریکی خاک در عصاره اشباع اندازه‌گیری شد. متغیرهای کمکی استفاده شده در این مطالعه، داده‌های طیفی خاک در محدوده مرئی - مادون قرمز نزدیک، قرائت‌های روش القاءگر الکترومغناطیس و داده‌های سنجده ETM+ لندست 8 بودند. در 100 مکان نمونه‌برداری، قرائت‌های افقی و عمودی با استفاده از EM38 قرائت شده و شاخص شوری، شاخص NDVI، شاخص روشنایی و باندهای 1، 2، 3، 4، 5، 6 و 7 با استفاده از نرم افزار Arc GIS و داده‌های سنجده ETM+ لندست 8 محاسبه و استخراج شدند. افزون بر این، 100 نمونه خاک با استفاده از طیف‌سنج زمینی (مدل FieldSpec®3, ASD, FR, USA) با طول موج 2500- 350 نانومتر تحت اسکن قرار گرفتند. جهت ارتباط دادن بین شوری خاک و متغیرهای کمکی این سه روش از مدل شبکه عصبی مصنوعی استفاده گردید. در نهایت شوری خاک با استفاده از مدل شبکه عصبی مصنوعی برآورد شده و با استفاده از روش اعتبارسنجی متقاطع مورد ارزیابی قرار گرفت.
یافته‌ها: مقادیر شوری خاک کم تا زیاد بودند (47/14 -23/0 دسی‌زیمنس بر متر). بیشینه مقادیر شوری خاک در مناطق مرکزی (اراضی پست و بایر) و کمینه مقادیر شوری خاک در اراضی مرتفع و مرتعی مشاهده شد. بر اساس آنالیز حساسیت، مدل شبکه عصبی مصنوعی در روش سنجش از دور، شاخص شوری، شاخص NDVI، باند 7 و باند 3 مهم‌ترین متغیرها برای پیش‌بینی شوری خاک بودند، به طور کلی، این نتایج نشان داد که مهم‌ترین متغیرهای کمکی برای پیش‌بینی شوری خاک به ترتیب داده‌های طیفی خاک در محدوده مرئی - مادون قرمز نزدیک، قرائت عمودی و داده‌های سنجش از دور بودند. روش طیف‌سنجی مرئی - مادون قرمز نزدیک برای پیش‌بینی شوری خاک دارای مقادیر 62/0، 94/0 و 0.28/0 به ترتیب برای ضریب تبیین، میانگین خطا و میانگین ریشه مربعات خطا بود و در مقایسه با القاءگر الکترومغناطیس و سنجش از دور بهتر بود اگر چه تلفیق سه روش (طیف‌سنجی مرئی - مادون قرمز نزدیک، القاءگر الکترومغناطیس و سنجش از دور) با هم بهترین نتایج جهت تخمین شوری خاک را داشت.
نتیجه‌گیری: مهمترین متغیر کمکی برای پیش‌بینی شوری خاک در منطقه داده‌های طیفی خاک در محدوده مرئی - مادون قرمز نزدیک بود. روش القاگر الکترومغناطیس هم متغیر مناسبی جهت پیش‌بینی شوری خاک بوده و می‌تواند به عنوان یک روش ارزان، دقیق و سریع برای پیش‌بینی شوری خاک توصیه شود. تلفیق سه روش (طیف‌سنجی مرئی - مادون قرمز نزدیک، القاءگر الکترومغناطیس و سنجش از دور) با هم بهترین نتایج جهت تخمین شوری خاک را داشت. بنابراین، پیشنهاد می‌شود که مدل شبکه عصبی مصنوعی و داده‌های کمکی همچون داده‌های طیفی روش طیف‌سنجی مرئی - مادون قرمز نزدیک و القاگر الکترومغناطیس در مطالعات آینده استفاده شود.

کلیدواژه‌ها

عنوان مقاله [English]

Comparison of estimation of soil salinity using spectroscopy, electromagnetic induction, and remote sensing

نویسندگان [English]

  • kamal nabiollahi 1
  • Kamran Azizi 2
  • Masoud Davari 3
1 Kurdistan university
2 Soil Science and Engineering, Faculty of Agricultural, University of Kurdistan
3 Soil Science and Engineering, Faculty of Agricultural, University of Kurdistan
چکیده [English]

Background and objectives: Soil salinity is one of the most important soil properties and it's variability investigation is essential to crop management, land degradation and environmental studies. Soil salinity is measured using electrical conductivity (EC) and estimation of soil salinity contents using experimental methods is expensive and time consuming. Therefore, the collection of information on the spatial distribution of soil salinity in n vast areas requires new inexpensive techniques. Recently, new techniques such as electromagnetic induction, visible - near infrared spectroscopy and remote sensing were applied to measure soil salinity. The purpose of this study is the estimation of soil salinity using visible- near infrared spectroscopy, electromagnetic induction, and remote sensing methods.
Materials and Methods: The study area is located 20 km northeast of Ghorveh city in Kurdistan Province and covers a surface of 26000 hectares. 100 soil samples (0–30 cm depth) were collected and. soil electrical conductivity was measured in a saturated extract. Applied auxiliary data in this study were spectral information of visible - near infrared spectroscopy method, reading of electromagnetic induction method, and ETM+ data of Landsat 8. In the 100 sampling sits, horizontal and vertical readings were read using EM38 and salinity index (SI) and normalized difference vegetative index (NDVI), bright index, and Bands 1, 2, 3, 4, 5, 6 , and 7were computed and extracted using Landsat 8 ETM+ data and Arc GIS software. Moreover, the 100 samples were scanned using spectrometer (model of FieldSpec®3, ASD, FR, USA) with a spectral range of 350 to 2500 nm. To make a relationship between soil salinity and auxiliary data of the three methods, artificial neural network (ANN) model were applied. Finally, soil salinity were estimated using ANN and were validated using cross validation method.
Results and Discussion: Soil salinity contents were low to high (0.23 to 14.47 dSm-1). The highest contents of soil salinity were observed in central regions (low and bare land) and the lowest contents of soil salinity were located in high and range land. Based on sensitive analysis of artificial neural network model, in remote sensing methods salinity index, NDVI index, band 7, and band 3 were the most variables to predict soil salinity. In general, the results showed the most important auxiliary variables to predict soil salinity were spectral information of visible - near infrared range, vertical reading, and remote sensing data, respectively. Soil visible - near infrared spectroscopy method to predict soil salinity had 0.94, 0.27 and 0.64, respectively for determination of coefficient (R2), mean error (ME), and root mean square root (RMSE) and was better compared to the electromagnetic induction, remote sensing although combination of three methods together had the best results to estimate soil salinity.
Conclusion: The most important auxiliary data to predict soil salinity in the study area was spectral information of visible - near infrared range. Electromagnetic induction method also is suitable auxiliary data to predict soil salinity and it can recommend as speed, accurate and cheap method to predict soil salinity. Combination of three methods together (electromagnetic induction, visible - near infrared spectroscopy and remote sensing) had the best results to estimate soil salinity.
Therefore, it is suggested to predict soil salinity, ANN model and auxiliary data such as spectral information of visible - near infrared spectroscopy method and electromagnetic induction will be applied in the future studies.

کلیدواژه‌ها [English]

  • Spectral range of visible - near infrared
  • EM38
  • Salinity index
  • Artificial neural network

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مجله مدیریت خاک و تولید پایدار
دوره 9، شماره 1
فروردین 1398
صفحه 101-117

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