روش برآورد رطوبت‌خاک با استفاده از تکنیک سنجش از دور توسط ماهواره Landsat

نوع مقاله : مقالات پژوهشی

نویسندگان

• سید فرشید موسوی زاده ¹
• حسین انصاری ²
• عیرضا فریدحسینی ³

¹ دانشجوی دکتری، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران

² استاد، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران

³ دانشیار، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران

https://doi.org/10.22067/jsw.2021.71524.1070

چکیده

خشکسالی یک فاجعه طبیعی پیچیده است که در سطح جهان زیاد اتفاق می‌افتد. رطوبت‌خاک به عنوان مستقیم‌ترین و مهم‌ترین متغیر توصیف خشکسالی، از جمله اطلاعات اساسی برای نظارت از راه دور بر وقایع خشکسالی و تخمین عملکرد محصول می‌باشد. برای کاهش نمونه‌برداری میدانی و استفاده همزمان از مدل‌های گیاهی برای تخمین عملکرد محصول استفاده از تصاویر ماهواره‌ای سهل‌ترین راه حل می‌باشد. در این پژوهش، با استفاده از روشی رطوبت‌خاک با فضای بازتاب طیفی نزدیک به مادون قرمز در مقابل باند طیفی قرمز (NIR- Red) تخمین و توسعه داده شد. در ابتدا فضای انعکاس طیفی NIR-Red پس از تصحیحات اتمسفری به صورت نمودار با استفاده از تصاویر ماهواره Landsat 7 و سنجنده ETM⁺ با روش اصلاح شده هندسی ایجاد شد. سپس با استفاده از معادله خط برازش شده در این نمودارها، مقادیر با محاسبات ریاضی به رطوبت حجمی تبدیل و با میانگین مقادیر رطوبت‌خاک اندازه‌گیری شده در دشت نیشابور (خراسان‌رضوی) در وسعت 13 هکتار در شش روز در طی دوران کشت محصول مقایسه و اعتبارسنجی شد. نتایج نشان داد برآورد رطوبت‌خاک که با روش هندسه فضایی در سطح خاک صورت گرفت با توجه به تطابق شش تصویر ماهواره‌ای از لحاظ زمان با اندازه‌گیری‌های میدانی، شاخص آماری NRMSE برابر 18 درصد بدست آمد که می‌توان دقت انجام کار را به غیر از زمان‌های 28 نوامبر و 30 دسامبر که وضعیت ابرناکی وجود داشت و باعث شد تصویربرداری خطای بیشتری داشته باشد، رضایت‌بخش دانست. بنابراین نتیجه‌گیری شد که مدل ساده و کارآمد هندسه فضایی Red-NIR توانایی زیادی برای تخمین رطوبت سطح خاک در شرایط جوی مساعد را داشته باشد و می‌توان از این روش برای مدل‌سازی گیاهی به عنوان اطلاعات ورودی استفاده نمود.

کلیدواژه‌ها

• آب‌خاک
• بازتاب طیفی
• محاسبات ریاضی
• مدل هندسه فضایی Red-NIR

20.1001.1.20084757.1400.35.6.7.2

موضوعات

• علوم آب

عنوان مقاله [English]

Soil Moisture Estimation Method Using Remote Sensing Technique by Landsat Satellite

نویسندگان [English]

• S.F. Mousavizadeh ¹
• H. Ansari ²
• A. R. Faridhoseini ³

¹ Ph.D. Candidate, Professor and Associate Professor, Department of Water Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

² Professor, Department of Water Science and Engineering, Ferdowsi University of Mashhad

³ Associate Professor, Department of Water Science and Engineering, Ferdowsi University of Mashhad

چکیده [English]

Introduction: In the last decade, satellite-based methods, including remote sensing and microwave methods, have been used in many studies to detect soil surface moisture regionally. Thermal remote sensing method is quite effective for checking moisture for bare soil but shows poor correlation for vegetated surfaces. In addition, there is a widespread use of this method in the presence of temperature differences during the day. Satellite imagery enables the ability to measure humidity according to the environmental conditions at the surface. Thus, compared to field measurements, remote sensing techniques are promising because they are capable of spatial measurements at a relatively low cost. Water supply is one of the main causes of evapotranspiration, which can affect it. Soil moisture can be considered as the most direct and important variable describing drought and is the main parameter describing water circulation and energy exchange between the surface and the atmosphere. Scale reduction methods for soil moisture can be divided into three main groups including satellite-based method, GIS data and model-based methods. The same methods have been used extensively in monitoring soil moisture for different spectral patterns at different wavelengths, from visible to microwave remote sensing data. Spectral reflectance decreases with increasing soil moisture in the visible and near-infrared (NIR) range. Therefore, these methods can be used to estimate soil moisture using satellite data for water budgeting and other meteorological and agricultural applications.
Materials and Methods: In this study, using the information provided by Zaki (2013), the measured humidity by the sensor was compared with the humidity obtained from the satellite. The soil moisture were measured in 16 points from an area of 13 hectares from Neyshabour plain of Khorasan Razavi province. The novelty of this study is to provide a simple method for using Landsat 7 satellite imagery to estimate the surface moisture of areas of the Earth to eliminate field sampling and optimal use for agriculture. One of the advantages of this method is the reduction of information obtained from the field as input values for crop modeling that can be used to estimate crop yield, so the moisture measured during the winter wheat crop period from November 2012 to March 2013 was used.
Results and Discussion: The placement of band numbers 3 and 4 opposite each other to calculate M, the line equation was fitted. Since satellite imagery is not performed daily by satellite, six images were extracted during the growing season. On November 12, which is actually 12 days after planting, the plant is entering the germination stage and the soil is mostly bare. Because the satellite does not receive enough reflected green light, the accuracy of the image in measuring soil moisture decreases, but after the plant grows, the green light is reflected and the amount of digital digit of band 4 is affected, as a result, the amount of moisture in the plant leaves and stem is involved in measuring soil moisture, which is consistent with the results obtained by Petropoulos et al.
Conclusion: In general, the results of this study showed that the simple and efficient Red-NIR spatial geometry model has a great ability to estimate soil surface moisture in favorable weather conditions and this method can be used for plant modeling as input data.

کلیدواژه‌ها [English]

• Mathematical calculations
• Red-NIR spatial geometry model
• Spectral reflection
• Soil water

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