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برآورد رطوبت سطحی خاک در اراضی کشاورزی با استفاده از تصاویر ماهوارهای و شاخصهای سنجش از دور (مطالعه موردی: شهرستان شوشتر) | ||
تحقیقات آب و خاک ایران | ||
دوره 53، شماره 5، مرداد 1401، صفحه 957-970 اصل مقاله (1.81 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2022.341981.669251 | ||
نویسندگان | ||
محمد عبیات1؛ مصطفی عبیات* 2؛ مرتضی عبیات2 | ||
1کارشناسارشد مهندسی منابع طبیعی- محیطزیست، دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران(خوزستان)، اهواز، ایران | ||
2دانشجوی دکتری جغرافیا و برنامه ریزی روستایی، دانشکده علوم جغرافیایی و برنامهریزی، دانشگاه اصفهان، اصفهان، ایران | ||
چکیده | ||
برآورد رطوبت خاک برای مدیریت بهینه منابع آب و خاک ضروری است. تصاویر لندست با قدرت تفکیک مکانی و زمانی مناسب، ابزار مناسبی در این مطالعات میباشند. هدف این مطالعه، برآورد و پهنهبندی رطوبت سطحی خاک در اراضی کشاورزی شهرستان شوشتر در استان خوزستان با استفاده از شاخصهای سنجش از دور میباشد. برای این کار، ابتدا 25 نمونه خاک اراضی کشاورزی از عمق 15-0 سانتیمتری زمین برداشت شده و رطوبت آنها اندازهگیری شد. سپس، شاخصهای اختلاف پوششگیاهی نرمال شده (NDVI)، شاخص گیاهی با تنظیم انعکاس خاک (SAVI)، دمای سطح زمین (LST)، اختلاف رطوبتی نرمال شده (NDMI)، شاخص تفاضلی نرمال شده کشاورزی (NDTI) و شاخص رطوبت خاک باند کوتاه مادونقرمز (SMSWIR) بر روی تصویر لندست 8 اعمال شد. در مرحله بعد، مقادیر این شاخصها برای اجرای رگرسیون آماری به نرمافزار SPSS منتقل شده و توابع برآورد رطوبت خاک به روش رگرسیون خطی چند متغیره به دست آمد. نتایج نشان داد؛ با توجه به ضریب تبیین بالا (73/0) و پایین بودن ریشه میانگین مربعات خطا (31/1) در روش هم زمان (Enter Method)، این مدل برای برآورد و پهنهبندی رطوبت سطحی خاک اراضی کشاورزی در منطقه مناسب ارزیابی شد. براساس نتایج تحقیق رطوبت سطحی خاک با شاخصهای NDVI، SAVI، NDMI، NDTI و SMSWIR رابطه مستقیم و با شاخص LST رابطه معکوس داشته است. همچنین، شاخص LST، برآورد بهتری از رطوبت خاک داشته که نشان دهنده تأثیر قابل توجه این عامل بر مقادیر رطوبت سطحی خاک میباشد. | ||
کلیدواژهها | ||
برآورد؛ خاک؛ رطوبت؛ شاخص؛ شوشتر | ||
عنوان مقاله [English] | ||
Estimation of Soil Surface Moisture in Agricultural Lands Using Satellite Images and Remote Sensing Indicators (Case Study: Shushtar County) | ||
نویسندگان [English] | ||
Mohammad Abiyat1؛ Mostefa Abiyat2؛ Morteza Abiyat2 | ||
1MSc in Natural Resources Engineering- Environment, Islamic Azad University, Science and Research Branch of Tehran (Khuzestan), Ahvaz, Iran | ||
2PhD Student in Geography and Rural Planning, Faculty of Geographical Sciences and Planning, Isfahan University, Isfahan, Iran | ||
چکیده [English] | ||
Estimation of soil moisture is essential for optimal management of water and soil resources. Landsat images with appropriate spatial and temporal resolution are good tools for these studies. The purpose of this study is to estimate and zoning the soil surface moisture in agricultural lands of Shushtar County in Khuzestan province using remote sensing indicators. To do this, first 25 soil samples of agricultural lands were taken from a depth of 0-15 cm and their moisture was measured. Then, normalized vegetation difference index (NDVI), soil reflection adjustment index (SAVI), surface temperature (LST), normalized moisture difference index (NDMI), normalized agricultural differential index (NDTI) and soil moisture index Infrared short band (SMSWIR) were applied to the Landsat 8 Image. In the next step, the values of these indices were transferred to SPSS software for statistical regression and the soil moisture estimation functions were obtained by multivariate linear regression. The results showed; Due to the high coefficient of determination (0.73) and the low root mean square error (1.31) in the simultaneous method (Enter Method), this model was considered suitable for estimating and zoning the surface moisture of agricultural lands in the region. According to the research results, soil surface moisture was directly related to NDVI, SAVI, NDMI, NDTI and SMSWIR indices and inversely related to LST index. Also, LST index has a better estimate of soil moisture, which indicates a significant effect of this factor on the amount of soil surface moisture. | ||
کلیدواژهها [English] | ||
Estimation, Soil, Moisture, Index, Shushtar | ||
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