برآورد رطوبت سطح خاک با استفاده از دادة آلوس پالسار 2

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری آبخیزداری، گرایش آب، دانشکدة منابع طبیعی، دانشگاه کشاورزی و منابع طبیعی، گرگان

2 استادیار گروه جغرافیا، دانشکدة جغرافیا، دانشگاه علوم انتظامی امین، تهران

3 دانشیار گروه آبخیزداری، دانشکدة منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی، گرگان

4 استادیار، گروه مناطق بیابانی، دانشکدة منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی، گرگان

چکیده

توزیع زمانی و مکانی رطوبت خاک متغیری کلیدی در برنامه‌ریزی هیدرولوژیکی است. در محدودة تحقیق، افزایش رطوبت خاک باعث سرعت شکل‌گیری رواناب می‌شود. هدف این تحقیق بررسی پتانسیل باند L از سنجندة پالسار 2 (PALSAR-2) ماهوارة آلوس (ALOS)، در برآورد رطوبت سطح خاک، به‌منظور مدیریت منابع آب و کاهش مخاطرات ناشی از سیل است. با استفاده از نمونه‌برداری تصادفی خوشه‌ای، نمونه‌ها دریافت شد. هم‌زمان با دریافت دادة SAR رطوبت وزنی، زبری سطح و محتوای آب گیاهی، میانگین ضرایب بازپراکنش راداری و زاویة فرود روی تصویر اندازه‌گیری شد. مراحل پیش‌پردازش، پردازش و پس‌پردازش دادة SAR با استفاده از نرم‌افزار SNAP و شاخص‌های گیاهی و نمناکی از لندست 8 سنسور OLI در محیط ArcGIS 10/5 استخراج شد. برای دریافت زبری سطح، دو دوربین دارای زاویة مایل به‌کار رفت و بیست قطعه عکس، با ده نقطة کنترلی (GCP) برای هر خوشه، گرفته شد. سپس با نرم‌افزار Agisoft PhotoScan ابر نقاط متراکم محاسبه و سپس مش‌بندی به‌منظور تولید DTM انجام گرفت. از زبانة Analysts 3D در ArcGIS پروفیل طولی ناهمواری‌های سطح برای هر خوشه استخراج شد. به‌منظور انتخاب مدل مناسب در منطقه، سه مدل در برآورد رطوبت سطح خاک، شامل مدل‌های Dubois-MLR-WCM، مد نظر قرار گرفت. نتایج سه مدل در برآورد رطوبت سطح خاک در پلاریزة HH به‌ترتیب در مدل Dubois با  و ، مدل MLR با  و  و مدل WCM با  و  به‌دست آمد. نتایج نشان داد مدل Dubois در اراضی بایر تا تنک برای محدودة تحقیق و شرایط مشابه مناسب‌تر است.

کلیدواژه‌ها

عنوان مقاله [English]

Estimation of Soil Surface Moisture Using ALOSPALSAR-2 Data

نویسندگان [English]

  • Sadolla Sangini 1
  • Hadi Fadaei 2
  • Amir Sadoddin 3
  • Vahed berdi Sheikh 3
  • Chogi Bairam Komaki 4
1 Ph.D. Student, Watershed Management, Dep. of Watershed Management, Faculty of Natural Resources, University of Agricultural Sciences and Natural Resources, Gorgan
2 Assistant Prof., Dep. of Geography, Faculty of Geography, University of Amin Police, Tehran
3 Associate Prof., Dep. of Watershed Management, Faculty of Natural Resources, University of Agricultural Sciences and Natural Resources, Gorgan
4 Assistant Prof., Dep. of Desert Management, Faculty of Natural Resources, University of Agricultural Sciences and Natural Resources, Gorgan
چکیده [English]

Temporal and spatial distribution of soil moisture is a key variable for hydrological planning. In the research area, increasing soil moisture accelerates the formation of runoff. The purpose of this study is to investigate the L-band potential of the Pulsar 2 sensor from the ALOS satellite in estimating soil surface moisture in order to manage water resources and reduce flood hazards. Samples were taken using random sampling. Simultaneously with obtaining SAR data, weight moisture, surface roughness and plant water content, average radar backscattering coefficients and indices angle were measured on the image. The pre-processing, processing and post-processing stages of SAR data were calculated using SNAP software and extraction of plant and moisture indices in ArcGIS 5.10 environment. Surface roughness was obtained using two angled cameras with 20 images with 10 control points (GCP) for each cluster, then using Agisoft Photo Scan software, dense cloud meshes and meshing were performed to produce DTM. Longitudinal profiles of surface roughness for each cluster were extracted from the Analysts 3D tab in ArcGIS. In order to select the appropriate model in the region, three models were considered in estimating soil surface moisture including Dubois-MLR-WCM models. The results of three models in estimating soil surface moisture in HH polarization in Dubois model with R2 = 0.82 and RMSE = 0.027, MLR model with R2 = 0.71 and RMSE = 0.03 and WCM model with R2 = 0.67 and RMSE, respectively = 0.033 was estimated. The results showed that Dubois model is more suitable for research areas and similar conditions in lands with sparse to medium cover.

Keywords: Soil surface moisture, ALOS PALSAR 2, Dubois model, Multivariate linear regression model, Water cloud model

کلیدواژه‌ها [English]

  • Soil surface moisture
  • ALOS PALSAR 2
  • Dubois model
  • Multivariate linear regression model
  • Water cloud model

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