برآورد تغییرات مکانی رطوبت خاک با بهره‌گیری از روش جنگل تصادفی و ویژگی‌های محیطی حاصل از تصاویر ماهواره‌ای در حوضه مرغاب خوزستان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم خاک، دانشکده مهندسی و فناوری کشاورزی، دانشگاه تهران

2 گروه علوم و مهندسی خاک، دانشکده مهندسی و فناوری کشاورزی، دانشگاه تهران

3 گروه مهندسی آب، دانشکده کشاورزی، دانشگاه گیلان

چکیده

در مدیریت اراضی، تهیه نقشه یکپارچه تغییرات ­رطوبت خاک با وضوح مکانی بالا و کیفیت مناسب از اهمیت بالایی برخوردار است. با توجه به کمبود ایستگاه‌های هواشناسی و هیدرومتری در حوزه‌های آبخیز، به‌ویژه در مناطق کوهستانی، مطالعات میدانی بررسی تغییرات رطوبت خاک فرآیندی زمان‌بر، پرهزینه و با خطا است. جهت دستیابی به مدلی مناسب برای پیش­بینی مکانی رطوبت خاک در فصل کم بارش در حوضه مرغاب استان خوزستان با مساحت 683 کیلومترمربع، نمونه­برداری میدانی به تعداد 174 نقطه در چهار عمق استاندارد با پروژه جهانی نقشه‌برداری رقومی خاک (5-0، 15-5، 30-15 و 60-30 سانتی‌متری) صورت گرفت. نقشه­های تغییرات مکانی رطوبت خاک با استفاده از اجرای مدل یادگیری ماشین جنگل تصادفی (RF) و دو مجموعه داده­ی فضاپایه شامل ویژگی‌های بیوفیزیکی سطح حاصل از تصاویر ماهواره لندست-8 و سنتینل-2 و ویژگی‌های توپوگرافی مستخرج از مدل رقومی ارتفاع تولید گردید. مناسب­ترین ویژگی­های کمکی پیش‌بینی کننده رطوبت خاک با روش حذف ویژگی برگشتی انتخاب گردیدند. نتایج میانگین تغییرات رطوبت خاک از لایه اول تا لایه چهارم به­ترتیب 2/2، 24/3، 41/3 و 6/4 درصد مشاهده گردید. در عمق سطحی (5-0 سانتیمتر)، ویژگی­های بیوفیزیک ارتباط بیش‌تری با تغییرات مکانی رطوبت خاک از خود نشان دادند و در اعماق پایین­تر، ویژگی­های توپوگرافی اهمیت بالاتری را نشان دادند. بررسی کارایی مدل RF در ارتباط با نوع تصویر مورد استفاده برای تولید ویژگی­های بیوفیزیکی بیانگر آن است که بر مبنای ضریب تطابق همبستگی مدل، استفاده از تصاویر سنتینل-2 در تلفیق با فاکتورهای توپوگرافی در عمق­های استاندارد بین 28/1 تا 66/3 درصد از دقّت بالاتری نسبت به تصاویر لندست-8 برخوردار است. به‌طورکلی الگوریتم جنگل تصادفی به همراه ویژگی­های بیوفیزیکی مستخرج از سنتینل دو و داده­های توپوگرافی در سطح حوضه آبخیز قادر است نقشه‌های رطوبت خاک را با دقّت بالایی فراهم نماید.

کلیدواژه‌ها


عنوان مقاله [English]

Prediction of Spatial Variations of Soil Moisture Using Random Forest Method and Environmental Features derived from Satellite Images in Marghab Basin of Khuzestan

نویسندگان [English]

  • Padideh Javadi 1
  • Hossein Asadi 2
  • Majid Vazifehdoust 3
1 Soil Science Department, Faculty of Agricultural Engineering and Technology, University of Tehran
2 Soil Science Department, Faculty of Agricultural Engineering and Technology, University of Tehran
3 Assistant professor, Department of Water Engineering, Faculty of Agricultural Science, University of Guilan,
چکیده [English]

Preparation of soil moisture map with high spatial resolution and appropriate quality is important in land management. Due to the lack of meteorological stations in watersheds, especially in mountainous areas, field measurement to study changes in soil moisture is time-consuming, costly and error-prone. To achieve a suitable model for spatial prediction of soil moisture in low rainfall season in Marghab Basin of Khuzestan province, 683 km2 area, field sampling was performed in 174 points at four standard depths (0-5, 5-15, 15-30, and 30-60 cm) correspond to the global digital soil mapping project. The spatial distribution of soil moisture was mapped by a machine learning model using two sets of remote sensing data, including surface biophysical features derived from Landsat-8 and Sentinel-2 satellite images, and topographic features derived from the digital elevation model. The most suitable auxiliary variables for predicting soil moisture were selected via the Recursive Feature Elimination (RFE) method. The results of the trend of mean changes in soil moisture from the first to the fourth layer were observed to be 2.2, 3.24, 3.41, and 4.6%, respectively. At the surface depths (0-5 cm), biophysical covariates had more impact on spatial variations of soil moisture, and at the lower depths (5-15, 15-30, and 30-60 cm), topographic attributes showed higher importance. The evaluation of RF model in relation to the type of image used for the production of biophysical features showed that based on the concordance correlation coefficient (CCC), the model performance increased between 1.28 to 3.66 in standard soil depths when using Sentinel-2 images compared to Landsat 8. Generally, the RF model and biophysical features were extracted from the Sentinel-2 satellite along with topographic attributes at the watershed scale are able to provide soil moisture prediction maps with acceptable accuracy.

کلیدواژه‌ها [English]

  • Remote Sensing Indicators
  • topographic factors
  • Random forest model
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