برآورد رطوبت سطحی خاک مبتنی بر مدل ذوزنقه‌‌ای نوری-حرارتی با استفاده از داده‌های لندست-8

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه آبیاری و زهکشی، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران.

2 گروه سنجش از دور محیطی و ژئوماتیک، مرکز آب، زمین و محیط زیست، INRS-کبک، کانادا.

10.22059/jwim.2024.376802.1163

چکیده

رطوبت خاک یک عامل حیاتی در تعامل بین زمین و جو است که نقش مهمی در ارزیابی شرایط خشک‌سالی در مناطق کشاورزی دارد و می‌تواند تأثیر قابل‌توجهی بر منابع آب سطحی و تولیدات کشاورزی داشته باشد. این مطالعه با هدف ارزیابی مدل ذوزنقه‌ای نوری-حرارتی (TOTRAM) در برآورد رطوبت سطحی خاک در مقیاس مزرعه با استفاده از تصاویر ماهواره لندست-8 در اراضی کشت و صنعت نیشکر حکیم فارابی خوزستان، ایران انجام شده است. در این راستا از 16 تصویر ماهواره لندست-8 در طول دوره رشد گیاه نیشکر در سال زراعی 1399-1398 استفاده گردید و هم­زمان رطوبت سطحی خاک در 27 نقطه کنترل زمینی در عمق 10-0 سانتی­متر اندازه­گیری شد. هم‌چنین به­منظور بررسی پتانسیل شاخص­های مختلف پوشش گیاهی در مدل TOTRAM از NDVI، SAVI و kNDVI در مدل­سازی رطوبت خاک استفاده گردید. توزیع پیکسل­ها در فضاهای مختلف LST-VI نشان داد که از تاریخ 20 آبان­ماه 1398 تا هفتم آبان­ماه 1399، تغییرات قابل‌توجهی در دمای سطح زمین رخ داده است. این تغییرات دما، باعث تغییرات زیاد توزیع پیکسل­ها و معادلات لبه مرطوب و خشک در طول یک سال شد. هم‌چنین نتایج نشان از همبستگی بهتر رطوبت خاک با TOTRAM-SAVI (56/0) در مقایسه با TOTRAM-kNDVI (46/0) داشت. علاوه ­بر این، بررسی نقشه­های رطوبت خاک حاصل از مدل TOTRAM نشان داد که با افزایش رشد گیاه، شاهد افزایش رطوبت خاک و کاهش توزیع ناهمگونی رطوبت خاک در اراضی نیشکر هستیم. به‌طورکلی مدل TOTRAM باوجود نیاز به واسنجی محلی قادر به برآورد مقدار رطوبت خاک در پهنه­های وسیع جغرافیایی با دقت قابل­قبول است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Estimation of Surface Soil Moisture Using the Thermal-Optical TRApezoid Model with Landsat-8 Data

نویسندگان [English]

  • Atefeh Nouraki 1
  • Mona Golabi 1
  • Mohammad Albaji 1
  • Abd Ali Naseri 1
  • Saeid Homayouni 2
1 Department of Irrigation and Drainage, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
2 Centre Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), 490 Couronne St, Quebec, QC G1K 9A9, Canada.
چکیده [English]

Soil moisture is a critical variable for land-atmosphere interactions. It measures drought conditions in agricultural areas and significantly impacts surface water and agricultural production. This study aims to evaluate the Thermal-Optical TRApezoid Model (TOTRAM) in estimating surface soil moisture at a farm scale using Landsat-8 imagery in the Hakim Farabi sugarcane agro-industrial company lands in Khuzestan, Iran. For this purpose, 16 Landsat-8 images were used during the sugarcane growing season in the agricultural year 2019-2020, and simultaneously, surface soil moisture was measured at 27 ground control points at a depth of 0-10 cm. Additionally, to investigate the potential of various vegetation indices in the TOTRAM model, NDVI, SAVI, and kNDVI were used in soil moisture modeling. Subsequently, the wet and dry edges were determined based on the distribution of pixels in the different LST-NDVI, LST-SAVI, and LST-kNDVI spaces. The distribution of pixels in various LST-VI spaces showed significant changes in land surface temperature from November 11, 2019, to October 28, 2020. These temperature changes led to significant variations in the distribution of pixels and the equations of the wet and dry edges over the studied period. The results also indicated a better correlation of soil moisture with TOTRAM-SAVI (0.56) compared to TOTRAM-kNDVI (0.46). Moreover, examining the soil moisture maps derived from the TOTRAM model showed that with increased plant growth, soil moisture increased, and soil moisture distribution heterogeneity decreased in the sugarcane fields. Overall, despite the need for local calibration, the TOTRAM model can estimate soil moisture with acceptable accuracy over large geographical areas.

کلیدواژه‌ها [English]

  • Optical and thermal remote sensing
  • Soil moisture
  • Sugarcane
  • Vegetation index
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