Volume 27, Issue 4 (2023)
MJSP 2023, 27(4): 131-148 |
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Rahimzadegan D M, Mohebbi M. Estimation of snow depth using active microwave images. MJSP 2023; 27 (4) :131-148
URL: http://hsmsp.modares.ac.ir/article-21-75169-en.html
URL: http://hsmsp.modares.ac.ir/article-21-75169-en.html
1- Associate Professor of Water Resources Engineering and Management Department. Civil Engineering Faculty K.N. Toosi University of Technology. Iran , rahimzadegan@kntu.ac.ir
2- Master's student in water resources management and civil engineering, Khwaja Nasiruddin Toosi University of Technology, Tehran, Iran
2- Master's student in water resources management and civil engineering, Khwaja Nasiruddin Toosi University of Technology, Tehran, Iran
Abstract: (277 Views)
Snow depth plays a critical role as a key input parameter in various agricultural, hydrological, and climatological models. Nevertheless, the process of estimating snow depth through optical remote sensing tools is subject to uncertainties stemming from constraints within the imaging technique. Consequently, the primary objective of this study is to employ active microwave remote sensing technology for the purpose of snow depth estimation in regions characterized by mountainous terrain. The radar interferometric technique employing active microwave imagery was utilized for the specific objective of examining the microwave signal's interaction with snow accumulation. Utilizing Sentinel 1 satellite images of the Zagros mountains in Iran during the months of February 2017, March 2019, and 2020, relevant data was acquired. Furthermore, field measurements of snow depth were conducted to validate the proposed algorithm. In order to enhance the accuracy of snow depth estimations, the data from both VV and VH channels was integrated by applying a weighting factor determined based on the local radiation angle. The comparison between the outcomes of the suggested approach and the field data revealed a correlation coefficient of 0.86. Furthermore, the calculated values for RMSE and P-Value were 14.37 cm and 0.009, correspondingly. Based on the statistical metrics derived from the validation process of the proposed technique, it demonstrated a satisfactory performance in the estimation of snow depth.
Article Type: Original Research |
Subject:
planning of environmental managment and riks
Received: 2024/05/16 | Accepted: 2024/07/31 | Published: 2023/12/22
Received: 2024/05/16 | Accepted: 2024/07/31 | Published: 2023/12/22
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