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22 results on '"Mirmazloumi, S. Mohammad"'

7. Ocean Remote Sensing Techniques and Applications: A Review (Part I)

Author

Amani, Meisam, primary, Moghimi, Armin, additional, Mirmazloumi, S. Mohammad, additional, Ranjgar, Babak, additional, Ghorbanian, Arsalan, additional, Ojaghi, Saeid, additional, Ebrahimy, Hamid, additional, Naboureh, Amin, additional, Nazari, Mohsen Eslami, additional, Mahdavi, Sahel, additional, Moghaddam, Sayyed Hamed Alizadeh, additional, Asiyabi, Reza Mohammadi, additional, Ahmadi, Seyed Ali, additional, Mehravar, Soroosh, additional, Mohseni, Farzane, additional, and Jin, Shuanggen, additional

Published
2022
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17. Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Author

Amani, Meisam, primary, Ghorbanian, Arsalan, additional, Ahmadi, Seyed Ali, additional, Kakooei, Mohammad, additional, Moghimi, Armin, additional, Mirmazloumi, S. Mohammad, additional, Moghaddam, Sayyed Hamed Alizadeh, additional, Mahdavi, Sahel, additional, Ghahremanloo, Masoud, additional, Parsian, Saeid, additional, Wu, Qiusheng, additional, and Brisco, Brian, additional

Published
2020
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20. New empirical backscattering models for estimating bare soil surface parameters.

Author

Mirmazloumi, S. Mohammad, Sahebi, Mahmod Reza, and Amani, Meisam

Subjects
*STANDARD deviations, *SYNTHETIC aperture radar, *BACKSCATTERING, *SOILS, *REGRESSION analysis
Abstract

Various models have been proposed to estimate the degree of backscatter in Synthetic Aperture Radar (SAR) images. However, it is still necessary to calibrate these models based on the characteristics of different study areas and to propose new models to achieve the highest possible accuracy in estimating the backscattering coefficient ( σ 0 ) SAR. In this study, three empirical models, including Champion, Sahebi and Zribi/Dechambre, were initially calibrated for two SAR datasets (i.e. The Airborne Synthetic Aperture Radar (AIRSAR) and Canadian Space Agency radar satellite (RADARSAT-1)) acquired over two bare soil study areas with various soil characteristics. The Zribi/Dechambre model was then modified by revising the roughness parameter to obtain higher accuracy in estimating σ 0 over a larger range of incidence angles (θ). A new empirical model was also proposed by combining the four parameters of Soil Moisture (SM), standard deviation of surface height -root mean square- (rms), correlation length (l), and θ. To this end, the most appropriate form of the regression model was investigated and used for each of these parameters to obtain the highest correlation between the in-situ data and σ 0 values. A comparison of the empirical models showed that the modified Zribi/Dechambre had the highest accuracy in predicting σ 0 values with the Root Mean Square Errors (RMSE) of 1.20 dB and 1.59 dB over Oklahoma and Quebec, respectively. Furthermore, coefficients values of the new proposed model remained stable in the two datasets unlike the other investigated models. In this study, the effects of l on the accuracy of the new proposed model were also assessed. It was concluded that l had a considerable impact on the accuracy of the proposed model and including this parameter can improve the accuracy by up to 1 dB. [ABSTRACT FROM AUTHOR]

Published
2021
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