Next Article in Journal
Application of ECIS to Assess FCCP-Induced Changes of MSC Micromotion and Wound Healing Migration
Previous Article in Journal
Physical Unclonable Functions in the Internet of Things: State of the Art and Open Challenges
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sensors
Volume 19
Issue 14
10.3390/s19143209
sensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks

by
Hamid Reza Mirsoleimani
1,
Mahmod Reza Sahebi
1,*,
Nicolas Baghdadi
2,* and
Mohammad El Hajj
2
1
Faculty of Geodesy and Geomatics Engineering & Remote Sensing Institute, K. N. Toosi University of Technology, Tehran 19667-15433, Iran
2
IRSTEA, UMR TETIS, University of Montpellier, 500 rue François Breton, 34093 Montpellier cedex 5, France
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(14), 3209; https://doi.org/10.3390/s19143209
Submission received: 7 June 2019 / Revised: 15 July 2019 / Accepted: 18 July 2019 / Published: 21 July 2019
(This article belongs to the Special Issue Spaceborne Radar Remote Sensing of Agricultural Canopies and Soil Moisture)
Browse Figures
Versions Notes

Abstract

The main purpose of this study is to investigate the performance of two radar backscattering models; the calibrated integral equation model (CIEM) and the modified Dubois model (MDB) over an agricultural area in Karaj, Iran. In the first part, the performance of the models is evaluated based on the field measurement and the mentioned backscattering models, CIEM and MDB performed with root mean square error (RMSE) of 0.78 dB and 1.45 dB, respectively. In the second step, based on the neural networks (NNS), soil surface moisture is estimated using the two backscattering models, based on neural networks (NNs), from single polarization Sentinel-1 images over bare soils. The inversion results show the efficiency of the single polarized data for retrieving soil surface moisture, especially for VV polarization.
Keywords: bare soils; soil moisture; neural networks; Sentinel-1; calibrated IEM; Modified Dubois Model; Iran bare soils; soil moisture; neural networks; Sentinel-1; calibrated IEM; Modified Dubois Model; Iran

Share and Cite

MDPI and ACS Style

Mirsoleimani, H.R.; Sahebi, M.R.; Baghdadi, N.; El Hajj, M. Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks. Sensors 2019, 19, 3209. https://doi.org/10.3390/s19143209

AMA Style

Mirsoleimani HR, Sahebi MR, Baghdadi N, El Hajj M. Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks. Sensors. 2019; 19(14):3209. https://doi.org/10.3390/s19143209

Chicago/Turabian Style

Mirsoleimani, Hamid Reza, Mahmod Reza Sahebi, Nicolas Baghdadi, and Mohammad El Hajj. 2019. "Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks" Sensors 19, no. 14: 3209. https://doi.org/10.3390/s19143209

APA Style

Mirsoleimani, H. R., Sahebi, M. R., Baghdadi, N., & El Hajj, M. (2019). Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks. Sensors, 19(14), 3209. https://doi.org/10.3390/s19143209

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop