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
Article Menu
Issue 14 (July-2) cover image

Export Article

Open AccessArticle

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

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
Received: 7 June 2019 / Revised: 15 July 2019 / Accepted: 18 July 2019 / Published: 21 July 2019
  |  
PDF [1560 KB, uploaded 21 July 2019]
  |  

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. View Full-Text
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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top