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

Hamid Reza Mirsoleimani; Mahmod Reza Sahebi; Nicolas Baghdadi; Mohammad El Hajj

doi:10.3390/s19143209

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

Sensors (Basel). 2019 Jul 21;19(14):3209. doi: 10.3390/s19143209.

Authors

Hamid Reza Mirsoleimani¹, Mahmod Reza Sahebi², Nicolas Baghdadi³, Mohammad El Hajj⁴

Affiliations

¹ Faculty of Geodesy and Geomatics Engineering & Remote Sensing Institute, K. N. Toosi University of Technology, Tehran 19667-15433, Iran.
² Faculty of Geodesy and Geomatics Engineering & Remote Sensing Institute, K. N. Toosi University of Technology, Tehran 19667-15433, Iran. sahebi@kntu.ac.ir.
³ IRSTEA, UMR TETIS, University of Montpellier, 500 rue François Breton, 34093 Montpellier cedex 5, France. nicolas.baghdadi@teledetection.fr.
⁴ IRSTEA, UMR TETIS, University of Montpellier, 500 rue François Breton, 34093 Montpellier cedex 5, France.

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: Iran; Modified Dubois Model; Sentinel-1; bare soils; calibrated IEM; neural networks; soil moisture.