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

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

doi:10.3390/s19143209

Hamid Reza Mirsoleimani, Mohammad El Hajj + Show 2 more

Open Access

https://doi.org/10.3390/s19143209

Copy DOI

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.

Highlights

Soil surface parameters, especially soil moisture, are the key parameters for numerous agricultural and hydrological applications [1,2]
The 19, aim of this study is to evaluate the potential of the calibrated integral equation model (CIEM) and the modified Dubois (MDB) for soil moisture Sensors 2019, 3 of(M
For soil moisture estimation based on neural networks (NNs), almost 70% of the measurements, namely 100 measurements, were randomly selected

Summary

Introduction

Especially soil moisture, are the key parameters for numerous agricultural and hydrological applications [1,2]. Synthetic aperture radar (SAR) data have been widely used for long-term soil parameters monitoring over large areas [3,4,5,6,7]. In this regard, the sensitivity of SAR signal to soil moisture benefits water management, flood forecasting, drought monitoring, and sustainable agriculture. Soil surface moisture and soil roughness are the two most important soil parameters affecting the backscattering coefficient [12]. It is frequently argued that the effects of soil surface roughness are equal or greater than those of soil moisture content on the backscattering coefficient [13,14,15]

Objectives

Results

Conclusion