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Article

Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data

1
CIRAD, CNRS, INRAE, TETIS, University of Montpellier, AgroParisTech, CEDEX 5, 34093 Montpellier, France
2
National Center for Remote Sensing, National Council for Scientific Research (CNRS), Riad al Soloh, Beirut 1107 2260, Lebanon
3
ITK, Cap Alpha, Avenue de l’Europe, 34830 Clapiers, France
4
Hydrology, Agriculture and Land Observation Group (HALO), Water Desalination and Reuse Center, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
5
CESBIO, University of Toulouse, CNES/CNRS/INRAE/IRD/UPS, 31400 Toulouse, France
6
UMR G-EAU, INRAE, 34090 Montpellier, France
*
Author to whom correspondence should be addressed.
Academic Editor: Luca Brocca
Remote Sens. 2021, 13(11), 2102; https://doi.org/10.3390/rs13112102
Received: 6 April 2021 / Revised: 14 May 2021 / Accepted: 25 May 2021 / Published: 27 May 2021
Surface soil moisture (SSM) is a key variable for many environmental studies, including hydrology and agriculture. Synthetic aperture radar (SAR) data in the C-band are widely used nowadays to estimate SSM since the Sentinel-1 provides free-of-charge C-band SAR images at high spatial resolution with high revisit time, whereas the use of L-band is limited due to the low data availability. In this context, the main objective of this paper is to develop an operational approach for SSM estimation that mainly uses data in the C-band (Sentinel-1) with L-bands (ALOS/PALSAR) as additional data to improve SSM estimation accuracy. The approach is based on the use of the artificial neural networks (NNs) technique to firstly derive the soil roughness (Hrms) from the L-band (HH polarization) to then consider the L-band-derived Hrms and C-band SAR data (VV and VH polarizations) in the input vectors of NNs for SSM estimation. Thus, the Hrms estimated from the L-band at a given date is assumed to be constant for a given times series of C-band images. The NNs were trained and validated using synthetic and real databases. The results showed that the use of the L-band-derived Hrms in the input vector of NN in addition to C-band SAR data improved SSM estimation by decreasing the error (bias and RMSE), mainly for SSM values lower than 15 vol.% and regardless of Hrms values. Based on the synthetic database, the NNs that neglect the Hrms underestimate and overestimate the SSM (bias ranges between −8.0 and 4.0 vol.%) for Hrms values lower and higher than 1.5 cm, respectively. For Hrms <1.5 cm and most SSM values higher than 10 vol.%, the use of Hrms as an input in the NNs decreases the underestimation of the SSM (bias ranges from −4.5 to 0 vol.%) and provides a more accurate estimation of the SSM with a decrease in the RMSE by approximately 2 vol.%. Moreover, for Hrms values between 1.5 and 2.0 cm, the overestimation of SSM slightly decreases (bias decreased by around 1.0 vol.%) without a significant improvement of the RMSE. In addition, for Hrms >2.0 cm and SSM between 8 to 22 vol.%, the accuracy on the SSM estimation improved and the overestimation decreased by 2.2 vol.% (from 4.5 to 2.3 vol.%). From the real database, the use of Hrms estimated from the L-band brought a significant improvement of the SSM estimation accuracy. For in situ SSM less than 15 vol.%, the RMSE decreased by 1.5 and 2.2 vol.% and the bias by 1.2 and 2.6 vol.%, for Hrms values lower and higher than 1.5 cm, respectively. View Full-Text
Keywords: soil moisture; surface roughness; SAR; L-band; C-band; ALOS/PALSAR; Sentinel-1; artificial neural networks soil moisture; surface roughness; SAR; L-band; C-band; ALOS/PALSAR; Sentinel-1; artificial neural networks
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MDPI and ACS Style

Hamze, M.; Baghdadi, N.; El Hajj, M.M.; Zribi, M.; Bazzi, H.; Cheviron, B.; Faour, G. Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data. Remote Sens. 2021, 13, 2102. https://doi.org/10.3390/rs13112102

AMA Style

Hamze M, Baghdadi N, El Hajj MM, Zribi M, Bazzi H, Cheviron B, Faour G. Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data. Remote Sensing. 2021; 13(11):2102. https://doi.org/10.3390/rs13112102

Chicago/Turabian Style

Hamze, Mohamad, Nicolas Baghdadi, Marcel M. El Hajj, Mehrez Zribi, Hassan Bazzi, Bruno Cheviron, and Ghaleb Faour. 2021. "Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data" Remote Sensing 13, no. 11: 2102. https://doi.org/10.3390/rs13112102

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