Polarimetric Two-Scale Two-Component Model for the Retrieval of Soil Moisture Under Moderate Vegetation via L-Band SAR Data

2016 ◽  
Vol 54 (4) ◽  
pp. 2470-2491 ◽  
Author(s):  
Gerardo Di Martino ◽  
Antonio Iodice ◽  
Antonio Natale ◽  
Daniele Riccio
Keyword(s):  
Soil Moisture ◽  
Component Model ◽  
Two Component ◽  
L Band ◽  
Sar Data

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

2021 ◽  
Vol 13 (11) ◽  
pp. 2102
Author(s):  
Mohamad Hamze ◽  
Nicolas Baghdadi ◽  
Marcel M. El Hajj ◽  
Mehrez Zribi ◽  
Hassan Bazzi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Bare Soil ◽  
Data Availability ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Alos Palsar ◽  
Times Series ◽  
L Band ◽  
Sar Data ◽  
Error Bias

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.

scholarly journals Soil moisture retrieval through a merging of multi-temporal L-band SAR data and hydrologic modelling

2009 ◽  
Vol 13 (3) ◽  
pp. 343-356 ◽  
Author(s):  
F. Mattia ◽  
G. Satalino ◽  
V. R. N. Pauwels ◽  
A. Loew
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Content ◽  
A Priori ◽  
Retrieval Algorithm ◽  
A Priori Information ◽  
Hydrologic Modelling ◽  
Multi Temporal ◽  
L Band ◽  
Sar Data ◽  
Priori Information

Abstract. The objective of the study is to investigate the potential of retrieving superficial soil moisture content (mv) from multi-temporal L-band synthetic aperture radar (SAR) data and hydrologic modelling. The study focuses on assessing the performances of an L-band SAR retrieval algorithm intended for agricultural areas and for watershed spatial scales (e.g. from 100 to 10 000 km2). The algorithm transforms temporal series of L-band SAR data into soil moisture contents by using a constrained minimization technique integrating a priori information on soil parameters. The rationale of the approach consists of exploiting soil moisture predictions, obtained at coarse spatial resolution (e.g. 15–30 km2) by point scale hydrologic models (or by simplified estimators), as a priori information for the SAR retrieval algorithm that provides soil moisture maps at high spatial resolution (e.g. 0.01 km2). In the present form, the retrieval algorithm applies to cereal fields and has been assessed on simulated and experimental data. The latter were acquired by the airborne E-SAR system during the AgriSAR campaign carried out over the Demmin site (Northern Germany) in 2006. Results indicate that the retrieval algorithm always improves the a priori information on soil moisture content though the improvement may be marginal when the accuracy of prior mv estimates is better than 5%.

Soil moisture limitations on monitoring boreal forest regrowth using spaceborne L-band SAR data

2011 ◽  
Vol 115 (1) ◽  
pp. 227-232 ◽  
Author(s):  
Eric S. Kasischke ◽  
Mihai A. Tanase ◽  
Laura L. Bourgeau-Chavez ◽  
Matthew Borr
Keyword(s):  
Soil Moisture ◽  
Boreal Forest ◽  
Forest Regrowth ◽  
L Band ◽  
Sar Data

Potential of soil moisture retrieval for tropical peatlands in Indonesia using ALOS-2 L-band full-polarimetric SAR data

2019 ◽  
Vol 40 (15) ◽  
pp. 5938-5956 ◽  
Author(s):  
Y. Izumi ◽  
J. Widodo ◽  
H. Kausarian ◽  
S. Demirci ◽  
A. Takahashi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Polarimetric Sar ◽  
Tropical Peatlands ◽  
L Band ◽  
Sar Data
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