Ku-band SAR data for bare soil moisture retrieval over agricultural fields

2002 ◽  
Author(s):  
E.E. Sano ◽  
M.S. Moran ◽  
A.R. Huete ◽  
T. Miura
Keyword(s):  
Soil Moisture ◽  
Bare Soil ◽  
Agricultural Fields ◽  
Sar Data ◽  
Ku Band

Soil Moisture Retrieval in Agricultural Fields Using Adaptive Model-Based Polarimetric Decomposition of SAR Data

2016 ◽  
Vol 54 (8) ◽  
pp. 4445-4460 ◽  
Author(s):  
Lian He ◽  
Rocco Panciera ◽  
Mihai A. Tanase ◽  
Jeffrey P. Walker ◽  
Qiming Qin
Keyword(s):  
Soil Moisture ◽  
Agricultural Fields ◽  
Adaptive Model ◽  
Model Based ◽  
Sar Data

An Extension of the Alpha Approximation Method for Soil Moisture Estimation Using Time-Series SAR Data Over Bare Soil Surfaces

2017 ◽  
Vol 14 (8) ◽  
pp. 1328-1332 ◽  
Author(s):  
Lian He ◽  
Qiming Qin ◽  
Rocco Panciera ◽  
Mihai Tanase ◽  
Jeffrey P. Walker ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Approximation Method ◽  
Bare Soil ◽  
Sar Data ◽  
Moisture Estimation

Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data

2011 ◽  
Vol 115 (1) ◽  
pp. 33-43 ◽  
Author(s):  
Imen Gherboudj ◽  
Ramata Magagi ◽  
Aaron A. Berg ◽  
Brenda Toth
Keyword(s):  
Soil Moisture ◽  
Agricultural Fields ◽  
Sar Data

scholarly journals Combining L and X band SAR data for estimating biomass and soil moisture of agricultural fields

2012 ◽  
Vol 45 (1) ◽  
pp. 99-109 ◽  
Author(s):  
Simonetta Paloscia ◽  
Simone Pettinato ◽  
Emanuele Santi
Keyword(s):  
Soil Moisture ◽  
Agricultural Fields ◽  
X Band ◽  
Sar Data

Orientation Angle Calibration for Bare Soil Moisture Estimation Using Fully Polarimetric SAR Data

2011 ◽  
Vol 49 (12) ◽  
pp. 4987-4996 ◽  
Author(s):  
Xinyi Shen ◽  
Yang Hong ◽  
Qiming Qin ◽  
Weilin Yuan ◽  
Sheng Chen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Orientation Angle ◽  
Bare Soil ◽  
Polarimetric Sar ◽  
Sar Data ◽  
Moisture Estimation

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.

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