Estimation of soil moisture and surface roughness parameters using L-band SAR measurements

1995 ◽  
Author(s):  
Jiancheng Shi ◽  
Peggy E. O'Neill ◽  
Ann Hsu ◽  
Jakob J. Vanzyl ◽  
Mark S. Seyfried
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Roughness Parameters ◽  
Surface Roughness Parameters ◽  
L Band

scholarly journals Sensitivity of C-Band SAR Polarimetric Variables to the Directionality of Surface Roughness Parameters

2021 ◽  
Vol 13 (11) ◽  
pp. 2210
Author(s):  
Zohreh Alijani ◽  
John Lindsay ◽  
Melanie Chabot ◽  
Tracy Rowlandson ◽  
Aaron Berg
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Laser Scanner ◽  
Careful Attention ◽  
Roughness Parameters ◽  
Retrieval Models ◽  
Surface Roughness Parameters ◽  
The Look ◽  
The Relationship ◽  
Roughness Measurements

Surface roughness is an important factor in many soil moisture retrieval models. Therefore, any mischaracterization of surface roughness parameters (root mean square height, RMSH, and correlation length, ʅ) may result in unreliable predictions and soil moisture estimations. In many environments, but particularly in agricultural settings, surface roughness parameters may show different behaviours with respect to the orientation or azimuth. Consequently, the relationship between SAR polarimetric variables and surface roughness parameters may vary depending on measurement orientation. Generally, roughness obtained for many SAR-based studies is estimated using pin profilers that may, or may not, be collected with careful attention to orientation to the satellite look angle. In this study, we characterized surface roughness parameters in multi-azimuth mode using a terrestrial laser scanner (TLS). We characterized the surface roughness parameters in different orientations and then examined the sensitivity between polarimetric variables and surface roughness parameters; further, we compared these results to roughness profiles obtained using traditional pin profilers. The results showed that the polarimetric variables were more sensitive to the surface roughness parameters at higher incidence angles (θ). Moreover, when surface roughness measurements were conducted at the look angle of RADARSAT-2, more significant correlations were observed between polarimetric variables and surface roughness parameters. Our results also indicated that TLS can represent more reliable results than pin profiler in the measurement of the surface roughness parameters.

scholarly journals Combined Use of Sentinel-1 SAR and Landsat Sensors Products for Residual Soil Moisture Retrieval over Agricultural Fields in the Upper Blue Nile Basin, Ethiopia

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3282
Author(s):  
Getachew Ayehu ◽  
Tsegaye Tadesse ◽  
Berhan Gessesse ◽  
Yibeltal Yigrem ◽  
Assefa M. Melesse
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Prediction Accuracy ◽  
Crop Residue ◽  
Residual Soil ◽  
Roughness Parameters ◽  
Blue Nile ◽  
Surface Roughness Parameters ◽  
Sar Data ◽  
Moisture Estimation

The objective of this paper is to investigate the potential of sentinel-1 SAR sensor products and the contribution of soil roughness parameters to estimate volumetric residual soil moisture (RSM) in the Upper Blue Nile (UBN) basin, Ethiopia. The backscatter contribution of crop residue water content was estimated using Landsat sensor product and the water cloud model (WCM). The surface roughness parameters were estimated from the Oh and Baghdadi models. A feed-forward artificial neural network (ANN) method was tested for its potential to translate SAR backscattering and surface roughness input variables to RSM values. The model was trained for three inversion configurations: (i) SAR backscattering from vertical transmit and vertical receive (SAR VV) polarization only; (ii) using SAR VV and the standard deviation of surface heights ( h r m s ), and (iii) SAR VV, h r m s , and optimal surface correlation length ( l e f f ). Field-measured volumetric RSM data were used to train and validate the method. The results showed that the ANN soil moisture estimation model performed reasonably well for the estimation of RSM using the single input variable of SAR VV data only. The ANN prediction accuracy was slightly improved when SAR VV and the surface roughness parameters ( h r m s and l e f f ) were incorporated into the prediction model. Consequently, the ANN’s prediction accuracy with root mean square error (RMSE) = 0.035 cm3/cm3, mean absolute error (MAE) = 0.026 cm3/cm3, and r = 0.73 was achieved using the third inversion configuration. The result implies the potential of Sentinel-1 SAR data to accurately retrieve RSM content over an agricultural site covered by stubbles. The soil roughness parameters are also potentially an important variable to soil moisture estimation using SAR data although their contribution to the accuracy of RSM prediction is slight in this study. In addition, the result highlights the importance of combining Sentinel-1 SAR and Landsat images based on an ANN approach for improving RSM content estimations over crop residue areas.

Effect of species and grinding disc distance on the surface roughness parameters of medium density fiberboard

2016 ◽  
Vol 75 (3) ◽  
pp. 335-346 ◽  
Author(s):  
Lidia Gurau ◽  
Nadir Ayrilmis ◽  
Jan Thore Benthien ◽  
Martin Ohlmeyer ◽  
Manja Kitek Kuzman ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Medium Density Fiberboard ◽  
Medium Density ◽  
Roughness Parameters ◽  
Surface Roughness Parameters
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