Investigating the potential for soil moisture and surface roughness monitoring in drylands using ERS SAR data

2001 ◽  
Vol 22 (11) ◽  
pp. 2129-2149 ◽  
Author(s):  
K. J. Tansey ◽  
A. C. Millington
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Sar Data

scholarly journals Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks

2012 ◽  
Vol 16 (6) ◽  
pp. 1607-1621 ◽  
Author(s):  
N. Baghdadi ◽  
R. Cresson ◽  
M. El Hajj ◽  
R. Ludwig ◽  
I. La Jeunesse
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Soil Moisture ◽  
A Priori ◽  
Soil Parameters ◽  
A Priori Information ◽  
Polarimetric Sar ◽  
The Neural Networks ◽  
Sar Data ◽  
Priori Information

Abstract. The purpose of this study was to develop an approach to estimate soil surface parameters from C-band polarimetric SAR data in the case of bare agricultural soils. An inversion technique based on multi-layer perceptron (MLP) neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM) on a wide range of surface roughness and soil moisture, as it is encountered in agricultural contexts for bare soils. The performances of neural networks in retrieving soil moisture and surface roughness were tested for several inversion cases using or not using a-priori knowledge on soil parameters. The inversion approach was then validated using RADARSAT-2 images in polarimetric mode. The introduction of expert knowledge on the soil moisture (dry to wet soils or very wet soils) improves the soil moisture estimates, whereas the precision on the surface roughness estimation remains unchanged. Moreover, the use of polarimetric parameters α1 and anisotropy were used to improve the soil parameters estimates. These parameters provide to neural networks the probable ranges of soil moisture (lower or higher than 0.30 cm3 cm−3) and surface roughness (root mean square surface height lower or higher than 1.0 cm). Soil moisture can be retrieved correctly from C-band SAR data by using the neural networks technique. Soil moisture errors were estimated at about 0.098 cm3 cm−3 without a-priori information on soil parameters and 0.065 cm3 cm−3 (RMSE) applying a-priori information on the soil moisture. The retrieval of surface roughness is possible only for low and medium values (lower than 2 cm). Results show that the precision on the soil roughness estimates was about 0.7 cm. For surface roughness lower than 2 cm, the precision on the soil roughness is better with an RMSE about 0.5 cm. The use of polarimetric parameters improves only slightly the soil parameters estimates.

scholarly journals Soil parameters estimation over bare agriculture areas from C-band polarimetric SAR data using neural networks

2012 ◽  
Vol 9 (3) ◽  
pp. 2897-2933 ◽  
Author(s):  
N. Baghdadi ◽  
R. Cresson ◽  
M. El Hajj ◽  
R. Ludwig ◽  
I. La Jeunesse
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Soil Moisture ◽  
A Priori ◽  
Soil Parameters ◽  
A Priori Information ◽  
Polarimetric Sar ◽  
The Neural Networks ◽  
Sar Data ◽  
Priori Information

Abstract. The purpose of this study was to develop an approach to estimate soil surface parameters from C-band polarimetric SAR data in the case of bare agricultural soils. An inversion technique based on Multi-Layer Perceptron (MLP) neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM) on a wide range of surface roughness and soil moisture, as it is encountered in agricultural contexts for bare soils. The performances of neural networks in retrieving soil moisture and surface roughness were tested for several inversion cases in using or not a priori knowledge on soil parameters. The inversion approach was then validated in using RADARSAT-2 images in polarimetric mode. The introduction of expert knowledge on the soil moisture (dry to wet soils or very wet soils) improves the soil moisture estimates whereas the precision on the surface roughness estimation remains unchanged. Moreover, the use of polarimetric parameters α1 and anisotropy were used to improve the soil parameters estimates. These parameters provide to neural networks the probable ranges of soil moisture (lower or higher than 0.30 cm3 cm−3) and surface roughness (lower or higher than 1.5 cm). Soil moisture can be retrieved correctly from C-band SAR data by using the neural networks technique. Soil moisture errors were estimated at about 0.098 without a priori information on soil parameters and 0.065 cm3 cm−3 (RMSE) applying a priori information on the soil moisture. The retrieval of surface roughness is possible only for low and medium values (lower than 2 cm). Results show that the precision on the soil roughness estimates was about 0.7 cm. For surface roughness lower than 2 cm, the precision on the soil roughness is better with a RMSE about 0.5 cm. The use of polarimetric parameters improves only slightly the soil parameters estimates.

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.

A Semiempirical Modeling of Soil Moisture, Vegetation, and Surface Roughness Impact on CYGNSS Reflectometry Data

2020 ◽  
pp. 1-17
Author(s):  
Simon H. Yueh ◽  
Rashmi Shah ◽  
M. Julian Chaubell ◽  
Akiko Hayashi ◽  
Xiaolan Xu ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Soil Moisture
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