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

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.

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

Remote Sensing ◽

10.3390/rs13112210 ◽

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.

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Estimation of soil moisture and surface roughness parameters from backscattering coefficient

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/36.263781 ◽

1993 ◽

Vol 31 (5) ◽

pp. 1094-1099 ◽

Cited By ~ 27

Author(s):

K.S. Rao ◽

S. Raju ◽

J.R. Wang

Keyword(s):

Surface Roughness ◽

Soil Moisture ◽

Roughness Parameters ◽

Backscattering Coefficient ◽

Surface Roughness Parameters

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Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

Remote Sensing ◽

10.3390/rs11020125 ◽

2019 ◽

Vol 11 (2) ◽

pp. 125 ◽

Cited By ~ 5

Author(s):

Getachew Ayehu ◽

Tsegaye Tadesse ◽

Berhan Gessesse ◽

Yibeltal Yigrem

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Prediction Model ◽

Residual Soil ◽

Nile Basin ◽

Blue Nile ◽

Volumetric Soil Moisture ◽

Blue Nile Basin ◽

Sar Data ◽

Upper Blue Nile Basin

In this study, a residual soil moisture prediction model was developed using the stepwise cluster analysis (SCA) and model prediction approach in the Upper Blue Nile basin. The SCA has the advantage of capturing the nonlinear relationships between remote sensing variables and volumetric soil moisture. The principle of SCA is to generate a set of prediction cluster trees based on a series of cutting and merging process according to a given statistical criterion. The proposed model incorporates the combinations of dual-polarized Sentinel-1 SAR data, normalized difference vegetation index (NDVI), and digital elevation model as input parameters. In this regard, two separate stepwise cluster models were developed using volumetric soil moisture obtained from automatic weather stations (AWS) and Noah model simulation as response variables. The performance of the SCA models have been verified for different significance levels (i.e., α = 0.01 , α = 0.05 , and α = 0.1 ). Thus, the AWS based SCA model with α = 0.05 was found to be an optimal model for predicting volumetric residual soil moisture, with correlation coefficient (r) values of 0. 95 and 0.87 and root mean square error (RMSE) of 0.032 and 0.097 m3/m3 during the training and testing periods, respectively. While in the case of the Noah SCA model an optimal prediction performance was observed when α value was set to 0.01, with r being 0.93 and 0.87 and RMSE of 0.043 and 0.058 m3/m3 using the training and testing datasets, respectively. In addition, our result indicated that the combined use of Sentinel-SAR data and ancillary remote sensing products such as NDVI could allow for better soil moisture prediction. Compared to the support vector regression (SVR) method, SCA shows better fitting and prediction accuracy of soil moisture. Generally, this study asserts that the SCA can be used as an alternative method for remote sensing based soil moisture predictions.

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