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

Abstract

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.

Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem

“…At this point, it should be briefly mentioned that both the measuring instrument (Stevens HydraProbe) and the timing of the field observations in tune with the overpasses of S1 are directly comparable to several recently published studies (Ayehu et al, 2020;Datta et al, 2020;Han et al, 2020;Ma et al, 2020), and thus are regarded as reliable from a technical point of view.…”

“…Spaceborne remote sensing (RS) provides spatially explicit information as satellites sense the same ground trace in regular time intervals, allowing for continuous monitoring (Babaeian et al, 2019). Estimates of θ are retrieved from different sensors measuring optical and thermal spectra (e.g., Rahimzadeh-Bajgiran et al, 2013;Zhang and Zhou, 2016), by passive and active microwave sensors (e.g., Schmugge and Jackson, 1997;Das and Paul, 2015), or the synergistic use of different sensor types such as using radar and optical data from Sentinel-2, Landsat, and MODIS (e.g., Attarzadeh et al, 2018;Ayehu et al, 2020;Foucras et al, 2020;Han et al, 2020;Ma et al, 2020). Synthetic Aperture Radars (SAR) are among the most effective and flexible active microwave sensor systems (e.g., Wang and Qu, 2009;Santi et al, 2016) due to their ability to penetrate the near-surface soil layer up to a depth of 5 cm (i.e., for C-band), which in turn enables to observe θ by directly relating the microwave scattering and emission to the water content of the focused object (e.g., Paloscia et al, 2013;Santi et al, 2016;Mohanty et al, 2017;Babaeian et al, 2019).…”

“…High spatiotemporal SAR-based retrieval of θ predominantly focusses on bare land surfaces (e.g., Datta et al, 2020), sparsely vegetated areas such as grassland and meadows (e.g., Xu et al, 2020), or agricultural farmland when the vegetation cover is low such as covered by crop residues (e.g., Ayehu et al, 2020). This is mainly due to the unique challenge resulting from the SAR backscatter's high sensitivity to surface characteristics (e.g., roughness) and vegetation properties (e.g., vegetation canopy architecture).…”

Application of Sentinel-1B Polarimetric Observations to Soil Moisture Retrieval Using Neural Networks: Case Study for Bare Siberian Chernozem Soil

“…Compared to IEM, Advanced IEM, and Dubois models, the Oh model [13] shows the best fitting between its predictions and representative experimental radar dataset over bare soils [1]. In contrast to existing physical-based approaches [6,7], in this paper, the well-known linear relationship between root-mean-square (RMS) heights of soil surface roughness and scattering anisotropy [14][15][16][17] was used to predict soil reflectivity based on NN and Oh models [13]. As a result, only satellite data and in situ soil moisture values on the key test site were needed to calibrate the NN, but the ground measurements of soil surface roughness could be excluded.…”

“…Such calibrated models are widely used for training artificial neural networks (NNs) [6][7][8]. The trained NN on physical-based scattering models [6][7][8] is more adaptive compared to simply training the NN with Sentinel-1 backscattering and ancillary soil moisture values [9][10][11][12].…”