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

Abstract

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.

A Convolutional Neural Network Algorithm for Soil Moisture Prediction from Sentinel-1 SAR Images

“…We can now say with high confidence that: (1) VV polarization is more sensitive than VH polarization for soil moisture retrieval; similar results have been found by [19][20][21][22][23]. (2) The combination of VV and VH polarization has resulted in improvement in the prediction of soil moisture from Sentinel-1 images, our result agree with [24][25][26]. (3) The best inputs to the CNN to retrieve soil moisture from Sentinel-1 images are Sigma0_VH and Sigma0_VV, or Gamma0_VH and Gamma0_VV, or Beta0_VH and Beta0_VV.…”

“…VV polarization was more sensitive for soil moisture retrieval from Sentinel-1 data as compared to VH polarization [21][22][23]. Several studies have indicated that the accuracy of the soil moisture estimates improved when using both VV and VH polarization, instead of only VV or VH polarization [24][25][26].…”

Evaluation ofmulti‐sourceprecipitation data in a watershed with complex topography based on distributed hydrological modeling

“…In this study, SCA was used to establish the statistical relationships between precipitation and geographical factors (latitude, longitude, elevation and slope). Based on these relationships, the research region could be divided into different sub‐regions to analyse the regional heterogeneity better (Ayehu, Tadesse, Gessesse, & Yigrem, 2019; Wang, Huang, Zhao, & Guo, 2015). The principle of SCA is to divide samples into a set of clusters with significant differences based on a series of cutting and merging processes according to a given statistical criterion.…”

Spatio‐temporal variability of multi‐layer soil water at a hillslope scale in the critical zone of the Chinese Loess Plateau

“…In recent years, many studies of soil water content (SWC) variability at a multitude of different spatio‐temporal scales have been conducted. For example, studies of only 20 m apart found profound differences at a local scale (Oerter & Bowen, 2019; Romano, 2014); surface SWC showed a greater effect on immediate plant responses at the field scale (Siegfried, Longchamps, & Khosla, 2019); spatial variability and temporal stability of soil water storage increased over depth at a watershed scale (Fu et al, 2018); higher SWC responses were linked to higher elevation at a regional‐scale (Ayehu, Tadesse, Gessesse, & Yigrem, 2019) and SWC trends were even noted in a global scale (Piles, Ballabrera‐Poy, & Munoz‐Sabater, 2019). Due to the variability and complexity of SWC, conventional in‐situ point‐scale measurements are not practicable for regional‐scale SWC assessment (Singh, Panda, & Mohanty, 2019).…”