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

Authors

Getachew Ayehu, Ethiopian Space Science & Technology Institute & Bahir Dar UniversityFollow
Tsegaye Tadesse, University of Nebraska-LincolnFollow
Berhan Gessesse, Ethiopian Space Science & Technology InstituteFollow
Yibeltal Yigrem, Bahir Dar UniversityFollow

ORCID IDs

https://orcid.org/0000-0002-4102-1137

Date of this Version

1-10-2019

Citation

2019 by the authors

Comments

Remote Sens. 2019, 11, 125; doi:10.3390/rs11020125 www.mdpi.com/journal/remotesensing

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., a = 0.01, a = 0.05, and a = 0.1). Thus, the AWS based SCA model with a = 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 a 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.