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.
Highlights
Soil moisture is a critical component of agricultural development because its availability and distribution substantially determine the growth and productivity of crops
Volumetric soil moisture from both automatic weather stations (AWS) observations and FLDAS Noah model were used as a dependent variable, while backscatter values of both VV and VH polarizations from Sentinel-1 Synthetic Aperture Radar (SAR), vegetation information based on normalized difference vegetation index (NDVI) analysis, and elevation information derived from digital elevation model (DEM) data were considered as independent variables to calibrate and validate the model
Two different stepwise cluster analysis (SCA) cluster trees were generated to show the relationship between remote sensing variables and volumetric residual soil moisture
Summary
Soil moisture is a critical component of agricultural development because its availability and distribution substantially determine the growth and productivity of crops. Ethiopia’s crop production and productivity are low and dominated by smallholder farmers [2]. Most of these farmers are unable to sustain their livelihoods by a single harvest during the main rainy season [3,4]. Across the UBN basin, following the harvest of main season cropping, certain carry-over moisture, called residual soil moisture, is left in the soil, after the periods of heavy rainfall, which could be used for additional short or medium cycle cropping to increase food and feed production. Practicing additional cropping depends on the extents of residual moisture available in the soil, both at spatial and temporal scale. Measurements of soil moisture using the conventional in-situ methods and hydrological modeling remain challenging due to their specific location point estimates [7] and the difficulties to determine the input parameters of the hydrological model [8], respectively
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