Abstract
This study estimates soil moisture content (SMC) using Sentinel-1A/B C-band synthetic aperture radar (SAR) images and an artificial neural network (ANN) over a 40 × 50-km2 area located in the Geum River basin in South Korea. The hydrological components characterized by the antecedent precipitation index (API) and dry days were used as input data as well as SAR (cross-polarization (VH) and copolarization (VV) backscattering coefficients and local incidence angle), topographic (elevation and slope), and soil (percentage of clay and sand)-related data in the ANN simulations. A simple logarithmic transformation was useful in establishing the linear relationship between the observed SMC and the API. In the dry period without rainfall, API did not decrease below 0, thus the Dry days were applied to express the decreasing SMC. The optimal ANN architecture was constructed in terms of the number of hidden layers, hidden neurons, and activation function. The comparison of the estimated SMC with the observed SMC showed that the Pearson’s correlation coefficient (R) and the root mean square error (RMSE) were 0.85 and 4.59%, respectively.
Highlights
Received: 13 December 2021Soil moisture content (SMC) is an important hydrological factor that determines runoff and infiltration in the water cycle following rainfall and affects the global energy balance by influencing the distribution ratio of sensible and latent heat [1,2,3]
This study aims to evaluate the coupling of synthetic aperture radar (SAR) and hydrological components and their applicability in soil moisture content (SMC) estimation using artificial neural network (ANN)
SMC was estimated via an ANN by using the hydrological components represented by the antecedent precipitation index (API) and dry days based on Sentinel-1 C-band SAR images
Summary
Received: 13 December 2021Soil moisture content (SMC) is an important hydrological factor that determines runoff and infiltration in the water cycle following rainfall and affects the global energy balance by influencing the distribution ratio of sensible and latent heat [1,2,3]. SMC was traditionally measured using observation equipment such as time-domain reflectometry (TDR), which allows a small quantity of point data to be obtained. This method has limitations in representing SMC for a wide area with heterogeneous characteristics. The main differences between these two methods are the electromagnetic energy source, the wavelength region of the electromagnetic spectrum used, the response measured by the sensor and so on [8] The former estimates SMC by analyzing correlations between SMC and various outputs from optical satellites, such as surface temperature and vegetation-related indices, and uses various statistical, empirical, or machine learning techniques [9,10,11].
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