Soil Moisture Content Estimation Based on Sentinel-1 SAR Imagery Using an Artificial Neural Network and Hydrological Components

Remote Sensing

Jeehun Chung¹  Yonggwan Lee¹  Jinuk Kim¹  Seongjoon Kim²  Chunggil Jung³ 

[1] Department of Civil, Environmental and Plant Engineering, Graduate School, Konkuk University, Seoul 05029, Korea;Division of Civil and Environmental Engineering, College of Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea;Forecast and Control Division, Yeongsan River Flood Control Office, 25, Jukbong-daero 22beon-gil, Gwangju 61934, Korea;

关键词: antecedent precipitation index;    artificial neural network;    Sentinel-1;    soil moisture content;    synthetic aperture radar;   

DOI  :  10.3390/rs14030465

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown