Application of Sentinel-1B Polarimetric Observations to Soil Moisture Retrieval Using Neural Networks: Case Study for Bare Siberian Chernozem Soil

• Main Authors: Konstantin Muzalevskiy, Anatoly Zeyliger
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Remote Sensing
• Subjects: microwave remote sensing; Sentinel-1; bare soil; soil moisture; soil permittivity
• Online Access: https://www.mdpi.com/2072-4292/13/17/3480

Sentinel-1 is currently the only synthetic-aperture radar, which radar measurements of the earth’s surface to be carried out, regardless of weather conditions, with high resolution up to 5–40 m and high periodicity from several to 12 days. Sentinel-1 creates a technological platform for the development of new globally remote sensing algorithms of soil moisture, not only for hydrological and climatic model applications, but also on a single field scale for individual farms in precision farming systems used. In this paper, the potential of soil moisture remote sensing using polarimetric Sentinel-1B backscattering observations was studied. As a test site, the fallow agricultural field with bare soil near the Minino village (56.0865°N, 92.6772°E), Krasnoyarsk region, the Russian Federation, was chosen. The relationship between the cross-polarized ratio, reflectivity, and the soil surface roughness established Oh used as a basis for developing the algorithm of soil moisture retrieval with neural networks (NNs) computational model. Two NNs is used as a universal regression technique to establish the relationship between scattering anisotropy, entropy and backscattering coefficients measured by the Sentinel-1B on the one hand and reflectivity on the other. Finally, the soil moisture was found from the soil reflectivity in solving the inverse problem using the Mironov dielectric model. During the field campaign from 21 May to 25 August 2020, it was shown that the proposed approach allows us to predict soil moisture values in the layer thickness of 0.00–0.05 m with the root-mean-square error and determination coefficient not worse than 3% and 0.726, respectively. The validity of the proposed approach needs additional verification on a wider dataset using soils of different textures, a wide range of variations in soil surface roughness, and moisture.