Model Based Four and Six Component Decompositions for Soil Moisture Retrieval

Soil moisture in bare and vegetation covered soil and its spatio-temporal variation is of great importance for various applications in the field of environment, hydrology, and agriculture. In this study, the fully polarimetric capabilities of longer wavelength (L-band, λ_c = 23 cm) synthetic aperture radar (SAR) sensor (PALSAR-2) were combined with model based polarimetric scattering power decomposition techniques to separate the different individual scattering contributions for soil moisture retrieval. Model based six-component scattering power decomposition (M6CSD) developed by (Singh and Yamaguchi, 2018 ) and Yamaguchi four-component decomposition (Y4CD) (Yamaguchi et al., 2011 ) algorithms with rotation of coherency matrix were implemented over the agricultural land for the separation of different scattering powers and calculation of surface dihedral scattering mechanism ratios. Extended Bragg (X-Bragg) and extended Fresnel (X-Fresnel) models were used for inverting ground scattering components into soil dielectric constant (SDC). Volumetric soil moisture was modelled using a widely used transformation model (Topp et al., 1980 ). The retrieved results shows an optimistic trend in terms of soil moisture (vol.