Abstract
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, \({\lambda }_{c}\) = 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. %) inversion under diverse fields of bare and vegetation cover. SAR retrieved soil moisture (vol. %) content over agricultural land was validated with the field survey data collected by time-domain reflectometry (TDR) probes. The performance and comparison of both the models was evaluated in terms of mean absolute error (MAE), root mean square error (RMSE), Pearson’s coefficient of correlation (r) and Nash–Sutcliffe model efficiency coefficient (NSE) (Wang et al., 2016). The Y4CD model result yields MAE of 9.00 vol. %, RMSE of 10.72 vol. %, r of 0.71 and NSE of 0.95 whereas those of M6CSD model are 5.24 vol. %, 6.27 vol. %, 0.91, and 0.98 respectively. These values clearly indicate that the M6CSD model performs better than the Y4CD model for the inversion of volumetric soil moisture using fully polarimetric SAR data. M6CSD model achieved 30.93% of inversion rate for soil moisture retrieval over large agricultural land while that of Y4CD was 24.34%.
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Shankar, H., Singh, D. & Chauhan, P. Model Based Four and Six Component Decompositions for Soil Moisture Retrieval. J Indian Soc Remote Sens 50, 435–450 (2022). https://doi.org/10.1007/s12524-021-01471-x
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DOI: https://doi.org/10.1007/s12524-021-01471-x