Empirical fitting of forward backscattering models for multitemporal retrieval of soil moisture from radar data at L-band

Abstract

Abstract. A multitemporal algorithm, originally conceived for the C-band radar aboard the Sentinel-1 satellite, has been updated to retrieve soil moisture from L-band radar data, such as those provided by the National Aeronautics and Space Administration Soil Moisture Active/ Passive (SMAP) mission. This type of algorithm may deliver more accurate soil moisture maps that mitigate the effect of roughness and vegetation changes. Within the multitemporal inversion scheme based on the Bayesian maximum a posteriori probability (MAP) criterion, a dense time series of radar measurements is integrated to invert a forward backscattering model. The model calibration and validation tasks have been accomplished using the data collected during the SMAP validation experiment 12 spanning several soil conditions (pasture, wheat, corn, and soybean). The data have been used to update the forward model for bare soil scattering at L-band and to tune a simple vegetation scattering model considering two different classes of vegetation: those producing mainly single scattering effects and those characterized by a significant multiple scattering involving terrain surface and vegetation elements interaction. The algorithm retrievals showed a root mean square difference (RMSD) around 5% over bare soil, soybean, and cornfields. As for wheat, a bias was observed; when removed, the RMSD went down from 7.7% to 5%.

Estimation of field-scale soil moisture content and its uncertainties using Sentinel-1 satellite imagery

“…These effects are enhanced by Sentinel-1's sub-optimal specifications for SMC retrieval, notably the C-band (wavelength 5.6 cm) observations at relatively large 7. Soil moisture content retrieval over meadows incidence angles and in VV polarization (Fascetti et al, 2017;Pulvirenti et al, 2018). Based on the theory it is, thus, expected that correcting Sentinel-1 σ 0 observations for vegetation effects contributes to more accurate SMC retrievals.…”