Speaker
Description
Accurate soil moisture determination over a region enhances our understanding of the hydrological processes, climate interaction and ecosystem dynamics. Timely soil moisture information can support sustainable agriculture, land and water management, urban planning and contributes to disaster preparedness such as floods and landslides. Synthetic Aperture Radar (SAR) data, particularly in different bands and frequencies, have proven highly effective in determining and classifying soil moisture in various geological and geographical scenarios. While single and dual-polarimetric SAR data have been widely used for assessing and monitoring soil moisture, dual polarimetric analysis faces challenges in accurately accounting for vegetation and soil roughness-induced depolarisation. Additionally, the lack of proper approaches for fully polarimetric data utilisation further limits our capabilities in this aspect.
This research proposes an extensive approach to leverage dual-frequency SAR Quad-Pol datasets for assessing sub-surface moisture content. We present a case study in the San Francisco area to generate a fine-resolution moisture map. Moreover, we statistically compare the moisture content derived from L-Band (λ = 23 cm) and C-Band (λ = 6 cm) datasets. The current analysis utilises polarimetric parameters and incoherent decomposition through a model-based technique using PolSAR-Pro Software.
Our findings reveal that soil moisture estimation in ALOS-2 (L-Band Data) is nearly double that of Radarsat-2 and Geofen-3 (C-Band Datasets) due to the greater penetration depth of L-Band. The proposed approach and comparative results hold the potential to yield valuable insights for determining precise sub-surface soil moisture assessment.
Summary
I am carrying out a National PhD Course in Earth Observation (Cycle 38°) at The Department of Earth Sciences of Sapienza University of Rome under the supervision of Prof. Paolo Mazzanti and Prof. Francesca Bozzano.
