A Multi-Frequency SDR-Based GBSAR: System Overview and First Results

Amézaga, Adrià; López-Martínez, Carlos; Jové, Roger. 2021. “A Multi-Frequency SDR-Based GBSAR: System Overview and First Results” MDPI Remote Sens. 13, no. 9: 1613

➲ Open access full paper

Summary

This work describes a system-level overview of a multi-frequency GBSAR built around a high performance software defined radio (SDR). The main goal of the instrument is to be employed as a demonstrator and experimental platform for multi-frequency GBSAR campaigns. The system is capable of operating in P, L, C and X-bands, and signal generation and digital signal processing are customizable and reconfigurable through software. An overview of the software and hardware and implementations of the system are presented. The operation of the system is demonstrated with two measuring campaigns showing focused amplitude images at different frequencies. It is shown how the usage of SDR for GBSAR systems is a viable design option.

GBSAR system and |S_{vv}| images at P-, L-, C- & X-bands.

A Model-free Four Component Scattering Power Decomposition for Polarimetric SAR Data

S. Dey, A. Bhattacharya, A. C. Frery, C. López-Martínez and Y. S. Rao, “A Model-free Four Component Scattering Power Decomposition for Polarimetric SAR Data,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Early Access, 2021

➲ Full paper

Summary

Target decomposition methods of polarimetric Synthetic Aperture Radar (PolSAR) data explain scattering information from a target. In this regard, several conventional model-based methods utilize scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branching conditions, leading to several limitations. These techniques assume \textit{ad hoc} scattering models within a radar resolution cell. Therefore, the use of several models makes the computation of scattering powers ambiguous. Some common issues of model-based decompositions are related to the compensation of the orientation angle about the radar line of sight and the negative power components’ occurrence. We propose a model-free four-component scattering power decomposition that alleviates these issues. In the proposed approach, we use the non-conventional 3D Barakat degree of polarization to obtain the scattered electromagnetic wave’s polarization state. The degree of polarization is used to obtain the even-bounce, odd-bounce, and diffused scattering power components. Along with this, a measure of target scattering asymmetry is also proposed, which is then suitably utilized to obtain the helicity power. All the power components are roll-invariant, non-negative and unambiguous. In addition to this, we propose an unsupervised clustering technique that preserves the dominance of the scattering power components for different targets. This clustering technique assists in understanding the importance of diverse scattering mechanisms based on target characteristics. The technique adequately captures the clusters’ variations from one target to another according to their physical and geometrical properties.

Second Multi-Frequency GBSAR Test Campaing, Castell de Subirats, Spain

In June 22nd, 2020, we continued the field test of our new multi-frequency GBSAR system, developed in a joint effort of Balamis and the Remote Sensing Laboratory of the Universitat Politècnica de Catalunya, as the PhD of Adrià Amézaga under the aegis of the Industrial Doctorate Programs of the Generalitat de Catalunya and the Spanish Ministry of Science, Innovation and Universities.

This time, the system was fully operational and we tested its performances for forest monitoring at X-, C-, L-band frequencies and finally P-band. The test area is located right next to the Castell de Subirats (Subirats Castle), in the outskirts of the Barcelona city.

The video below shows the four 3-hour time-series of |S_{vv}| images at X-, C-, L- and P-band frequencies measured this day.  One can observe how signal stability increases as the frequency gets lower. This demonstrates that vegetation is transparent at lower frequencies, mainly L- and P-band, so we are observing the soil and the rocky structures under the vegetation.

Multi-Frequency GBSAR Test Campaing, Castell de Subirats, Spain

At the end of May 2020, we resumed our activities to test the new multi-frequency GBSAR system, developed in a joint effort of Balamis and the Remote Sensing Laboratory of the Universitat Politècnica de Catalunya, as the PhD of Adrià Amézaga under the aegis of the Industrial Doctorate Programs of the Generalitat de Catalunya and the Spanish Ministry of Science, Innovation and Universities.

This time, we tested the system performances for forest monitoring at X-, C- and L-band frequencies. The test area is located right next to the Castell de Subirats (Subirats Castle), in the outskirts of the Barcelona city.

The video below shows the three time-series of |S_{vv}| images at X-, C- and L-band frequencies measured this day. It is interesting to observe the effects of data stability at lower frequencies, specially L-band, and how some rocky structures under the vegetation are only visible at this frequency.

Estimation of Vegetation Structure Parameters From SMAP Radar Intensity Observations

T. Jagdhuber, C. Montzka, C. López-Martínez, M. J. Baur, M. Link, M. Piles, N. N. Das and F. Jonard, “Estimation of Vegetation Structure Parameters From SMAP Radar Intensity Observations,” in IEEE Transactions on Geoscience and Remote Sensing, Early Access, 2020

➲ Full paper

Summary

In this article, we present a multipolarimetric estimation approach for two model-based vegetation structure parameters (shape AP and orientation distribution \Psi of the main canopy elements). The approach is based on a reduced observation set of three incoherent (no phase information) polarimetric backscatter intensities (|S_{hh}|^2, |S_{hv}|^2, and |S_{vv}|^2) combined with a two-parameter (AP and \Psi) discrete scatterer model of vegetation. The objective is to understand whether this confined set of observations contains enough information to estimate the two vegetation structure parameters from the L-band radar signals. In order to disentangle soil and vegetation scattering influences on these signals and ultimately perform a vegetation-only retrieval of vegetation shape AP and orientation distribution \Psi, we use the subpixel spatial heterogeneity expressed by the covariation of co- and cross-polarized backscatter \Gamma_{PP-PQ} of the neighboring cells and assume it is indicative for the amount of a vegetation-only co-to-cross-polarized backscatter ratio \mu_{PP-PQ}. The ratio-based retrieval approach enables a relative (no absolute backscatter) estimation of the vegetation structure parameters which is more robust compared to retrievals with absolute terms. The application of the developed algorithm on global L-band Soil Moisture Active Passive (SMAP) radar data acquired from April to July 2015 indicates the potential and limitations of estimating these two parameters when no fully polarimetric data are available. A focus study on six different regions of interest, spanning land cover from barren land to tropical rainforest, shows a steady increase in orientation distribution toward randomly oriented volumes and a continuous decrease in shape arriving at dipoles for tropical vegetation. A comparison with independent data sets of vegetation height and above-ground biomass confirms this consistent and meaningful retrieval of AP and \Psi. The retrieved shapes and orientation distributions represent the main vegetation elements matching the literature results from model-based decompositions of fully polarimetric L-band data at the SMAP spatial resolution. Based on our findings, AP and \Psi can be directly applied for parameterizing the vegetation scattering component of model-based polarimetric decompositions. This should facilitate decomposition into ground and vegetation scattering components and improve the retrieval of soil parameters (moisture and roughness) under vegetation.