## Dual-Polarimetric Descriptors From Sentinel-1 GRD SAR Data for Crop Growth Assessment

Narayanarao Bhogapurapu, Subhadip Dey, Avik Bhattacharya, Dipankar Mandal, Juan M. Lopez-Sanchez, Heather McNairn, Carlos López-Martínez, Y.S. Rao, “Dual-polarimetric descriptors from Sentinel-1 GRD SAR data for crop growth assessment,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 178, p. 20-35, Aug 2021

Summary

➲Full paper

Accurate and high-resolution spatio-temporal information about crop phenology obtained from Synthetic Aperture Radar (SAR) data is an essential component for crop management and yield estimation at a local scale. Crop growth monitoring studies seldom exploit complete polarimetric information contained in dual-pol GRD SAR data. In this study, we propose three polarimetric descriptors: the pseudo scattering-type parameter (θc), the pseudo scattering entropy parameter (Hc), and the co-pol purity parameter (mc) from dual-pol S1 GRD SAR data. We also introduce a novel unsupervised clustering framework using Hc and θc with six clustering zones to represent various scattering mechanisms. We implemented the proposed algorithm on the cloud-based Google Earth Engine (GEE) platform for Sentinel-1 SAR data. We have shown the sensitivity of these descriptors over a time series of data for wheat and canola crops at a test site in Canada. From the leaf development stage to the flowering stage for both crops, the pseudo scattering-type parameter θc changes by approximately 17°. Moreover, within the entire phenology window, both mc and Hc varies by about 0.6. The effectiveness of θc and Hc to cluster the phenological stages for the two crops is also evident from the clustering plot. During the leaf development stage, about 90% of the sampling points were clustered into the low to medium entropy scattering zone for both the crops. Throughout the flowering stage, the entire cluster shifted into the high entropy vegetation scattering zone. Finally, during the ripening stage, the clusters of sample points were split between the high entropy vegetation scattering zone and the high entropy distributed scattering zone, with >55% of the sampling points in the high entropy distributed scattering zone. This innovative clustering framework will facilitate the operational use of S1 GRD SAR data for agricultural applications.

## 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

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.

## 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

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.

## Coastline Detection Based on Sentinel-1 Time-Series for Ship and Flood Monitoring Applications

R. Pelich, M. Chini, R. Hostache, P. Matgen and C. López-Martínez, “Coastline Detection Based on Sentinel-1 Time Series for Ship- and Flood-Monitoring Applications,” in IEEE Geoscience and Remote Sensing Letters, Early Access, 2020

Summary

This letter addresses the use of the Sentinel-1 time series with the aim of proposing an automatic and unsupervised coastline detection method that averages the dynamical variations of coastal areas over a limited period of time, e.g., one year. First, we propose applying a temporal averaging filter that allows the temporal variations in coastal areas, e.g., due to tides or vegetation, to be encapsulated, and, at the same time, the speckle to be reduced, without decreasing the spatial resolution of the synthetic aperture radar (SAR) time series. Then, based on the distinctive backscattering values of the sea and land pixels, we will employ an iterative hierarchical tiling method in order to accurately characterize the two classes using bimodal distribution. The distribution is then segmented by a thresholding and region-growing procedure to separate the sea and land classes. A large-scale quantitative comparison between the SAR-derived and open street map (OSM) coastlines allows for a numerical evaluation of the results, i.e., an overall agreement ranging from 80% to 90%. In addition, Sentinel-2 images are used to evaluate the estimated SAR coastline qualitatively. Furthermore, the benefits of having an accurate SAR coastline are shown in the case of two well-known Earth observation-monitoring applications, ship detection, and floodwater mapping.