L-Band SAR Co-Polarized Phase Difference Modeling for Corn Fields

Barber, M.E.; Rava, D.S.; López-Martínez, C. “L-Band SAR Co-Polarized Phase Difference Modeling for Corn Fields”. Remote Sens. 202113, 4593, Nov. 2021


➲ Open access full paper

This paper aims at modeling the microwave backscatter of corn fields by coupling an incoherent, interaction-based scattering model with a semi-empirical bulk vegetation dielectric model. The scattering model is fitted to co-polarized phase difference measurements over several corn fields imaged with fully polarimetric synthetic aperture radar (SAR) images with incidence angles ranging from 20° to 60°. The dataset comprised two field campaigns, one over Canada with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR, 1.258 GHz) and the other one over Argentina with Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) (ALOS-2/PALSAR-2, 1.236 GHz), totaling 60 data measurements over 28 grown corn fields at peak biomass with stalk gravimetric moisture larger than 0.8 g/g. Co-polarized phase differences were computed using a maximum likelihood estimation technique from each field’s measured speckled sample histograms. After minimizing the difference between the model and data measurements for varying incidence angles by a nonlinear least-squares fitting, well agreement was found with a root mean squared error of 24.3° for co-polarized phase difference measurements in the range of −170.3° to −19.13°. Model parameterization by stalk gravimetric moisture instead of its complex dielectric constant is also addressed. Further validation was undertaken for the UAVSAR dataset on earlier corn stages, where overall sensitivity to stalk height, stalk gravimetric moisture, and stalk area density agreed with ground data, with the sensitivity to stalk diameter being the weakest. This study provides a new perspective on the use of co-polarized phase differences in retrieving corn stalk features through inverse modeling techniques from space.

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


➲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.

Proposed schematic workflow to derive the dual-polarimetric descriptors from Sentinel-1 dual-pol GRD SAR data on the GEE platform.

Book: Polarimetric Synthetic Aperture Radar

➲ Open access book

Our open access book “Polarimetric Synthetic Aperture Radar – Principles and Applications”, funded by the European Space Agency, has been recently published by Springer. This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from space borne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans.

Book chapters:

  • Basic Principles of SAR Polarimetry by C. López-Martínez, E. Pottier
  • Forest Applications by K. P. Papathanassiou, S. R. Cloude, M. Pardini, M. J. Quiñones, D. Hoekman, L. Ferro-Famil et al.
  • Agriculture and Wetland Applications by J. M. Lopez-Sanchez, J. D. Ballester-Berman, F. Vicente-Guijalba, S. R. Cloude, H. McNairn, J. Shang et al.
  • Cryosphere Applications by I. Hajnsek, G. Parrella, A. Marino, T. Eltoft, M. Necsoiu, L. Eriksson et al.
  • Urban Applications by E. Colin-Koeniguer, N. Trouve, Y. Yamaguchi, Y. Huang, L. Ferro-Famil, V. D. Navarro Sanchez et al.
  • Ocean Applications by M. Migliaccio, F. Nunziata, A. Marino, C. Brekke, S. Skrunes
Book cover.

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


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


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.