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.
In 2020, we produced the 2020 European Commission Copernicus MOOC in collaboration with many other colleagues and experts in Earth observation, and excellently coordinated by the Competence Center of the University of Luxembourg. During the two on-line editions, more than 9500 people from 169 country registered in the MOOC, demonstrating the large interest in this type of training. Now, the complete MOOC is in open access for everyone who wants to learn about the European Union’s Earth observation programme Copernicus.
The course addresses three key topics in twelve different modules:
Chapter 1 – Understanding Copernicus data and services– what they are, and how they can be accessed and used
Module 1 – Introduction to Copernicus
Module 2: Accessing Copernicus data and services
Chapter 2 – Learning from success stories – understanding how existing Copernicus-enabled services and applications have been developed and deployed
Module 3: Renewable energy
Module 4: Security and Emergency Management
Module 5: Resource management
Module 6: Land Use and Management
Module 7: Air quality, water pollution and ecosystem health monitoring
Module 8: Combining Copernicus data with other types of data; AI; Machine learning
Chapter 3 – Doing it yourself – acquiring the key skills and knowledge to develop and deploy Copernicus-enabled products and services and to navigate the Copernicus ecosystem
Module 9: Ideation – Build up your idea for a Copernicus-enabled product or service
Module 10: Prototyping – Test and validate your Copernicus-enabled product or service
Module 11: Developing – Successfully develop your Copernicus-enabled product or service
Module 12: Collaboration – Working together to develop the next generation of Copernicus-enabled services
All the modules contain plenty of training material: videos, handbooks and additional references.
The course modules are taught in English by internationally-recognised experts and successful practitioners. Different engaging and interactive formats are used during the lessons, from webinars and videos to use cases and projects. The course is built on a user-driven approach aimed at enabling participants to work on their own projects, learn from others, understand the latest trends in situational awareness technologies, and become active members of the Copernicus community.
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
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.
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.
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
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.