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.

Copernicus MOOC Webinar

On September 24th, 2020, in collaboration with the colleagues of the University of Luxembourg Competence Centre, we conducted the 2nd edition of Copernicus MOOC Webinar for Module 2 Accessing Copernicus data and services, with more than 250 participants.

The objective of the second module of the Copernicus MOOC is, in essence, to understand the Copernicus low level ecosystem, focusing in the following learning objectives:

  • Understand how to navigate in the Copernicus “labyrinth”;
  • Identify the various data types provided by Copernicus and how to access them;
  • Know and be able to compare the various services you may use to access and process Copernicus data.

In the ninety-minute webinar, we presented the basics of Synthetic Aperture Radar (SAR) and SAR Interferometry (InSAR), to better understand Sentinel-1 data. The presentation was followed by a one-hour Q&A session. As we saw in the first edition of the Copernicus MOOC in Spring 2020, we were impressed by the number and the quality of the questions raised by the different participants, indicating a clear interest in the use of Copernicus radar data.

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

➲ Full paper


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.

Sentinel-1 InSAR Coherence for LandCover Mapping: A comparison of multiple feature-based classifiers

Jacob, A., Vicente-Guijalba, F., Lopez-Martinez, C., Lopez-Sanchez, J.M., Litzinger, M., Kristen, H., Mestre-Quereda, A., Ziolkowski, D., Lavalle, M., Notarnicola, C., Suresh, G., Antropov, O., Ge, S., Praks, J., Ban, Y., Pottier, E., Mallorqui, J., Duro, J. & Engdahl, M., “Sentinel-1 InSAR Coherence for LandCover Mapping: A comparison of multiple feature-based classifiers”, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 13, pp. 535-552, 2020.

➲ Open access full paper


This work investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyses the performance of this feature along with polarisation and intensity products according to different classification strategies and algorithms. Seven different classification work flows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain: interferometric coherence, backscattered intensities and polarisation. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximise diversity land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Doñana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%), obtained also promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherencebased results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinels-1A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1A/B stacks, i.e., 6-day sampling, are considered.