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

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.

Polarimetric SAR Time Series Change Analysis Over Agricultural Areas

A. Alonso-González, C. López-Martínez, K. P. Papathanassiou and I. Hajnsek, “Polarimetric SAR Time Series Change Analysis Over Agricultural Areas,” in IEEE Transactions on Geoscience and Remote Sensing, Early Access, 2020

Summary

This article proposes a change detection and analysis technique for monitoring the phenological development of agricultural vegetation by means of multitemporal Polarimetric Synthetic Aperture Radar (PolSAR) acquisitions. The technique relies on the generalized eigendecomposition of the polarimetric covariance matrices of the individual acquisitions. It both quantifies the magnitude of the change between PolSAR images acquired at different times and also provides an interpretation of occurred change in terms of the modified polarization states. This makes the algorithm suitable for investigating scattering dynamics associated with the phenological development of agricultural vegetation. To aid the interpretation of the changes detected, a representation based on the polarization states affected by the change process is proposed. The technique is evaluated using part of the multitemporal AGRISAR 2006 campaign data set. This data set consists of 12 quad-polarimetric images acquired by the German Aerospace Center (DLR) E-SAR airborne system at L-band from April 2006 to August 2006 over the Demmin test site. It covers large parts of the development cycle of different crop types. As a part of the evaluation, reference ground measurements are used to facilitate the interpretation of the data. The evaluation focuses on five important crop types: wheat, barley, rape, maize, and sugar beet. The results show that the proposed technique is able to detect and characterize different types of changes related to distinct development states of different crop types as the plant growing, maturation, and drying processes.

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.

Summary

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.