Special Issue on Applied Earth Observation and Remote Sensing in Latin America

This special Issue focused on recent research led by South American researchers and teams. It is a long overdue pos- sibility offered to researchers in this geographical area to share their excellent work with the international community. Accord- ingly, the response to the call for papers was overwhelming, with more than 60 papers submitted from eight countries. Eventually, 23 articles were accepted, among which 11 are authored from Brazil, while Argentina and Mexico contribute each with five papers, and Colombia and Ecuador have one arti- cle accepted each. Testifying the international breadth of these researches, seven of these contributions have coauthors from outside Latin America: two from Italy, and one from France, Canada, Finland, USA, and Germany. Before describing the contributions that have been selected for this issue, it is worth recalling briefly the history and current situation of remote sensing activities in the three major countries in the area, which, as mentioned, contribute to the large majority of the works published in the following pages.ITESO, A.C

Bandt-Pompe symbolization dynamics for time series with tied values: A data-driven approach

In 2002, Bandt and Pompe [Phys. Rev. Lett. 88, 174102 (2002)] introduced a successfully symbolic encoding scheme based on the ordinal relation between the amplitude of neighboring values of a given data sequence, from which the permutation entropy can be evaluated. Equalities in the analyzed sequence, for example, repeated equal values, deserve special attention and treatment as was shown recently by Zunino and co-workers [Phys. Lett. A 381, 1883 (2017)]. A significant number of equal values can give rise to false conclusions regarding the underlying temporal structures in practical contexts. In the present contribution, we review the different existing methodologies for treating time series with tied values by classifying them according to their different strategies. In addition, a novel data-driven imputation is presented that proves to outperform the existing methodologies and avoid the false conclusions pointed by Zunino and co-workers.Fil: Traversaro Varela, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Lanús; ArgentinaFil: Redelico, Francisco Oscar. Hospital Italiano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes; ArgentinaFil: Risk, Marcelo. Hospital Italiano; Argentina. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Frery, Alejandro César. Universidade Federal de Alagoas; BrasilFil: Rosso, Osvaldo Aníbal. Hospital Italiano; Argentina. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Los Andes; Chil

Assessment of SAR Image Filtering using Adaptive Stack Filters

Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is then filtered by a Boolean function, which characterizes the filter. Adaptive stack filters can be designed to be optimal; they are computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work we study the performance of adaptive stack filters when they are applied to Synthetic Aperture Radar (SAR) images. This is done by evaluating the quality of the filtered images through the use of suitable image quality indexes and by measuring the classification accuracy of the resulting images

Polarimetric SAR Image Segmentation with B-Splines and a New Statistical Model

We present an approach for polarimetric Synthetic Aperture Radar (SAR) image region boundary detection based on the use of B-Spline active contours and a new model for polarimetric SAR data: the GHP distribution. In order to detect the boundary of a region, initial B-Spline curves are specified, either automatically or manually, and the proposed algorithm uses a deformable contours technique to find the boundary. In doing this, the parameters of the polarimetric GHP model for the data are estimated, in order to find the transition points between the region being segmented and the surrounding area. This is a local algorithm since it works only on the region to be segmented. Results of its performance are presented

The Roles of the S3MPC: Monitoring, Validation and Evolution of Sentinel-3 Altimetry Observations

The Sentinel-3 Mission Performance Centre (S3MPC) is tasked by the European Space Agency (ESA) to monitor the health of the Copernicus Sentinel-3 satellites and ensure a high data quality to the users. This paper deals exclusively with the effort devoted to the altimeter and microwave radiometer, both components of the Surface Topography Mission (STM). The altimeters on Sentinel-3A and -3B are the first to operate in delay-Doppler or SAR mode over all Earth surfaces, which enables better spatial resolution of the signal in the along-track direction and improved noise reduction through multi-looking, whilst the radiometer is a two-channel nadir-viewing system. There are regular routine assessments of the instruments through investigation of telemetered housekeeping data, calibrations over selected sites and comparisons of geophysical retrievals with models, in situ data and other satellite systems. These are performed both to monitor the daily production, assessing the uncertainties and errors on the estimates, and also to characterize the long-term performance for climate science applications. This is critical because an undetected drift in performance could be misconstrued as a climate variation. As the data are used by the Copernicus Services (e.g., CMEMS, Global Land Monitoring Services) and by the research community over open ocean, coastal waters, sea ice, land ice, rivers and lakes, the validation activities encompass all these domains, with regular reports openly available. The S3MPC is also in charge of preparing improvements to the processing, and of the development and tuning of algorithms to improve their accuracy. This paper is thus the first refereed publication to bring together the analysis of SAR altimetry across all these different domains to highlight the benefits and existing challenges

420,000 year assessment of fault leakage rates shows geological carbon storage is secure

Carbon capture and storage (CCS) technology is routinely cited as a cost effective tool for climate change mitigation. CCS can directly reduce industrial CO2 emissions and is essential for the retention of CO2 extracted from the atmosphere. To be effective as a climate change mitigation tool, CO2 must be securely retained for 10,000 years (10 ka) with a leakage rate of below 0.01% per year of the total amount of CO2 injected. Migration of CO2 back to the atmosphere via leakage through geological faults is a potential high impact risk to CO2 storage integrity. Here, we calculate for the first time natural leakage rates from a 420 ka paleo-record of CO2 leakage above a naturally occurring, faulted, CO2 reservoir in Arizona, USA. Surface travertine (CaCO3) deposits provide evidence of vertical CO2 leakage linked to known faults. U-Th dating of travertine deposits shows leakage varies along a single fault and that individual seeps have lifespans of up to 200 ka. Whilst the total volumes of CO2 required to form the travertine deposits are high, time-averaged leakage equates to a linear rate of less than 0.01%/yr. Hence, even this natural geological storage site, which would be deemed to be of too high risk to be selected for engineered geologic storage, is adequate to store CO2 for climate mitigation purposes

A PolSAR scattering power factorization framework using a geodesic distance

International audienc

A PolSAR Scattering Power Factorization Framework and Novel Roll-Invariant Parameter-Based Unsupervised Classification Scheme Using a Geodesic Distance

International audienceWe propose a generic scattering power factorization framework (SPFF) for polarimetric synthetic aperture radar (PolSAR) data to directly obtain N scattering power components along with a residue power component for each pixel. Each scattering power component is factorized into similarity (or dissimilarity) using elementary targets and a generalized volume model. The similarity measure is derived using a geodesic distance between pairs of 4×4 real Kennaugh matrices. In standard model-based decomposition schemes, the 3×3 Hermitian-positive semi-definite covariance (or coherency) matrix is expressed as a weighted linear combination of scattering targets following a fixed hierarchical process. In contrast, under the proposed framework, a convex splitting of unity is performed to obtain the weights while preserving the dominance of the scattering components. The product of the total power (Span) with these weights provides the nonnegative scattering power components. Furthermore, the framework, along with the geodesic distance (GD) is effectively used to obtain specific roll-invariant parameters such as scattering-type parameter (α GD ), helicity parameter (τ GD ), and purity parameter (P GD ). A P GD /α GD unsupervised classification scheme is also proposed for PolSAR images. The SPFF, the roll invariant parameters, and the classification results are assessed using C-band RADARSAT-2 and L-band ALOS-2 images of San Francisco