Abstract
In Portugal, almonds are a very important crop, due to their nutritional properties. In the northeastern part of the country, the almond sector has endured over time, with strong cultural traditions and key economic significance. In these areas, several cultivars are used. In effect, the presence of various almond cultivars implies differentiated management in irrigation, disease control, pruning system, and harvest planning. Therefore, cultivar classification is essential over large agricultural areas. Over the last decades, remote-sensing data have led to important breakthroughs in the classification of different cultivars for several crops. Nonetheless, for almonds, studies are incipient. Thus, this study aims to fill this knowledge gap and explore the classification of almond cultivars in an almond orchard. High-resolution multispectral data were acquired by an unmanned aerial vehicle (UAV). Vegetation indices (VIs) and tree structural parameters were, subsequently, estimated. To obtain an accurate cultivar identification, four machine learning classifiers, such as K-nearest neighbour (kNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were applied and optimized through the fine-tuning process. The accuracy of machine learning classifiers was analysed. SVM and RF performed best with OAs of 76% and 74% using VIs and spectral bands (GREEN, GRVI, GN, REN, ClRE). Adding the canopy height model (CHM) improved performance, with RF and XGBoost having OAs of 88% and 84%. kNN performed worst with an OA of 73% using only VIs and spectral bands, 80% with VIs, spectral bands and CHM, and 93% with VIs, CHM, and tree crown area (TCA). The best performance was achieved by RF and XGBoost with OAs of 99% using VIs, CHM, and TCA. These results demonstrate the importance of the feature selection process. Moreover, this study reveals the feasibility of remote-sensing data and machine learning classifiers in the classification of almond cultivars.

Abstract
In this paper, two Persistent Scatterer Interferometry (PSI) methodologies are compared in order to further understand their potential in the detection of surface deformation. A comparison of these two algorithms is a comparison of the two classes of PSI techniques available: coherence estimation based on a temporal model of deformation (represented by DePSI) and coherence estimation based on spatial correlation (represented by StaMPS). Despite the similarity between the results obtained from the application of the two independent PSI methodologies, significant differences in PS density and distribution were detected, motivating a comparative study between both techniques. We analyze which approach might be more appropriate for studying specific areas/environments, which is helpful in evaluating the benefits that could be derived from an integration of the two methodologies. Several experiments are performed to assess the sensitivity of both PSI approaches to different parameter settings and circumstances. The most significant differences in the processing chain of both procedures are then investigated. We apply both methodologies to the Granada Basin area (southern Spain) and realize that coherence does not improve significantly as function of the methodology applied. If oversampling is implemented in the StaMPS processing chain, the PS density increases so that the density in the urbanized areas is similar to the results provided by DePSI but in all the remaining covers the density is significantly higher. The general results provided by both approaches are very similar in the relative deformations estimated.

Abstract
Differential SAR interferometry (DInSAR) is a very effective technique for measuring crustal deformation. However, almost all interferograms include large areas where the signals decorrelate and no measurements are possible. Persistent scatterer interferometry (PS-InSAR) overcomes the decorrelation problem by identifying resolution elements whose echo is dominated by a single scatterer in a series of interferograms. Two time series of 29 ERS-1/2 and 22 ENVISAT ASAR acquisitions of the Granada basin, located in the central sector of the Betic Cordillera (southern Spain), covering the period from 1992 to 2005, were analyzed. Rough topography of the study area associated to its moderate activity geodynamic setting, including faults and folds in an uplifting relief by the oblique Eurasian-African plate convergence, poses a challenge for the application of interferometric techniques. The expected tectonic deformation rates are in the order of similar to 1 mm/yr, which are at the feasibility limit of current InSAR techniques. In order to evaluate whether, under these conditions, InSAR techniques can still be used to monitor deformations we have applied and compared two PS-InSAR approaches: DePSI, the PS-InSAR package developed at Delft University of Technology (TU Delft) and StaMPS (Stanford Method for Persistent Scatterers) developed at Stanford University. Ground motion processes have been identified for the first time in the study area, the most significant process being a subsidence bowl located at the village of Otura. The idea behind this comparative study is to analyze which of the two PS-InSAR approaches considered might be more appropriate for the study of specific areas/environments and to attempt to evaluate the potentialities and benefits that could be derived for the integration of those methodologies.

Abstract
Recently, two CAT-1 projects have been initiated related to Granada Basin area (Betic Cordillera, Southern Spain), aimed at the detection of deformation in the region applying time series InSAR methodology. Due to the outstanding availability of ERS-1/2 and Envisat acquisitions, a time span of more than 12 years is covered and time series can be produced, enabling us to assess the feasibility of monitoring deformation with millimetre precision. The ultimate goal of these projects is the quantification of displacements and determination of their mean directions, relating them to dynamic changes and stress accumulation in order to identify potential seismic hazard locations. For the moment, not all available scenes have been received, so a preliminary data processing has been carried out at the Padul fault area. In this paper, we present a status report of both projects.

Abstract
The Benínar Dam, located in Southeastern Spain, is an earth-fill dam that has experienced filtration issues since its construction in 1985. Despite the installation of various monitoring systems, the data collected are sparse and inadequate for the dam’s lifetime. The present research integrates Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) and dam modeling to validate the monitoring of this dam, opening the way to enhanced integrated monitoring systems. MT-InSAR was proved to be a reliable and continuous monitoring system for dam deformation, surpassing previously installed systems in terms of precision. MT-InSAR allowed the almost-continuous monitoring of this dam since 1992, combining ERS, Envisat, and Sentinel-1A/B data. Line-of-sight (LOS) velocities of settlement in the crest of the dam evolved from maximums of -6 mm/year (1992–2000), -4 mm/year (2002–2010), and -2 mm/year (2015–2021) with median values of -2.6 and -3.0 mm/year in the first periods (ERS and Envisat) and -1.3 mm/year in the Sentinel 1-A/B period. These results are consistent with the maximum admissible modeled deformation from construction, confirming that settlement was more intense in the dam’s early stages and decreased over time. MT-InSAR was also used to integrate the monitoring of the dam basin, including critical slopes, quarries, and infrastructures, such as roads, tracks, and spillways. This study allows us to conclude that MT-InSAR and dam modeling are important elements for the integrated monitoring systems of embankment dams. This conclusion supports the complete integration of MT-InSAR and 3D modeling into the monitoring systems of embankment dams, as they are a key complement to traditional geotechnical monitoring and can overcome the main limitations of topographical monitoring.

Abstract
Remote sensing, particularly satellite-based, can play a valuable role in monitoring areas prone to geohazards. The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential hazards. This paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data, with a particular emphasis on: (1) detecting mining subsidence, where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar (InSAR) technology. The results showed that the Efficient Channel Attention (ECA) U-Net model performed better than the U-Net (baseline) model in terms of Mean Intersection over Union (MIoU) and Intersection over Union (IoU) indicators; (2) monitoring water conservancy and hydropower engineering. The Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets (SBAS) InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north side. The dam body also showed obvious deformation with a velocity exceeding 60 mm/a; (3) the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural preservation. The overall outcome of these methods showed that the use of Artificial Intelligence (AI) techniques in combination with InSAR data leads to more efficient analysis and interpretation, resulting in improved accuracy and prompt identification of potential hazards; and (4) finally, this study also presents a method for detecting landslides in mountainous regions, using optical imagery. The new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods, this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time such as decades.