This paper explores the use of Polarimetric SAR Interferometry (PolInSAR) in order to separate the Ground and Volume components of the SAR signal in the context of agricultural monitoring. This separation assumes a PolInSAR two-layer model and allows to extract the covariance matrix of each component. Then, a polarimetric change analysis technique may be employed to analyze independently the temporal evolution of the ground and volume components.The technique is evaluated with real data from the DLR’s F-SAR sensor CROPEX campaign, containing fully polarimetric PolInSAR data at L-, C- and X-band frequencies. Special attention is given to the validity of the PolInSAR two-layer volume at different frequencies and acquisition dates and the limitations of this separation technique.

This work compares the observed interferometric coherence from high-resolution F-SAR data with the predictions of a physical interferometric repeat-pass soil moisture model over agricultural areas. Differences between wheat fields and bare surfaces are analyzed at C- and L-bands. At C-band, the wheat crops strongly influence the temporal coherence and obscure the signal from the changes in soil moisture. At the L-band, the effect of vegetation is less pronounced, and a high coherence is observed. For bare surfaces, C-band coherence is high in areas with no changes and lower in drying areas, better matching the model predictions. At the L-band, the observed coherence is higher than model predictions.

This study presents a Digital Twin (DT) that is being created to optimize the use of the available hydric resources, and mitigate the effects of the increasing water shortage in irrigated agriculture in fields in the Urgell channel region (Lleida). A DT is "a virtual representation of an object or system that spans its lifecycle, it is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making." It will model the water fluxes using the knowledge of the amounts of water taken in, used, and returned to the environment, and other parameters that impact the water budget, such as atmospheric variables (temperature, water vapor deficit, relative humidity, solar radiance…), surface soil moisture, and evapotranspiration maps, etc. Satellite Earth Observation (EO) data, collocated with in-situ data from a network of 20 soil moisture probes and 2 meteo stations will be used to train the DT. Additionally, a rover-based ground penetrating radar will be used for cross-calibration.

This study addresses discerning causal relationships in complex systems, a key aspect of interpretable machine learning. It focuses on the unusual and intense early summer weather in South Asia during April and May 2022 that led to an increased number of forest fires. This work employs a Bayesian network (BN), constructed using the NOTEARS algorithm, to analyse the contribution of various land and atmospheric variables on the extent of burned areas. In a scenario analysis using peak values of 300-hPa meridional circulation index and 500-hPa Geopotential Height Anomalies, indicative of a strong atmospheric block, the likelihood of large burned areas (>3.06 log ha or >1150 ha) increases from 36.6% to 41.6%. This is due to a rise of conditional probabilities in the Vapor Pressure Deficit (VPD) (> 5.21 kPa) by 24.8%, and the Land Surface Temperature (LST) (>45.7°C) by 15.6%. In addition, sensitivity and spatial analyses indicate that extreme dry conditions, characterized by high LST and VPD due to the trapping effects of the omega block jet stream pattern, were the primary factors influencing the extent of burned areas during the 2022 South Asia heatwave.

This study presents a methodology for disaggregating the ESA Climate Change Initiative (CCI) Soil Moisture (SM) maps from 0.25° to a 60 m grid, using a feedforward neural network. This technique is applied over an area of 66,700 km2, encompassing parts of Oklahoma and Kansas (US), throughout 2021. The disaggregation approach leverages synergies between different variables, including various Sentinel-2 bands and indices, land surface temperature from MODIS, accumulated precipitation from ERA5-Land, terrain elevation and slope from the STRM, and soil composition. The methodology employs a two-step process: (i) the model is first trained using all the variables at low resolution (0.25°), and (ii) it is then employed to estimate the SM at high resolution using the input variables at 60 m. Results indicate that the model trained at low resolution achieves a reasonably high accuracy over the testing data (RMSE3•m-3and R2>0.9). The preliminary analysis of the 60 m resolution SM maps and their comparison with two in-situ stations show a strong correlation (R>0.8), and uRMSE close to 0.04 m3•m-3, with a bias ranging from 0.022 m3•m-3to 0.06 m3•m-3.

A Random Forest (RF) regression-tree method to derive high-resolution (60 m) surface soil moisture maps is proposed in this study. The developed methodology integrates multi-source synergies by incorporating information from the visible, near-infrared until short-wave infrared spectrum (Sentinel-2), reanalysis data (ERA5-Land) and terrain information (SRTM), using exclusively open access data. The analysis focuses on the central part of the Iberian Peninsula and covers a four-year period (2018-2021). The resulting high-resolution soil moisture maps exhibit greater spatial heterogeneity compared to the ESA Climate Change Initiative (CCI) soil moisture, which was used as a reference in the training of the RF model. These maps have been evaluated using in situ soil moisture measurements from the REMEDHUS network, and show good agreement in terms of Pearson's correlation (0.83), and uRMSE (0.028 m3•m-3), demonstrating the method’s significant potential for deriving high-resolution soil moisture information.

Tensor decompositions are a powerful tool for multidimensional data analysis, interpretation, and signal processing. This work develops a constrained tensor decomposition framework for complex multidimensional Synthetic Aperture Radar (SAR) data. The framework generalizes the Canonical Polyadic (CP) decomposition by formulating it as an optimization problem and allows precise control over the shape and properties of the output factors. The implementation supports complex tensors, automatic differentiation, different loss functions, and optimizers. We discuss the importance of constraints for physical validity, interpretability, and uniqueness of the decomposition results. To illustrate the framework, we formulate a polarimetric time series decomposition and apply it to data acquired over agricultural areas to analyze the development of four crop types at X, C, and L bands over the period of twelve weeks. The obtained temporal factors describe the changes in the crops in a compact way and show a correlation to certain crop parameters. We extend the existing polarimetric time series change analysis with the decomposition to show the changes in more detail and provide an interpretation through the polarimetric factors. The decomposition framework is extensible and promising for joint information extraction from multidimensional SAR data.

Polarimetric SAR Interferometry (Pol-InSAR) is a SAR remote sensing discipline with unique and powerful applications related to the vertical structure of natural and man-made volume scatterers. The coherent combination of single- or multi -baseline interferograms acquired at different polarisations provides sensitivity to the vertical distribution of scattering processes and allows their characterisation by using the associated (volume) interferometric coherences.

This paper analyzes the potential of the joint exploitation of the Spanish PAZ satellite and the two TerraSAR-X/TanDEM-X German satellites for interferometry. Since both platforms are almost identical and they were launched on the same orbital plane, similar images under the same geometry may be acquired by both missions. Several time series over different test sites with distinct land cover types in Germany and Spain that have been acquired by both missions are compared in order to evaluate the possibility of joint exploitation for interferometry. Results show no loss of performance on the in-terferograms obtained while combining satellites of both missions. This allows to effectively reduce the minimum revisit time from 11 days to 4 or 7 days.

Polarimetric SAR Interferometry (Pol-InSAR) is a SAR remote sensing discipline with unique and powerful applications related to the vertical structure of natural and man-made volume scatterers. The coherent combination of single- or multi -baseline interferograms acquired at different polarisations provides sensitivity to the vertical distribution of scattering processes and allows their characterisation by using the associated (volume) interferometric coherences [1]–[5].

The BIOMASS DEM Product Prototype Processor (BIO-DEMPP) is being developed in the frame of ESA's Earth Explorer BIOMASS mission. The prototype includes a complete interferometric SAR chain, from the stack co-registration until the mosaicking of the derived height products (Digital Elevation and Digital Terrain Models). This paper presents an overview of the BIODEMPP architectural design and its validation strategy, as well as first results obtained with simulated BIOMASS-like data.

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.

This work introduces a methodology to separate the ground and volume contributions from Polarimetric SAR Interferometric acquisitions. Based on the employed two layer model, the radar response may be decomposed into these two main contributions and the polarimetric covariance matrices of the corresponding ground and volume layers may be extracted from the data. The technique will be evaluated with a real single- and multi-baseline fully Polarimetric SAR data acquired by the E-SAR airborne sensor over forest.

In this paper, polarimetric multi-baseline (MB) Synthetic Aperture Radar (SAR) Interferometry data are used to estimate the polarimetric ground component under vegetation. However, the solution of the applied separation algorithm is not unique and depends on the constraints in the regularization. First, the effect of this non-uniqueness is analyzed and then exploited to isolate a ground component with minimized influence of depolarizing scattering mechanisms. Using experimental MB SAR data acquired by DLR's airborne sensor F-SAR, the polarimetric entropy and mean alpha angle of the isolated ground component are compared to the original polarimetry of the full image. Finally, the ground polarimetry is interpreted for changing soil moisture vegetation conditions in corn. To this purpose, three dates are compared characterized by 1) a change in soil moisture, 2) a change in vegetation cover or 3) a simultaneous change of soil moisture and vegetation cover.

SAR polarimetry (PolSAR) can play an important role in change detection both in terms of improving the detection capabilities and providing physical information regarding the change. In agricultural context, such information can be used to help retrieving the phenological stage and eventually identifying stress conditions. In this work, a new change detection based on PolSAR data is proposed. The methodology is based on the use of the normalised difference between covariance matrices acquired at two different instants. A diagonalisation of such matrix allows identifying the scattering mechanisms that suffer the largest change. The methodology is tested exploiting L-band quad-polarimetric E-SAR (DLR) data from the AGRISAR 2006 campaign.

The standard TanDEM-X baselines have been designed to optimize the high resolution global Digital Elevation Model (DEM) generation. However, during the Science Phase of the mission longer baselines are available. This allows interferometric measurements with a higher vertical sensitivity, more appropriate for agricultural applications, where the crop heights are too small to be properly detected and analyzed with the standard baselines. This paper evaluates the use of the experimental long baseline TanDEM-X acquisitions for the monitoring of the agricultural changes in dual-pol single-pass interferometric time series.

This work focuses on the exploitation of PolSAR and PolInSAR temporal series datasets in the context of change detection and characterization. Instead of a classical pixel-based approach, a Binary Partition Tree (BPT) data structure is employed to perform a region-based processing of the image. Once the homogeneous regions of the data set are obtained, the temporal dimension of the data is analyzed to quantify the significance of the polarimetric temporal variation. In this work, the PolInSAR information will also be exploited for this purpose, in order to achieve a more detailed characterization of change and no-change areas.

Speckle noise represents one of the main components of the multidimensional SAR signal that limit the complete exploitation of multidimensional SAR data, and polarimetric SAR data in particular. This paper consider the main trends in the characterization and the filtering of the multidimensional Speckle noise component.

This paper deals with the processing of polarimetric synthetic aperture radar (SAR) time series. Different approaches to deal with the temporal dimension of the data are considered, which are derived from different target characterizations in this dimension. These approaches are the basis for defining two different binary partition tree (BPT) structures that are employed for SAR polarimetry (PolSAR) data processing. Once constructed, the BPT is processed by a tree pruning, producing a set of spatiotemporal homogeneous regions, and estimating the polarimetric response within them. It is demonstrated that the proposed technique preserves the PolSAR information in the spatial and the temporal domains without introducing bias nor distortion. Additionally, the evolution of the data in the temporal dimension is also analyzed, and techniques to obtain BPT-based scene change maps are defined. Finally, the proposed techniques are employed to process two real RADARSAT-2 data sets.

This paper investigates the validity of the random-volume-over-ground (RVoG) scattering model assumption for forest scattering on polarimetric interferometric synthetic aperture radar (PolInSAR) data. The model makes some assumptions about the data and the structure of coherency matrices, namely, the equality of the polarimetric covariance matrices and the affine equivalence of the contracted polarimetric interferometric covariance matrix with a Hermitian matrix. The proposed methodology is divided into two main steps. First, invertible affine transforms (ATs) are studied and proposed as a tool to operate with coherence regions. Based on this analysis, the concept of the trace matrix is introduced as its rank depends on the RVoG model assumption validity. Then, with the objective to consider the effects of speckle noise, we consider a maximum-likelihood (ML) framework, on the hypothesis of data distributed according to the complex Gaussian distribution. Hence, we define the ML estimator (MLE) of the PolInSAR coherency matrix according to the RVoG model assumption and the generalized likelihood ratio test of the model. The validity tests and the MLE are analyzed in terms of simulated and real PolInSAR data, considering P-band and L-band data over tropical and boreal forests.

A novel analysis of the statistics of the eigendecomposition of the coherency matrix and the H/A/a¯ parameters of polarimetric synthetic aperture radar data is addressed. The objective is to overcome previous approaches that prevented the extraction of information about the sample eigenvectors or restricted the analysis to simulated data. This paper considers a perturbation analysis of the eigendecomposition of the coherency matrix, making it possible to obtain analytical expressions for the sample eigenvalues and their means and variances, the sample mean entropy and anisotropy, the sample eigenvectors and the sample ai angles, as well as for the sample mean alpha angle a¯. All the parameters are shown to be estimated asymptotically non-biased with respect to the number of averaged samples. It is also demonstrated that the sample eigenvectors are more robust than the sample eigenvalues to the presence of speckle. Finally, a simple technique for the precise removal of the entropy bias is presented.

In this paper the exploitation of PolSAR temporal series datasets is presentedin the context of change detection and characterization. A Binary Partition Tree (BPT) data structure is employed in order to extract homogeneous regions of the image containing pixels that are following a similar polarimetric temporal evolution. Then the temporal dimension of the data is analyzed firstly to quantify the significance of the polarimetric temporal variation, making possible the detection of scene changes, and secondly to analyze and characterize those changes. Finally, the proposed technique is employed to process a real RADARSAT-2 dataset to show its capabilities and potentialities.

This work addresses the analysis of the validity of the Random-Volume-over-Ground scattering model for forests studies based on PolInSAR data. The analysis is based on the definition of a Generalized Likelihood Ratio Test that allows to test the model validity for all the pixels of the images and without any external information. Finally, the validity of the RVoG models assumption is tested for data from Tropical and Boreal forests imaged at P- and L-band at different interferometric configurations.

The eigendecomposition of the coherency matrix, as well as the Entropy, Anisotropy and mean Alpha angle are crucial for the physical understanding of the scattered echo in SAR polarimetry. This contribution considers a perturbation analysis of the coherency matrix that allows the statistical characterization of all the previous parameters. In addition, a novel and effective algorithm for the unbiased estimation of the Entropy parameter is presented.

The current increase of spatial as well as spectral resolutions of modern remote sensing sensors represents a real opportunity for many practical applications but also generates important challenges in terms of image processing. In particular, the spatial correlation between pixels and/or the spectral correlation between spectral bands of a given pixel cannot be ignored. The traditional pixel-based representation of images does not facilitate the handling of these correlations. In this paper, we discuss the interest of a particular hierarchical region-based representation of images based on binary partition tree (BPT). This representation approach is very flexible as it can be applied to any type of image. Here both optical and radar images will be discussed. Moreover, once the image representation is computed, it can be used for many different applications. Filtering, segmentation, and classification will be detailed in this paper. In all cases, the interest of the BPT representation over the classical pixel-based representation will be highlighted.

In this paper, the processing of temporal PolSAR image series is addressed through a region-based and multi-scale data representation, the Binary Partition Tree (BPT). This structure contains useful information related to the data structure at different detail levels that may be employed for different applications. The construction of this structure ans its exploitation is addressed in this work in the context of the speckle filtering and data segmentation applications. A new region model and processing strategy are defined to tackle with the temporal dimension of the data. Finally, to illustrate the capabilities of the proposed technique, results are shown with a real RADARSAT-2 dataset.

This paper presents a Polarimetric SAR data speckle filtering technique, based on a combined filtering in the spatial and polarimetric domains. It is based on a bilateral filtering employing distance measures over these domains. These measures concentrate all the information related to the domain structure that is needed for an adaptation to the scene morphology. A weighted average is performed over a given window favoring closer and similar pixels. As a consequence, an adaptive filtering is achieved, attaining higher filtering over homogeneous areas whereas point scatters remain almost unchanged. Results will be shown over a real RADARSAT-2 data.

In this paper, the study of polarimetric optimization techniques for Differential SAR Interferometry (DInSAR) applications is analyzed. This work has been carried out in the framework of deformation map retrieval on landslides. A large number of landslides occur on vegetated areas with a poor density of temporal coherent scatterers, which are characterized by a fast decorrelation at X-band. The objective of the techniques proposed in this paper is to increase the number of temporal coherent scatterers to improve the robustness of the DInSAR algorithms exploiting the polarimetric capabilities of data. The relationship between optimum coherences and its corresponding phase quality in terms of DInSAR application is analyzed using Ground-Based SAR zero-baseline fully-polarimetric data.

In this paper,we propose the use of binary partition trees (BPT) to introduce a novel region-based and multi-scale polarimetric SAR (PolSAR) data representation. The BPT structure represents homogeneous regions in the data at different detail levels. The construction process of the BPT is based, firstly, on a region model able to represent the homogeneous areas, and, secondly, on a dissimilarity measure in order to identify similar areas and define the merging sequence. Depending on the final application, a BPT pruning strategy needs to be introduced. In this paper, we focus on the application of BPT PolSAR data representation for speckle noise filtering and data segmentation on the basis of the Gaussian hypothesis, where the average covariance or coherency matrices are considered as a region model. We introduce and quantitatively analyze different dissimilarity measures. In this case, and with the objective to be sensitive to the complete polarimetric information under the Gaussian hypothesis, dissimilarity measures considering the complete covariance or coherency matrices are employed. When confronted to PolSAR speckle filtering, two pruning strategies are detailed and evaluated. As presented, the BPT PolSAR speckle filter defined filters data according to the complete polarimetric information. As shown, this novel filtering approach is able to achieve very strong filtering while preserving the spatial resolution and the polarimetric information. Finally, the BPT representation structure is employed for high spatial resolution image segmentation applied to coastline detection. The analyses detailed in this work are based on simulated, as well as on real PolSAR data acquired by the ESAR system of DLR and the RADARSAT-2 system.

The aim of this paper is to present a Polarimetric Synthetic Aperture Radar data processing technique on the space-time domain. This approach is based on a Binary Partition Tree (BPT), which is a region-based and multi-scale data representation. Results with series of RADARSAT-2 real data are analyzed from the point of view of speckle filtering and change detection applications, to illustrate the capabilities to detect and preserve spatial and temporal contours.

Polarimetric SAR Interferometry makes possible a detailed analysis of the volumetric scattering processes present in microwave scattering in case of forest areas. In previous contributions, the authors, under the hypothesis of the RVoG coherent scattering model, developed a process for the direct extraction of the ground topography. In this paper, the authors present a generalization of this technique allowing to determine a set of scattering mechanisms making possible an optimization process for the estimation of the ground topography. The applicability of this technique, in case of X-band polarimetric SAR interferometric data, is explored considering data obtained in the frame of the TanDEM-X mission.

A new multi-scale PolSAR data filtering technique, based on a Binary Partition Tree (BPT) representation of the data, is proposed. Different alternatives for the construction and the exploitation of the BPT for filtering and segmentation are presented.\nResults with simulated and experimental PolSAR data are presented to shown the capabilities of the BPT-filtering strategy to maintain both spatial details and the polarimetric information.

The work detailed in this paper analyzes the topographic phase retrieval process on forested areas by means of Polarimetric Interferometric SAR data. On the basis of the Random Volume over Ground scattering model, an alternative implementation for the retrieval of the topographic phase, avoiding the bias introduced by the volumetric scattering components is presented.