Polarimetric SAR for the monitoring of agricultural crops

The monitoring of agricultural crops is a matter of great importance. Remote sensing has been unanimously recognized as one of the most important techniques for agricultural crops monitoring. Within the framework of active remote sensing, the capabilities of the Synthetic Aperture Radar (SAR) to provide fine spatial resolution and a wide area coverage, both in day and night time and almost under all weather conditions, make it a key tool for agricultural applications, including the monitoring and the estimation of phenological stages of crops. The monitoring of crop phenology is fundamental for the planning and the triggering of cultivation practices, since they require timely information about the crop conditions along the cultivation cycle. Due to the sensitivity of polarization of microwaves to crop structure and dielectric properties of the canopy, which in turn depend on the crop type, retrieval of phenology of agricultural crops by means of polarimetric SAR measurements is a promising application of this technology, especially after the launch of a number of polarimetric satellite sensors. In this thesis C-band polarimetric SAR measurements are used to estimate pheno- logical stages of agricultural crops. The behavior of polarimetric SAR observables at different growth stages is analyzed and then estimation procedures, aimed at the retrieval of such stages, are defined. The second topic on which this thesis is focused on is the land cover types discrimi- nation by means of X-band multi-polarization SAR data

Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data

In the last decade, suboptimal Bayesian filtering (BF) techniques, such as Extended Kalman Filtering (EKF) and Particle Filtering (PF), have led to great interest for crop phenology monitoring with Synthetic Aperture Radar (SAR) data. In this study, a novel approach, based on the Grid-Based Filter (GBF), is proposed to estimate crop phenology. Here, phenological scales, which consist of a finite number of discrete stages, represent the one-dimensional state space, and hence GBF provides the optimal phenology estimates. Accordingly, contrarily to literature studies based on EKF and PF, no constraints are imposed on the models and the statistical distributions involved. The prediction model is defined by the transition matrix, while Kernel Density Estimation (KDE) is employed to define the observation model. The approach is applied on dense time series of dual-polarization Sentinel-1 (S1) SAR images, collected in four different years, to estimate the BBCH stages of rice crops. Results show that 0.94 ≤ R2 ≤ 0.98, 5.37 ≤ RMSE ≤ 7.9 and 20 ≤ MAE ≤ 33.This research was funded in part by the Spanish Ministry of Science and Innovation (State Agency of Research, AEI) and the European Funds for Regional Development (EFRD) under Projects TEC2017-85244-C2-1-P and PID2020-117303GB-C22, and in part by the University of Alicante (ref. VIGROB-114)

Model-Based Decomposition of Dual-Pol SAR Data: Application to Sentinel-1

In this study, we advance a new family of model-based decompositions adapted for dual-pol synthetic aperture radar data. These are formulated using the Stokes vector formalism, coupled to mappings from full quad-pol decomposition theory. A generalized model-based decomposition is developed, which allows separation of an arbitrary Stokes vector into partially polarized and polarized wave components. We employ the widely used random dipole cloud as a volume model but, in general, non-dipole options can be used. The cross-polarized phase δ, and the α angle, which is a function of the ratio between wave components, measure the transformation of polarization state on reflection. We apply the decomposition to dual-pol data provided by Sentinel-1 covering different scenarios, such as agricultural, forest, urban and glacial land-ice. We show that the polarized term of received polarization state is not usually the same as the transmitted one, and can therefore be used for key applications, e.g., classification and geo-physical parameter estimation. We show that, for vegetated terrain, depolarization is not the only influencing factor to Sentinel-1 backscattered intensities and, in the case of vertical crops (e.g., rice), this allows the crop orientation effects to be decoupled from volume scattering in the canopy. We demonstrate that coherent dual-pol systems show strong phase signatures over glaciers, where the polarized contribution significantly affects the backscattered state, resulting in elliptical polarization on receive. This is a key result for Sentinel-1, for which dual-pol phase analysis coupled to dense time series offer great opportunities for land-ice monitoring.This work was funded by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Projects TEC2017-85244-C2-1-P and PID2020-117303GB-C22, and by the University of Alicante under grant VIGROB-114

Retrieval of phenological stages of onion fields during the first year of growth by means of C-band polarimetric SAR measurements

The phenological stages of onion fields in the first year of growth are estimated using polarimetric observables and single-polarization intensity channels. Experiments are undertaken on a time series of RADARSAT-2 C-band full-polarimetric synthetic aperture radar (SAR) images collected in 2009 over the Barrax region, Spain, where ground truth information about onion growth stages is provided by the European Space Agency (ESA)-funded agricultural bio/geophysical retrieval from frequent repeat pass SAR and optical imaging (AgriSAR) field campaign conducted in that area. The experimental results demonstrate that polarimetric entropy or copolar coherence when used jointly with the cross-polarized intensity allows unambiguously distinguishing three phenological intervals.This work was partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER, under Project TEC2011-28201-C02-02

Stroke by inducing HDAC9-dependent deacetylation of HIF-1 and Sp1, promotes TfR1 transcription and GPX4 reduction, thus determining ferroptotic neuronal death

: Background: The inhibition of histone deacetylase 9 (HDAC9) represents a promising druggable target for stroke intervention. Indeed, HDAC9 is overexpressed in neurons after brain ischemia where exerts a neurodetrimental role. However, mechanisms of HDAC9-dependent neuronal cell death are not yet well established. Methods: Brain ischemia was obtained in vitro by primary cortical neurons exposed to glucose deprivation plus reoxygenation (OGD/Rx) and in vivo by transient middle cerebral artery occlusion. Western blot and quantitative real-time polymerase chain reaction were used to evaluate transcript and protein levels. Chromatin immunoprecipitation was used to evaluate the binding of transcription factors to the promoter of target genes. Cell viability was measured by MTT and LDH assays. Ferroptosis was evaluated by iron overload and 4-hydroxynonenal (4-HNE) release. Results: Our results showed that HDAC9 binds to hypoxia-inducible factor 1 (HIF-1) and specificity protein 1 (Sp1), two transcription activators of transferrin 1 receptor (TfR1) and glutathione peroxidase 4 (GPX4) genes, respectively, in neuronal cells exposed to OGD/Rx. Consequently, HDAC9 induced: (1) an increase in protein level of HIF-1 by deacetylation and deubiquitination, thus promoting the transcription of the pro-ferroptotic TfR1 gene; and (2) a reduction in Sp1 protein levels by deacetylation and ubiquitination, thus resulting in a down-regulation of the anti-ferroptotic GPX4 gene. Supporting these results, the silencing of HDAC9 partially prevented either HIF-1 increase and Sp1 reduction after OGD/Rx. Interestingly, silencing of the neurodetrimental factors, HDAC9, HIF-1, or TfR1 or the overexpression of the prosurvival factors Sp1 or GPX4 significantly reduced a well-known marker of ferroptosis 4-HNE after OGD/Rx. More important, in vivo, intracerebroventricular injection of siHDAC9 reduced 4-HNE levels after stroke by preventing: (1) HIF-1 and TfR1 increase and thus the augmented intracellular iron overload; and (2) a reduction of Sp1 and its target gene GPX4. Conclusions: Collectively, results obtained suggest that HDAC9 mediates post-traslational modifications of HIF-1 and Sp1 that, in turn, increases TfR1 and decreases GPX4 expression, thus promoting neuronal ferroptosis in in vitro and in vivo models of stroke

Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data

In the last decade, suboptimal Bayesian filtering (BF) techniques, such as Extended Kalman Filtering (EKF) and Particle Filtering (PF), have led to great interest for crop phenology monitoring with Synthetic Aperture Radar (SAR) data. In this study, a novel approach, based on the Grid-Based Filter (GBF), is proposed to estimate crop phenology. Here, phenological scales, which consist of a finite number of discrete stages, represent the one-dimensional state space, and hence GBF provides the optimal phenology estimates. Accordingly, contrarily to literature studies based on EKF and PF, no constraints are imposed on the models and the statistical distributions involved. The prediction model is defined by the transition matrix, while Kernel Density Estimation (KDE) is employed to define the observation model. The approach is applied on dense time series of dual-polarization Sentinel-1 (S1) SAR images, collected in four different years, to estimate the BBCH stages of rice crops. Results show that 0.94 ≤ R2 ≤ 0.98, 5.37 ≤ RMSE ≤ 7.9 and 20 ≤ MAE ≤ 33

Thermal Noise Removal From Polarimetric Sentinel-1 Data

This study proposes, for the first time, an approach to remove thermal noise from the wave coherency matrix, C₂, estimated from single-look complex dual-polarization Interferometric Wide Swath mode Sentinel-1 synthetic aperture radar data. The approach is straightforward; it exploits the ThermalNoiseRemoval module, provided by the European Space Agency (ESA) in its Sentinel Application Platform (SNAP) software, to remove thermal noise from the channel intensities. Then, noise correction on the complex data is applied, in order to estimate the noise-free C₂ matrix. As a further novelty, the proposed approach can be implemented in SNAP, through the use of a processing graph that is here provided. The method is applied on a dense time series of Sentinel-1 data, collected on an agricultural area located near Seville, Spain. The impact of thermal noise on the estimation of the eigendecomposition parameters of C₂, i.e., entropy (H₂), average alpha angle (α₂), and anisotropy (A₂), is assessed for different land-cover types, namely river, rice, forest, and urban areas. Monte Carlo simulations are implemented to assess the performance of the proposed approach in estimating H₂, α₂, and A₂. Results show that the proposed noise removal method improves the estimation of these parameters for the considered land-cover classes

A New Methodology for Rice Area Monitoring with COSMO-SkyMed HH-VV PingPong Mode SAR Data

In this paper, a novel approach is proposed to exploit a time series of COSMO-SkyMed (CSK) HH-VV SAR images to map rice fields and to estimate the sowing dates. The approach relies on multi-polarization features, i.e., the squared modulus of the HH and VV channels and the polarization ratio, extracted from CSK SAR scenes. The key step consists of extracting a rice training signature related to the multipolarization features. This signature allows estimating the sowing date that, at once, is used to refine the rice map obtained by the conventional interpretation of the CSK time series in terms of the scattering mechanisms of the different growing cycles. Experiments, carried out on a time series of 32 CSK images, collected from the Mekong Delta region, South Vietnam, confirm the soundness of the proposed methodology which is shown to provide results comparable to the ones obtained by a literature approach that exploits a similar dataset

Mieloma multiplo: da plasmocitoma a coinvolgimento multiorgano

Multiple myeloma: from plasmacytoma to multi-organic involvement. Solitary plasmacytoma is a rare form of plasma cell dyscrasia characterized by localized proliferation of neoplasticmonoclonal plasmacells. The lesion can originate in bone or in soft tissue, with no or minimal evidence of bonemarrow plasmacytosis (<10%) and absence of end-organ damage signs such as hypercalcaemia, renal insufficiency,anaemia, or bone lesions (CRAB). We present a case of solitary bone plasmacytoma (SPB) that rapidly evolved tomultiple myeloma (MM). A partial response was obtained within few months of chemotherapy but then the diseaserapidly progressed with involvement of liver, kidneys and lungs. Salvage therapy (bendamustine-bortezomib-dexamethasone, 1 cycle) had no effect and the patient died shortly after. Biochemical work up plays a central role inthe follow up of MM patients, as recommended by international guidelines. In some cases the disease is soaggressive that early diagnosis and treatment fail to improve the outcome

A Complete Procedure for Crop Phenology Estimation With PolSAR Data Based on the Complex Wishart Classifier

A new methodology to estimate the growth stages of agricultural crops using the time series of polarimetric synthetic aperture radar (PolSAR) images is proposed. The methodology is based on the complex Wishart classifier and both phenological intervals and training areas are identified measuring the distances among polarimetric covariance matrices obtained from the time series of PolSAR data. Consequently, the computation of PolSAR features, which is the main step of state-of-the-art methods, is no longer needed, and the proposed approach can be applied in the same way to any crop type. Experiments undertaken on a dense time series of fully polarimetric C-band RADARSAT-2 images, collected at incidence angles ranging from 23° to 39°, in ascending/descending orbit passes, demonstrate that the proposed methodology can be successfully applied to retrieve the phenological stages of four different crop types. In addition, the effect of combining beams corresponding to different sensor's configurations has been evaluated, showing that it affects the retrieval accuracies. Validation with ground data shows the following: overall accuracy is between 54% and 86%; producer's accuracy (PA) and user's accuracy (UA) range between 21% and 100% and between 33% and 100%, respectively