This paper reports on the European Space Agency (ESA) SARSimHT-NG experiment conducted in 2022 over a well-researched location in southern Italy. The experiment involved extensive ground data gathering coordinated with the German Aerospace Center’s (DLR) F-SAR airborne acquisitions in C- and L-bands. The campaign aimed to simulate and analyse geostationary Synthetic Aperture Radar (GeoSAR) measurements over an agricultural area. Besides, it explored the synergy between GeoSAR systems and other Low Earth Orbit (LEO) satellite missions, namely Sentinel-1 Next Generation (NG) and the Radar Observation System for Europe in L-band (ROSE-L). The hyper-temporal repeat cycle of GEoSAR systems is instrumental in investigating the rapid processes of the water cycle. The concept was developed for the Hydroterra mission proposal submitted to the ESA Earth Explorer 10 call and studied in phase 0. The paper illustrates the soil and vegetation in situ data collected together with the L- and C-band fully polarimetric SAR time series acquired at a very high revisit. Moreover, insights into the retrieval of the surface dynamics of the water cycle are also provided.

This paper provides an in-depth exploration of the Crop Module within the "EarTH Observation for the Early forecasT of Irrigation needS (THETIS)" project, specifically addressing challenges in precision agriculture. The study unfolds in the "Fortore" irrigation district (Southern Italy), focusing in particular on the 6/B district. The Crop Module, rooted in AquaCrop crop model architecture, emerges as a pivotal component in simulating and predicting crop growth, development, and water dynamics. It operates across leaf development, crop growth and productivity, and water balance levels, ensuring adaptability to daily temperature variations for real-time simulations. In interaction with the Soil Water Balance Module (SWB) and leveraging insights from satellite imagery, the Crop Module undergoes meticulous calibration and validation. The expected outcomes encompass increased precision in irrigation scheduling, early anticipation of water demand, and improved seasonal forecasting. This comprehensive approach positions stakeholders for informed decision-making, fostering sustainability and efficiency in agricultural practices.

This paper reports on a Spatial Decision Support System (SDSS) for the early, medium, and short-term forecast of irrigation needs in a semi-arid Mediterranean environment. The SDSS is developed in the context of the "EarTH Observation for the Early forecasT of Irrigation needS (THETIS)" project supported by the Italian Space Agency (ASI). THETIS integrates hydrologic and crop growth models with advanced Earth Observation (EO) products, Artificial Intelligence (AI) and a WEBGIS interface to provide basin-scale information for efficient planning of irrigation resources. The study describes initial results concerning the irrigated area of the Apulian Tavoliere (AT) served by the Reclamation Consortium of the Capitanata, Foggia, Italy.

An algorithm to identify and monitor tillage practices, using Copernicus Sentinel-1 (S-1) and Sentinel-2 (S-2) data, is presented. The technique operates on agricultural fields that are either bare or sparsely vegetated. These fields are first segmented using the Normalized Difference Vegetation Index (NDVI), obtained from S-2, or the S-1 VH/VV ratio in overcast conditions. Then, a change detection approach is applied both to S-1 cross-polarized backscatter and copolarized interferometric coherence. To decouple the impact of tillage from that of moisture change on radar measurements, a two-scale strategy is used. The premise is that whereas soil moisture is primarily influenced by precipitation events happening at the medium (1.0-10 km) scale, tillage changes occur at the local, i.e., field (~0.1 km) scale. The algorithm was assessed against a multi-year ground data set collected at three sites. It includes conventional tillage change and no-tilled events. Results achieve an overall accuracy of 81%.

The objective of this paper was to assess the use of multi-frequency SAR data for the mapping and monitoring of the spatial and temporal variability of land surface parameters and agricultural practices. In particular, the focus was on the retrieval of surface soil moisture (SSM) and vegetation water content (VWC) and on the classification and monitoring of irrigation extent and tillage practices at high resolution. The paper illustrates the data basis collected over three European sites, namely Apulian Tavoliere (Southern Italy), Jolanda di Savoia (Northern Italy), and Castilla y Leon (Spain), and the main results.

In this contribution, the application of an algorithm based on Sentinel-1 and Sentinel-2 data to identify tillage changes over agricultural fields at approximately ∼100m resolution is shown.The aims is to asses the capability of the tool to detect on a large spatial scale tillage events and their temporal repetitiveness. In this respect, this tool can be employed for monitoring fields where the Conservation Agriculture – that has in the no-tillage a main base principle – is applied.The methodology employs a multiscale temporal change detection on S-1 VH backscatter in order to single out VH changes due to agricultural practices only. The algorithm can be applied over bare or scarcely vegetated agricultural fields, which are identified from S-2 NDVI measurements.The good accuracy level (better than 80%) derived from a comparison with ground truth data acquired over the Apulian Tavoliere agricultural site, fosters to further improve the tool for practical applications.

The study aims to consolidate and validate a suite of Earth Observation algorithms of interest for applications in agriculture. The algorithms are at different levels of maturity. Still, they share the objective of contributing to sustainable water management and food security. They deal with monitoring the soil moisture, the vegetation water content, the extent of irrigated areas and the changes in the surface roughness of agricultural fields. The paper introduces the data sets, the algorithms and discusses some examples of initial results.

This study proposes a hybrid incoherent–coherent change detection (CD) approach to retrieve surface soil moisture (SSM) from Sentinel-1 data. It combines time-series observations of synthetic aperture radar (SAR) backscatter and interferometric closure phase to deliver a method that does not require external calibration. A proof-of-concept assessment based on synthetic and experimental data is presented. Sentinel-1 andin situdata over a study site in Southern Italy during the Winter–Spring season 2017 that covered both bare and vegetated soil conditions have been acquired and analyzed. For bare soils, results indicate good performance, that is, Pearson correlation ≈0.8 and root mean square error (RMSE) ≈0.05 m3/m3. Conversely, over vegetated surfaces, poor results are found.

The upcoming launch of the L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) will enable multi-frequency SAR observations when combined with existing C-band satellite missions (e.g., Sentinel-1). Due to the different penetration depths of the SAR signals, multi-frequency SAR offers great potential for field-scale agricultural monitoring and the estimation of soil and plant parameters. The SARSense campaign, conducted between June and August 2019 at the Selhausen agricultural test site near Jülich, Germany, has yielded a comprehensive dataset that includes both air- and space-borne C- and L-band SAR data, extensive in-situ field measurements of soil and plant parameters as well as unmanned aerial systems (UAS)-based multispectral and thermal infrared measurements and cosmic neutron sensing observations. The study provides both, an insight into the strengths and limitations of the acquired dataset as well as an analysis of the different behaviour of C- and L-band backscattering on changing soil moisture and plant parameters for taproot crops and cereals.

Approximately, 30% of the Sentinel-1 (S-1) swath over land is imaged with incidence angles higher than 40°. Still, the interplay among the scattering mechanisms taking place at such a high incidence and their implications on the backscatter information content is often disregarded. This article investigates, through an experimental and numerical study, the S-1 sensitivity to the surface soil moisture (SSM) over agricultural fields observed at low (~33°) and high (~43°) incidence angles and quantifies the impact of the incidence angle on the SSM retrieval accuracy. The study sites are the Apulian Tavoliere (Italy) and REd de MEDición de la HUmedad del Suelo (REMEDHUS) (Spain), which are both instrumented with a hydrologic network continuously measuring SSM. At low incidence angles, results confirm that for crops such as wheat and barley, dominated in C-band by surface scattering, there exists a good sensitivity of S-1 VV to SSM. At high incidence angles, the sensitivity to SSM holds through the combination of the soil attenuated and double bounce scattering. Conversely, over crops dominated by volume scattering, such as sugar beet, the S-1 VV signal is not correlated with the in situ SSM observations, neither at low nor at high incidence. For all the crops, the sensitivity of S-1 to SSM in VH is found significantly lower than in VV. The impact of the incidence angle on the SSM retrieval has been studied with a recursive algorithm based on a short-term change detection approach. An upper and lower bounds for the worsening of the S-1 VV retrieval performance at far versus near range observations have been estimated. In the worst-case scenario, the root mean square error (RMSE) increases from ~0.056 m3/m3, at low incidence, to ~0.071 m3/m3, at high incidence. The mechanism that lowers the retrieval accuracy at high incidence angles is further investigated in the synthetic experiment and its impact on the RMSE is estimated in terms of the volume scattering contribution.

This paper takes stock of a Sentinel-1 (S-1) surface soil moisture (SSM) product, developed in the ESA SEOM project “Exploitation of Sentinel-1 for Surface Soil Moisture Retrieval at High Resolution” (Exploit-S-1). The characteristics of the product are illustrated and the benefits of the synergy with the future L-band Radar Observation System for Europe (ROSE-L) mission are discussed.

In summer 2019 the SARSense campaign was held in Jülich, Germany, to provide insights into the potentials and specifications of the ESA Copernicus candidate mission ROSE-L (Radar Observation System for Europe). ROSE-L will consist of two satellites that carry a polarimetric L-band SAR. Since the L-band signal can penetrate through many natural materials such as vegetation, dry snow and ice, the mission will provide additional information that cannot be gathered by the Copernicus Sentinel-1 C-band SAR mission. The overall objective of the SARSense 2019 campaign is to analyze the mission design concerning its potential for agricultural monitoring services including target applications such as soil moisture monitoring, irrigation management, crop type discrimination, food security and precision farming. The SARSense in situ measurements of soil moisture, soil temperature, vegetation properties, UAS-based multispectral and thermal mapping, as well as the airborne SAR observations are presented as well as strategies for soil moisture retrieval and first analysis.

This paper investigates the sensitivity of Sentinel-1 (S-1) interferometric coherence to crop structure and near surface soil moisture (SSM) content. The study analyzes a data set collected in 2017 over the Apulian Tavoliere agricultural site (Southern Italy). The data set includes: i) in situ data over more than 600 agricultural fields monitored during the 2017 winter and spring growing seasons; ii) time-series of S-1 IW VV & VH backscatter & interferometric coherence; iii) time series of S-1 SSM maps. The temporal behavior of S-1 coherence and VH backscatter has been assessed over the monitored agricultural fields. Initial results indicate a stronger sensitivity of S-1 coherence than VH backscatter to crop geometric structure. In addition, an analysis at site scale, conducted before and after an important rain event, indicates a change of SSM from 0.18 to 0.30 m3/m3along with a change of S-1 coherence from 0.61 to 0.53.

This paper presents an experimental sensitivity analysis of Sentinel-1 (S-1) backscatter to soil moisture (SM) content observed at low (i.e., ~33°) and high (i.e., ~45°) incidence angles over five agricultural fields of an experimental farm located in the Puglia region (Italy). The analysis focuses on the period from March to June 2017 during which 38 S-1 images along ascending orbits were acquired over the site. Results indicate a slight decrease in the radar sensitivity to SM going from low to high incidence with an impact on SM retrieval error that increases from ~5.65 m3/m3% to ~7.63 m3/m3%.

Many reasons make NFV an attractive paradigm for IT security: lowers costs, agile operations and better isolation as well as fast security updates, improved incident responses and better level of automation. On the other side, the network threats tend to be increasingly complex and distributed, implying huge traffic scale to be monitored and increasingly strict mitigation delay requirements. Considering the current trend of the networking and the requirements to counteract to the evolution of cyber-threats, it is expected that also network monitoring will move towards NFV based solutions. In this paper, we present D-StreaMon an NFV-capable distributed framework for network monitoring realized to face the above described challenges. It relies on the StreaMon platform, a solution for network monitoring originally designed for traditional middleboxes. An evolution path which migrates StreaMon from middleboxes to Virtual Network Functions (VNFs) has been realized.

In this paper we show how the familiar concept of gradient descent can be extended in presence of binary neurons. The procedure we devised formally operates on generic feedforward networks of logistic-like neurons whose activations are re-scaled by an arbitrarily large gauge. Whereas the gradient decays exponentially with increasing values of the gauge, the sign of each component becomes definitely equal to a constant value. Those values are actually computed by means of a "twin" network of binary neurons. This allows the application of any "Manhattan" training algorithm such as resilient propagation.

Analog artificial neural networks (ANN) could be the core of an "intelligent" signal processor, with the today used digital processing replaced by a raw "data driven" methodology. Characteristic of this approach is: analog input/output: signals don't need A/D and D/A converters; speed: an analog system can be faster than a digital or a mixed-mode one; effectiveness: ANN already demonstrated their power in many applications; low power: analog circuits can save power. NESP2 is a neural signal processor which offers the above characteristics and is based on a traditional, available and not expensive double polysilicon double metal commercial VLSI process.