We report some results of an experiment to detect early occurrence of Xylella fastidiosa (Xf) in olive trees in the Apulia Region (southern Italy), performed in the framework of a project to assess the feasibility of a service addressed to agricultural authorities. An acquisition campaign was performed in September 2022, over a Xf-affected test area, using UAVborne hyperspectral and thermal sensors. Ground data were also collected through qPCR. Results of classification through SVM provide overall accuracy values ranging from 0.76 to 0.84.

The GEORES project is funded by the Italian Space Agency (ASI) and aims to develop a geospatial application meant to improve environmental sustainability and resilience to climate changes in urban areas, based on the synergistic use of the most advanced Earth Observation (EO) technologies, Artificial Intelligence (AI) and eXplainable AI (XAI). GEORES is organized into four main modules to support management of the main risks associated with land degradation: (1) Sediment Connectivity; (2) Land Displacement; (3) Urban Floods; (4) Urban Wildfires. For each module, EO data, calculation models and algorithms are integrated to identify "hot-spots" of urban and peri-urban territory at high risk from the point of view of land degradation caused by phenomena of hydrogeological instability, sediment flow or vegetation fires. The extracted information is expressed with specific indicators ("geo-analytics") calculated dynamically and automatically. The demonstration is undertaken in the Metropolitan City of Bari and Gargano Promontory, Apulia Region, southern Italy, and foresees the engagement of final users (i.e. Regional Civil Protection and Municipality of Bari).

Bayesian estimation of posterior probabilities for the presence of floodwaters, coupled with accurate time series regression methods, show good performance in the monitoring of inundations at high temporal and spatial resolution from long stacks of synthetic aperture radar (SAR) data. We report results on the integration of SAR intensity and cascaded InSAR coherence time series in different polarization channels within a Bayesian framework. The method is being tested over sites in both northern and southern Italy, with X- and C-band SAR data. The results indicate some advantage in using more than one independent channel in the Bayesian inference for some types of land cover, in terms of area under the curve (AUC) when compared to independent flood maps acquired over known events. Stacks of surface water confidence levels computed over both test sites show promising characteristics, both on agricultural and coastal areas.

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%.

Xylella fastidiosa (Xf) is a plant pathogen affecting olives trees, which has been identified as the bacterium responsible of a devastating landscape transformation in Apulia Region (Italy) from 2013. Actually, it has been found to affect 679 plant species worldwide, such as almond, vine and citrus.In this paper, experimental results concerning the automatic detection of trees infected by Xf from very high resolution hyperspectral and thermal images are shown. First of all, a set of vegetation indices and plant physiological traits related to rapid changes in photosynthetic pigments and leaf processes were computed from hyperspectral data. This information together with thermal data has been used as input to a RUSBoost classifier. Trees in training and test data set were labelled by performing quantitative real time-Polymerase-Chain-Reaction (qPCR) assays.Encouraging experimental results have been obtained, with Overall Accuracies greater than 90%, also when a reduced set of features is used as input for RUSBoost.

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.

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.

Multi-temporal remotely sensed data are a precious source of information for high spatial and temporal resolution flood mapping. We present a methodology for flood mapping through processing of long time series of Sentinel-l SAR data, as well as ancillary information. A Bayesian framework is adopted to derive probabilistic maps of the presence of flood waters, through modeling of backscatter time series, based on the as-sumption that floods represent impulsive temporal anomalies. We illustrate some results on a time series of Sentinel-l data acquired from 2015 to 2021 over a test area on the Basento river watershed, Basilicata Region, in Southern Italy, recurrently subject to floods.

We observe relatively high InSAR mean coherence levels over badlands, i.e. clayey bare soil areas, on a test site in the Basilicata region, in southern Italy. Time series of InSAR coherences on cascaded short-baseline image pairs, obtained from stacks of Sentinel-1 SAR images, exhibit oscillating behaviour, with significant correlation with cumulated rainfall levels on bad-land areas, while on other areas with crops or spontaneous vegetation the correlation is lower, and a seasonal trend is instead statistically significant. These observations seem to point to the possibility of investigating erosion phenomena over badland areas through InSAR time series, which involves a significant step forward, in terms of spatial and temporal resolution, with respect to traditional measurements which require repeated topographic surveys at long intervals, or sparse in-field point measurements.

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.

The objective of this study is to cross-compare three algorithms for retrieving surface soil moisture (SSM) from ESA's Sentinel-1 (S-1) data. The context is provided by the large scientific and application interest in SSM products at high resolution and regional/continental scale that can be retrieved from S-l data alone or in combination with other missions such as NASA/SMAP and ESA/SMOS. Of the three investigated algorithms, one inverts a scattering model exploiting a Bayesian approach, whereas the other two are change detection approaches. The cross-comparison is carried out by using both simulated and experimental data. Strengths and weaknesses of the three algorithms are identified and discussed.

Soil moisture content is an essential climate variable that is operationally delivered at low resolution (e.g. 36-9 km) by earth observation missions, such as ESA/SMOS, NASA/SMAP and EUMETSAT/ASCAT. However numerous land applications would benefit from the availability of soil moisture maps at higher resolution. For this reason, there is a large research effort to develop soil moisture products at higher resolution using, for instance, data acquired by the new ESA's Sentinel missions. The objective of this study is twofold. First, it presents the validation status of a pre-operational soil moisture product derived from Sentinel-1 at 1 km resolution. Second, it assesses the possibility of integrating Sentinel-2 data and additional ancillary information, such as parcel borders and high resolution soil texture maps, in order to obtain soil moisture maps at “field scale” resolution, i.e. ~0.1 km. Case studies concerning agricultural sites located in Europe are presented.

Multi-sensor, multi-band and multi-temporal remote sensing data can be very useful in precise flood monitoring. In this paper, we describe DAFNE, a Matlab'v-based, open source toolbox, to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach. DAFNE is based on Bayesian Networks, and is composed of several independent modules, each one performing a different task. Multi-temporal and multi-sensor data can be easily handled, with the possibility of producing time series of output flood maps, and thus follow the evolution of single or recurrent flood events. Here, an application of the toolbox is illustrated to delineate a flood map, close to the peak of inundation occurred in April 2015 on the Strymonas river (Greece), from multi-band optical and SAR data.

In this paper, an algorithm using Sentinel-1 (S-1) and Sentinel-2 (S-2) data to identify changes of tillage over agricultural fields at approximately ~100m resolution is presented. The methodology implements 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. An initial assessment at farm scale using in situ and S-1 and SPOT5-Take5 data, acquired over the Apulian Tavoliere in southern Italy in 2015, is illustrated. A full validation of the approach is in progress over three European agricultural areas located in Italy, Spain and France. Results will be further reported in the paper.

The systematic retrieval of near surface soil moisture (SSM) fields at high resolution (e.g., 0.1-1.0 km) is a challenging task that requires the exploitation of new retrieval algorithms and SAR data with advanced observational capabilities (in terms of spatial/temporal resolution, radiometric accuracy, very large swath, long-term continuity and rapid data dissemination). The launch of the Sentinel-1 (S-1) constellation provides these capabilities and calls for the development and validation of pre-operational SSM products at high resolution. The objective of this paper is to present and initially assess a SSM retrieval algorithm developed in view of S-1 data exploitation. The activity is supported by a large scientific community engaged in fostering a more effective interaction between researchers working in the field of high and low resolution SSM retrieval.

In precision flood monitoring it is important to follow the temporal evolution of an event. Often, however, sufficient temporal coverage of events spanning several days can be attained only by recurring to multi-sensor data, due to different acquisition characteristics and schedules of different types of sensors. We present an example of a successful fusion of data coming from both SAR (COSMO-SkyMed stripmap, 3-m resolution) and optical (RapidEye, multispectral, 5 m-resolution) data, covering a flood event in southern Italy. The data fusion is performed through a Bayesian network approach, a reliable means to infer probabilistic information from heterogeneous sources. Results show accordance with independent model-based flood maps reaching accuracies of up to 96%.

Accurate flood mapping is important for both planning activities during emergencies and as a support for the successive assessment of damaged areas. A valuable information source for such a procedure can be remote sensing synthetic aperture radar (SAR) imagery. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground. For this reason, a data fusion approach of remote sensing data with ancillary information can be particularly useful. In this paper, a Bayesian network is proposed to integrate remotely sensed data, such as multitemporal SAR intensity images and interferometric-SAR coherence data, with geomorphic and other ground information. The methodology is tested on a case study regarding a flood that occurred in the Basilicata region (Italy) on December 2013, monitored using a time series of COSMO-SkyMed data. It is shown that the synergetic use of different information layers can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to data obtained independently from the analysis of optical images; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, in spite of their heterogeneous temporal SAR/InSAR signatures, reaching accuracies of up to 89%.

High-resolution flood mapping is an essential step in the monitoring and prevention of inundation hazard, both to gain insight into the processes involved in the generation of flooding events, and from the practical point of view of the precise assessment of inundated areas, useful e.g. in the case of post-event recovery and insurance indemnity assessments. Synthetic Aperture Radar (SAR) data present several favourable characteristics for flood mapping, such as their relative insensitivity to the meteorological conditions during acquisitions, thanks to the use of microwaves as sensing radiation, as well as the possibility of acquiring imagery independently of solar illumination, thanks to the active nature of the radar sensors. The Italian COSMO-SkyMed (CSK) SAR constellation is particularly useful in this respect, because it allows image sequences of flooding events to be built up with short revisit times. The acquisition of several images before, during and after the event often allow a reconstruction of the flooding dynamics. Moreover, they help in interpreting the backscatter signatures of different land cover types, reducing uncertainties about the actual presence of water, which can be seriously misleading, especially over agricultural areas [1, 2]. Finally, when acquisitions are made from the same geometry, with short repeat intervals, SAR interferometry (InSAR) observables, such as the coherence or the differential InSAR phase can be exploited as additional information layers. The favorable characteristics of these next-generation sensors have been exploited by a number of researchers worldwide [3, 4, 5] to improve performances of flood mapping approaches. Recently, our group [6, 2] has used high-resolution CSK radar images for flood mapping exploiting both the intensity and the interferometric coherence, with promising results. Nevertheless, additional information can be used to improve flood detection. In case of flooding, distance from the river, terrain elevation, hydrologic information or some combination of these data can add useful information that leads to a better performance in flood detection.

This paper deals with oriented texture analysis applied to a problem of defect detection and classification in leather inspection, for industrial application. In this paper we present results obtained using a defects detector based on oriented texture analysis, which reveals itself useful for a few classes of leather defects, such as scars or folds. These defects can be detected by using a black and white camera running over the leather patch and by classifying textures, on the basis of their gradient orientations and local coherence. Leather defects are detected by segmenting the oriented texture map of the leather surface. The orientation in every point is expressed using a normalized B-spline basis whose knots are distributed on the leather surface. The resulting erector of coefficients can be used to perform the final segmentation and localize defective areas. A morphological segmentation procedure is applied to the regularized oriented texture field in order to extract probable defective areas.

In this paper a neural algorithm for defect detection in industrial inspection is proposed. One of the most difficult problems in process control and automated inspection is the identification, description and classification of surface defects and anomalies. A critical role in surface inspection is played by texture because most of the defects are rich in textural content. The goal of this work is to propose a quantitative and qualitative technique to detect surface defects with oriented texture structure. The neural algorithm, that the authors propose to classify an oriented flow field, can classify each kind of defect with textural characteristics: once the system has been set to recognize a limited number of patterns, it is able to identify and analyze a broader family of patterns. To the surface image to be analyzed is associated a vector field which computes the dominant local orientations of the gradients of the image smoothed with a Gaussian filter. The different defective surface regions are recovered and classified minimizing an energy function by means of a neural network. Experimental results of tests performed on ferromagnetic surfaces show as the proposed neural at algorithm works.