PEOPLEBartolomeo Ventura

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Bartolomeo Ventura

Senior Technician
Institute for Earth Observation

T +39 0471 055 374

bartolomeo.ventura@eurac.edu

https://orcid.org/0000-0002-8898-4205

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About

Data manager of all the data collected by the Institute within various projects.

STAC catalog
FAIR data
Metadata in ISO-OGC standards
Coding and CI/CD pipeline

Bartolomeo Ventura is senior researcher at the Institute for Earth Observation since 2010. He received the Degree in Physics, 110/110, and the PhD in Physics from the University of Bari (Italy) in 2004 and 2008 respectively.

From 2009 to January 2010, he worked as a researcher for the University of Bari in the field of SAR and Radiometric data analysis to study the physical and morphological parameters characterizing northern liquid surfaces found on Titan and compare them with southern ones. At Eurac Research he is responsible for the research line “Data Management & Processing Science” (DMPS). DMPS aims to provide access and solutions to the processing environment for research, to foster the uptake of new technologies, and bridge to IT infrastructure and service providers. He is also responsible for managing and organising all the data collected by the Institute within various projects. The definition of the best strategy for the creation, organization, and maintenance of the data structures as well as the definition of access policies and the long-term data preservation represents the main goal for this task. He is also in charge of the creation of well-structured metadata (ISO, OGC and STAC standards-compliant) and easy data access.

ORCID: https://orcid.org/my-orcid?orcid=0000-0002-8898-4205

ResearchGate: https://www.researchgate.net/profile/B-Ventura

Academia: https://eurac.academia.edu/BVentura

Scientific Publications

Journal article2025

Enhancing mountain grassland mapping: A comparative study with PRISMA hyperspectral, multispectral, and SAR data

Patriarca E, Stendardi L, Dorigatti E, Sonnenschein R, Ventura B, Claus M, Castelli M, Tufail B,

Ruth Sonnenschein, Bartolomeo Ventura, Claudia Notarnicola, Mariapina Castelli, Michele Claus, Emanuela Patriarca

Mountain grasslands are increasingly threatened by climate change, land abandonment, and overexploitation. Remote sensing is a valuable tool for monitoring these changes through vegetation mapping. However, challenges such as frequent cloud cover, short growing seasons, and limited field data can reduce the accuracy of results. In this study, we evaluated the effectiveness of different remote sensing data for classifying mountain grasslands in the Sciliar-Catinaccio Nature Park, Italy. We compared classification results using a hyperspectral PRISMA image (Sept 29, 2023), multispectral data from a single-date Sentinel-2 image (Sept 25, 2023), and Spectral-Temporal Metrics (STM) derived from a Sentinel-2 time series from 2021 to 2023. Additionally, we assessed the impact on accuracy of combining optical datasets with Synthetic Aperture Radar (SAR) data, including a time series of 2023 Sentinel-1 backscatter and coherence metrics. Using the Recursive Feature Elimination algorithm (RFE), we selected the most relevant features for classification and applied both Random Forest (RF) and Support Vector Machines (SVM) classifiers. SVM outperformed RF, performing better with the limited training data available. SAR data did not significantly enhance classification and was therefore excluded by the RFE algorithm. PRISMA-based classification achieved up to 74 % accuracy, while single-date Sentinel-2 imagery reached 52 %. The use of STM improved classification performance, yielding an overall accuracy of 77 %. The highest accuracy (87 %) was achieved by combining PRISMA and STM features. These findings suggest that while individual optical datasets may not provide optimal classification accuracy, integrating data from multiple optical sensors significantly enhances the mapping of mountain grasslands.

DOI

DOI: 10.1016/j.rsase.2025.101666

Journal article2024

A Copernicus-based evapotranspiration dataset at 100 m spatial resolution over four Mediterranean basins

Bartkowiak P, Ventura B, Jacob A, Castelli M

Bartolomeo Ventura, Alexander Jacob, Mariapina Castelli, Paulina Bartkowiak

Project: 4DMED-Hydrology

Evapotranspiration (ET) is responsible for regulating the hydrological cycle, with a relevant impact on air humidity and precipitation that is particularly important in the context of acute drought events in recent years. With the intensification of rainfall deficits and extreme heat events, the Mediterranean region requires regular monitoring to enhance water resource management. Even though remote sensing provides spatially continuous information for estimating ET on large scales, existing global products with spatial resolutions ≥ 0.5 km are insufficient for capturing spatial detail at a local level. In the framework of ESA's 4DMED-Hydrology project, we generate an ET dataset at both high spatial and high temporal resolutions using the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model driven by Copernicus satellite data. We build an automatic workflow to generate a 100 m ET product by combining data from Sentinel-2 (S2) MultiSpectral Instrument (MSI) and Sentinel-3 (S3) land surface temperature (LST) with ERA5 climate reanalysis derived within the period 2017–2021 over four Mediterranean basins in Italy, Spain, France, and Tunisia (Po, Ebro, Hérault, and Medjerda). First, original S2 data are pre-processed before deriving 100 m inputs for the ET estimation. Next, biophysical variables, like leaf area index and fractional vegetation cover, are generated, and then they are temporally composited within a 10 d window according to S3 acquisitions. Consequently, decadal S2 mosaics are used to derive the remaining TSEB-PT inputs. In parallel, we sharpen 1 km S3 by exploiting the dependency between coarse-resolution LST and 100 m S2 reflectances using a decision tree algorithm. Afterwards, climate forcings are utilized to model energy fluxes and then for daily ET retrieval. The daily ET composites demonstrate reasonable TSEB-PT estimates. Based on the validation results against eight eddy covariance (EC) towers between 2017 and 2021, the model predicts 100 m ET with an average RMSE of 1.38 mm d−1 and a Pearson coefficient equal to 0.60. Regardless of some constraints mostly related to the high complexity of EC sites, TSEB-PT can effectively estimate 100 m ET, which opens up new opportunities for monitoring the hydrological cycle on a regional scale. The full dataset is freely available at https://doi.org/10.48784/b90a02d6-5d13-4acd-b11c-99a0d381ca9a, https://doi.org/10.48784/fb631817-189f-4b57-af6a-38cef217bad3, https://doi.org/10.48784/70cd192c-0d46-4811-ad1d-51a09734a2e9, and https://doi.org/10.48784/7abdbd94-ddfe-48df-ab09-341ad2f52e47 for the Ebro, Hérault, Medjerda, and Po catchments, respectively (Bartkowiak et al., 2023a–d).

DOI Source

DOI: 10.5194/essd-16-4709-2024

Conference proceedings article2024

Linking hyperspectral PRISMA data with ecosystem functional properties at ICOS sites

Vaglio Laurin G, Sebastiani A, Nardella L, Calfapietra C, Barbati A, Ventura B, Papale D

Bartolomeo Ventura

Selected Ecosystem Functional Properties, calculated from data collected by the flux tower stations of the ICOS network in Europe, were linked to vegetation indices extracted by satellite PRISMA hyperspectral data. Fourteen stations and sixty images were gathered for this preliminary research, in five different ecosystems including various forest types, grasslands, and wetlands. Several pre-processing steps, for both flux and PRISMA data, were needed prior to computation of correlation values and analysis of the fit for the most promising relationships. The results indicate that certain indices are highly correlated with Gross Primary Productivity, Net Ecosystem Exchange, Light Use Efficiency, and Water Use Efficiency, with the first three EFPs obtaining better values. Similar results are obtained also considering only broadleaved forest data. All the relationships resulted being linear or at the second polynomial order. Narrowband indices reporting information from the green to near infrared spectral region resulted the most useful ones. This effort shows the potential of upscaling tower station information to larger extent with PRISMA data, and additional research is planned expanding the dataset to other sites.

DOI Source

DOI: 10.1117/12.3033908

Database2023

Daily evaporation product - Ebro basin (Version v1) [Data set]

Bartkowiak P, Ventura B, Jacob A, Castelli M

Bartolomeo Ventura, Alexander Jacob, Mariapina Castelli, Paulina Bartkowiak

Project: 4DMED-Hydrology

Evaporation (E) is the estimated flux of water evaporated from the land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over the Ebro basin in Spain. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

DOI Source

DOI: 10.48784/b90a02d6-5d13-4acd-b11c-99a0d381ca9a

Database2023

Daily evaporation product - Herault basin (Version v1) [Data set]

Bartkowiak P, Ventura B, Jacob A, Castelli, M

Bartolomeo Ventura, Alexander Jacob, Mariapina Castelli, Paulina Bartkowiak

Project: 4DMED-Hydrology

Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Hérault basin in France. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

DOI Source

DOI: 10.48784/FB631817-189F-4B57-AF6A-38CEF217BAD3

Database2023

Daily evaporation product - Medjerda basin (Version v1) [Data set]

Bartkowiak P, Ventura B, Jacob A, Castelli M

Bartolomeo Ventura, Alexander Jacob, Mariapina Castelli, Paulina Bartkowiak

Project: 4DMED-Hydrology

Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Medjerda basin in Tunisia. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

DOI Source

DOI: 10.48784/70cd192c-0d46-4811-ad1d-51a09734a2e9

Database2023

Daily evaporation product - Po basin (Version v1) [Data set]

Bartkowiak P, Ventura B, Jacob A, Castelli M

Bartolomeo Ventura, Alexander Jacob, Mariapina Castelli, Paulina Bartkowiak

Project: 4DMED-Hydrology

Evaporation (E) is the estimated flux of water evaporated from land surface, including vegetation and bare soil. The product is generated from Two-source Energy Balance (TSEB) model forced by ESA Copernicus Sentinel-2A/B MSI and Sentinel-3A/B SLSTR LST imagery together with ECMWF ERA5 reanalysis data. Evaporation maps are available at 100-m spatial resolution for the period 2017-2021 over Po basin in Italy. The spatial extent corresponds to Sentinel-2 tiling grids overlapping with the study area. The E layer contains one single band with evaporation values per day [mm/day] corresponding to Sentinel-3 acquisition day. Invalid pixels, mainly due to cloud contamination and lack of the input data for TSEB, are filled with NaN values. Evaporation outputs are generated in Cloud Optimized GeoTIFF (COG) format with metadata included in the file attributes. Datasets are available for each month (Jan-Dec) of the year separately in the form of stacked daily E observations.

DOI Source

DOI: 10.48784/7ABDBD94-DDFE-48DF-AB09-341AD2F52E47

Journal article2023

Publishing Eurac Research data on the GEOSS Platform

Roncella R, Ventura B, Vianello A, Boldrini E, Santoro M, Mazzetti P, Nativi S

Bartolomeo Ventura, Andrea Vianello

This paper is the third of a series that introduces some of the main dataset resources presently shared through the GEOSS Platform. The GEOSS Platform is a brokering infrastructure that brokers more than 190 autonomous information systems and data catalogs; it was created to provide the technological tool to implement the Global Earth Observation System of Systems (GEOSS). This manuscript focuses on the analysis of Eurac Research datasets and illustrates the data publishing process to enroll the Eurac Research Data Provider to the GEOSS Platform through the administrative and technical registrations. The study provides an analysis of the GEOSS user searches for Eurac Research data in order to understand the main use of datasets of an important Data Provider.

DOI Source

DOI: 10.1080/20964471.2023.2187659

Journal article2019

A Methodology for Heterogeneous Sensor Data Organization and Near Real-Time Data Sharing by Adopting OGC SWE Standards

Ventura B, Vianello A, Frisinghelli D, Rossi M, Monsorno R, Costa A

Armin Costa, Roberto Monsorno, Bartolomeo Ventura, Andrea Vianello

Finding a solution to collect, analyze, and share, in near real-time, data acquired by heterogeneous sensors, such as traffic, air pollution, soil moisture, or weather data, represents a great challenge. This paper describes the solution developed at Eurac Research to automatically upload data, in near real-time, by adopting Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards to guarantee interoperability. We set up a methodology capable of ingesting heterogeneous datasets to automatize observation uploading and sensor registration, with minimum interaction required of the user. This solution has been successfully tested and applied in the Long Term (Socio-)Ecological Research (LT(S)ER) Matsch-Mazia initiative, and the code is accessible under the CC BY 4.0 license.

DOI Source

DOI: 10.3390/ijgi8040167

Conference proceedings article2017

Organizing Access to Complex Multi‐Dimensional Data: An Example from the ESA SEOM SInCohMap Project

Jacob A, Vicente-Guijalba F, Kristen H, Costa A, Ventura B, Monsorno R, Notarnicola C

Armin Costa, Roberto Monsorno, Bartolomeo Ventura, Claudia Notarnicola, Alexander Jacob

In a landscape of ever-growing access to free data, the problem of how to process all this data remains a challenge. With this article, we show how we tackle these challenges within the ESA SEOM project SInCohMap, where a critical task is to host a round robin to test different classification approaches for land cover mapping utilizing information derived from complex SAR data, in particular the evolution of multi-temporal coherence from the Copernicus Sentinel-1 satellites. Access to data and processing facilities is provided by the Eurac Research Sentinel Alpine Observatory on their computing infrastructure based on free open source technology, featuring cloud computing on OpenNebula and Kubernetes, multi-dimensional data arrays on Rasdaman and web-based python development on Jupyter.

DOI Source

DOI: 10.2760/383579

Journal article2017

Organizing earth observation data inside a spatial data infrastructure

Innerebner M, Costa A, Chuprikova E, Monsorno R, Ventura B

Ekaterina Chuprikova, Armin Costa, Roberto Monsorno, Bartolomeo Ventura

Scientists as well public institutions dealing with geospatial data often work with a large amount of heterogeneous data deriving from different sources. Without a well-defined, organized structure they face problems in finding and reusing existing data, and as consequence this may cause data inconsistency and storage problems. A catalog system based on the metadata of spatial data facilitates the management of large amount of data and offers service to retrieve, discover and exchange geographic data in an quick and easy fashion. Currently, most online catalogs are more focusing on the geographic data and there has been only few interests in catalogizing Earth observation data, in which in addition the acquisition information matters. This article presents an automatic metadata extraction approach that creates from different optical data deriving from various satellite missions of scientific interest (i.e. MODIS, LANDSAT, RapidEye, Suomi-NPP VIIRS, Sentinel-1A, Sentinel-2A) metadata information, based on an extended model of the standard ISO 19115. The XML schema ISO 19139-2 with the support of gridded and imagery information defined in ISO 19115-2 was examined, and based on the requirements of experts working in the research field of Earth observation the schema was extended. The XML schema ISO 19139-2 and its extension has been deployed as a new schema plugin in the spatial catalog Geonetwork Open Source in order to store all relevant metadata information about satellite data and the appropriate acquisition and processing information in an online catalog. A real-world scenario that is productively used in the EURAC research group institute for Applied Remote Sensing illustrates a workflow management for Earth observation data including data processing, metadata extraction, generation and distribution.

DOI Source

DOI: 10.1007/s12145-016-0276-0

Journal article2012

Inferring soil moisture variability in the Mediterrean Sea area using infrared and passive microwave observations

Notarnicola C, Caporaso L, Di Giuseppe F, Temimi M, Ventura B, Zebisch M

Bartolomeo Ventura, Claudia Notarnicola, Marc Zebisch

The objective of this study was to infer soil moisture variability from a combination of passive microwave and infrared satellite observations. The proposed approach is mainly based on the concept of apparent thermal inertia (ATI) and makes use of the daily gradient in brightness temperature from MODIS AQUA to infer soil moisture at moderate spatial resolution. Soil moisture retrievals from optical polar orbiting satellites are affected by discontinuities due to the presence of clouds and spurious fluctuations because of low temporal sampling, which is not sufficient for a reliable daily cycle sampling. To mitigate these limitations, we propose using soil moisture temporal trend derived from passive microwave based product, namely the NASA AMSR-E soil moisture product, to filter estimates from MODIS observations. Passive microwave-based soil moisture products exhibit less fluctuation because of their coarse resolution and lower sensitivity to atmosphere. They can therefore be considered as natural “low pass filters” thus reducing the effect of noise in the infrared based estimates. A sensitivity test was conducted to identify to determine the contribution of various factors to the inferred soil moisture from ATI and the error that they may introduce in the estimates. The ATI-based approach was then applied to qualitatively describe the spatial distribution of soil moisture. The algorithm was validated over two different test areas in Italy and France where reference measurements are available. For the test site in Italy, the obtained ATI values were clustered around four different values corresponding to different levels of wetness. The determined four classes of soil moisture (low, medium, medium-high, and high) were compared to available in situ observations. An agreement with in situ observations of 81% was obtained. In densely vegetated areas, only three classes of soil moisture were instead distinguishable. The obtained agreement between observed and inferred soil moisture values was 88%. Also, in the second study area in France, where vegetation is more dominant, only three classes of soil moisture were determined with a lower agreement of 73%. In addition, the ATI trends are in agreement with thermal inertia values determined from physics-based formulation. This study showed that a combination of infrared and passive microwave observation may lead to a better mapping of soil moisture at the regional scale.

L'objectif de cette étude est de déduire la variabilité de l'humidité des sols par la combinaison d'observations satellitaires infrarouges et d'ondes micrométriques passives. L'approche proposée repose principalement sur le concept d'inertie thermique apparente (ATI) et utilise le gradient journalier de la température de brillance (BT) provenant de MODIS AQUA pour déduire l'humidité du sol à résolution spatiale modérée. Les extractions de l'humidité des sols par les satellites optique en orbite polaire sont affectées par des discontinuités dues à la présence de nuages et de fausses fluctuations dues à un échantillonnage temporel qui n'est pas suffisant pour un cycle d’échantillonnage journalier fiable. Pour pallier à ces limitations, nous proposons l'utilisation de la tendance temporelle de l'humidité des sols, dérivée d'un produit basé sur les ondes micrométriques, à savoir le produit d'humidité des sols AMSR-E de la NASA, afin de filtrer les estimations des observations de MODIS. Les produits d'humidité des sols basés sur les ondes micrométriques passives montrent moins de fluctuations grâce à leur résolution grossière et leur sensibilité moins grande à l'atmosphère. Il peuvent être alors considérés comme des filtrages fréquentiels passe-bas naturels, réduisant ainsi l'effet du bruit dans les estimations basées sur les infrarouges. Un test de sensibilité a été conduit pour déterminer la contribution de facteurs variés sur l'humidité des sols déduite par l'ATI et l'erreur qu'ils pourraient introduire dans les estimations. L'approche basée sur l'ATI a été alors appliquée pour décrire qualitativement la distribution spatiale de l'humidité des sols. L'algorithme a été validé sur deux zones de test différentes, en Italie et en France, où des mesures étaient disponibles. Pour le site en Italie, les valeurs ATI obtenues étaient regroupées autour de quatre valeurs différentes correspondant à différents niveaux d'humidité. Les quatre classes d'humidité déterminées (basse-moyenne-moyenne/haute-haute) ont été comparées à des observations in-situ disponibles. Une concordance de 81% avec les observations in-situ a été obtenue. Dans les zones à forte végétation, seulement trois classes d'humidité des sols étaient reconnaissables. La concordance entre l'humidité inférée et les valeurs observées est de 88%. Aussi, dans la seconde zone d’étude en France, où la végétation est plus dominante, seulement trois classes d'humidité ont été déterminé avec une concordance moins grande de 73%. De plus, les tendances de l'ATI sont en accord avec les valeurs d'inertie thermique déterminées à partir de formules physiques. Cette étude montre qu'une combinaison d'observation d'ondes infrarouges et micrométriques peut mener à une meilleure cartographie de l'humidité des sols à l’échelle régionale.

DOI

DOI: 10.5589/m12-011

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Journal article2012

Inferring soil moisture variability in the Mediterrean Sea area using infrared and passive microwave observations

Notarnicola C, Caporaso L, Di Giuseppe F, Temimi M, Ventura B, Zebisch M

Bartolomeo Ventura, Claudia Notarnicola, Marc Zebisch

DOI

DOI: 10.5589/m12-011

Bartolomeo Ventura

About

Scientific Publications

Enhancing mountain grassland mapping: A comparative study with PRISMA hyperspectral, multispectral, and SAR data

A Copernicus-based evapotranspiration dataset at 100 m spatial resolution over four Mediterranean basins

Linking hyperspectral PRISMA data with ecosystem functional properties at ICOS sites

Daily evaporation product - Ebro basin (Version v1) [Data set]

Daily evaporation product - Herault basin (Version v1) [Data set]

Daily evaporation product - Medjerda basin (Version v1) [Data set]

Daily evaporation product - Po basin (Version v1) [Data set]

Publishing Eurac Research data on the GEOSS Platform

A Methodology for Heterogeneous Sensor Data Organization and Near Real-Time Data Sharing by Adopting OGC SWE Standards

Organizing Access to Complex Multi‐Dimensional Data: An Example from the ESA SEOM SInCohMap Project

Organizing earth observation data inside a spatial data infrastructure

Inferring soil moisture variability in the Mediterrean Sea area using infrared and passive microwave observations

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Scientific Publications

Enhancing mountain grassland mapping: A comparative study with PRISMA hyperspectral, multispectral, and SAR data

A Copernicus-based evapotranspiration dataset at 100 m spatial resolution over four Mediterranean basins

Linking hyperspectral PRISMA data with ecosystem functional properties at ICOS sites

Daily evaporation product - Ebro basin (Version v1) [Data set]

Daily evaporation product - Herault basin (Version v1) [Data set]

Daily evaporation product - Medjerda basin (Version v1) [Data set]

Daily evaporation product - Po basin (Version v1) [Data set]

Publishing Eurac Research data on the GEOSS Platform

A Methodology for Heterogeneous Sensor Data Organization and Near Real-Time Data Sharing by Adopting OGC SWE Standards

Organizing Access to Complex Multi‐Dimensional Data: An Example from the ESA SEOM SInCohMap Project

Organizing earth observation data inside a spatial data infrastructure

Inferring soil moisture variability in the Mediterrean Sea area using infrared and passive microwave observations