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.

Over the Apulian Tavoliere (southern Italy), an activity of ground data collection for the validation of Earth Observation (EO) products is ongoing since 2014. The site is a large agricultural area (about 4000 km2) in the Apulian region (Italy). Over the area, measurements of the main soil and vegetation parameters, relevant for agricultural applications, have been carried out according to international protocols. This article describes the test site, the measurement campaigns and the related research projects. Moreover, examples of the obtained products and services are also given.

Virtual reality has achieved significant popularity in recent years, and allowing users to move freely within an immersive virtual world has become an important factor critical to realize. The user’s interactions are generally designed to increase the perceived realism, but the locomotion techniques and how these affect the user’s task performance still represent an open issue, much discussed in the literature. In this article, we evaluate the efficiency and effectiveness of, and user preferences relating to, freehand locomotion techniques designed for an immersive virtual environment performed through hand gestures tracked by a sensor placed in the egocentric position and experienced through a head-mounted display. Three freehand locomotion techniques have been implemented and compared with each other, and with a baseline technique based on a controller, through qualitative and quantitative measures. An extensive user study conducted with 60 subjects shows that the proposed methods have a performance comparable to the use of the controller, further revealing the users’ preference for decoupling the locomotion in sub-tasks, even if this means renouncing precision and adapting the interaction to the possibilities of the tracker sensor.

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.

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 main objective of this study is to assess the use of Sentinel-1 (S-1) data for surface soil moisture (SSM) retrieval and wheat mapping (WM) at high spatial resolution (e.g. 100–500m), which constitute valuable information for improving crop yield forecast at large scale. A knowledge based classification method and a SSM retrieval algorithm, developed in view of the European Space Agency Sentinel-1 mission, have been applied to a time series of S-1A data collected from October 2014 to April 2015 over a well-documented agricultural site in southern Italy. In particular, observations of SSM content recorded by a network of ground stations deployed in an experimental farm have been used to test the accuracy of the retrieved SSM values. First results indicate an rms error between 5% and 6%. However, the range of observed SSM values is still quite limited and, therefore, longer time series are needed to investigate the retrieval performance over the full range of SSM values.

The objective of this work is to assess the retrieval of above ground dry biomass (ABG) of wheat fields from dual polarized X- and C-band SAR data. A linear regression between the ratio of cross- and co-polarized backscatter (i.e. PQ/PP) and AGB measured during three past (i.e. the TerraSARSIM'03, AgriSAR'06, COSMOLAND'10-11) and one ongoing campaign over the Apulian Tavoliere has been sought and then validated. Results indicate that both at C- and X-band AGB is well correlated with the PQ/PP ratio up to a AGB value of approximately 3 kg/m2; the estimated AGB error is approximately 0.6 kg/m2. Based on the obtained regression functions, AGB maps of the Apulian Tavoliere have been derived from COSMO-SkyMed Ping Pong and Sentinel-1A IW images. The relationship between the observed AGB spatial patterns are in agreement with the spatial distribution of soil fertility and, with some exceptions due to late drought conditions, with wheat grain yield productivity.

A ground network of 12 stations continuously monitoring soil moisture and temperature at various depths has been recently set up over an experimental site of 4km2in the Capitanata plain (Southern Italy). The calibration of the instrumentation is in progress. The long-term high resolution ground observations will be well-suited for SAR-derived soil moisture product validation. Moreover, the ground network will be also associated with hydrologic and agricultural model activities, with the aim of combining land process models with Earth Observation for improving land applications, such as flood/drought and crop yield monitoring and forecast. Indeed, the Capitanata plain is a crucial area in the Mediterranean basin for studying the impact of climate changes and anthropogenic pressure on water availability/demand and wheat production.

This paper reports on the results of an Italian project aimed at investigating the use of X-band COSMO-SkyMed (CSK) SAR data for applications in agriculture and hydrology. Existing classification and retrieval algorithms have been tailored to CSK data and time series of crop, leaf area index and soil moisture maps have been retrieved and assessed through the comparison with in situ data collected over three agricultural sites. In addition, the CSK-derived surface parameters have been integrated into crop growth and hydrologic models and the resulting improvements have been assessed. Results indicate that multi-temporal dual-polarized CSK data are very well-suited for agricultural crop classification and that the integration of maps of SAR-derived surface parameters into crop growth and/or hydrologic models, in general, leads to significant improvements in the model performances.

The objective of this paper is to report on the activities carried out during the first year of the Italian project "Use of COSMO-SkyMed data for LANDcover classification and surface parameters retrieval over agricultural sites" (COSMOLAND), funded by the Italian Space Agency. The project intends to contribute to the COSMO-SkyMed mission objectives in the agriculture and hydrology application domains.

In this study, LAI maps derived from SPOT and MERIS data have been compared. The analysis has been conducted over an agricultural site located in Southern Italy, where temporal series of ground and SPOT and MERIS data have been acquired during the 2006-2008 growing seasons. LAI retrieved from SPOT data has been firstly validated by using in situ measurements. Then, LAI derived from SPOT and MERIS over large fields of wheat, sugar beet and tomato has been compared. Results show that LAI retrieved from MERIS data is underestimated as compared with LAI retrieved from SPOT data. However, for wheat and beet crops, the root mean square error for LAI-MERIS tested over a large number of fields is about 1 m2/m2, whereas is higher for tomato crop.

The purpose of this paper is to assess the use of C-band HH/VV backscatter ratio for mapping winter wheat. This paper analyzes two temporal series of images acquired in 2006 and 2007 by the Advanced Synthetic Aperture Radar (ASAR) system in alternating polarization (AP) mode, over an agricultural site located in southern Italy. Results on test data show that classification accuracies between 75% and 80% can be achieved by using a single ASAR image, acquired during the peak of the wheat-growing season. To achieve accuracies close to 90%, a spatial averaging at field scale is necessary.

The objective of this work is to investigate the synergistic use of leaf area index (LAI) retrieved by ENVISAT ASAR data and crop growth models, such as CERES-Wheat, to improve the accuracy of wheat yield predictions. The estimate reliability of CERES-Wheat strongly depends on the accuracy of its numerous inputs, which are not always available or accurate. As a consequence, the model would largely benefit from using updated information on the wheat status, provided by remote sensing at field scale. This work shows that the assimilation of ENVISAT ASAR AP data into the model lead to significant improvements in the wheat dry biomass and the grain yield model predictions.

Based on recent experimental studies which have found a strong correlation between a multitemporal series of C-band HH/W backscatter ratios acquired at 40deg incidence angle and wheat biomass, this work investigates the effect of the assimilation of the radar retrieved information into CERES-Wheat crop model. A sensitivity analysis has shown that an inaccurate knowledge of some model inputs, concerning soil properties and crop management, can lead to erroneous predictions. However adopting a reinitialisation assimilation strategy, significant improvements in the model estimations have been obtained

This paper investigates the relationship between C-band backscatter measurements and wheat biomass and the underlying soil moisture content. It aims to define strategies for retrieval algorithms with a view to using satellite C-band synthetic aperture radar (SAR) data to monitor wheat growth. The study is based on a ground-based scatterometer experiment conducted on a wheat field at the Matera site in Italy during the 2001 growing season. From March to June 2001, eight C-band scatterometer acquisitions at horizontal-horizontal and vertical-vertical polarization, with incidence angles ranging from 23/spl deg/ to 60/spl deg/, were taken. At the same time, soil moisture, wheat biomass, and canopy structure were collected. The paper describes the experiment and investigates the radar sensitivity to biophysical parameters at different polarizations and incidence angles, and at different wheat phenological stages. Based on the experimental results, the retrieval of wheat biomass and soil moisture content using Advanced Synthetic Aperture Radar data is discussed.