Soil Moisture Retrieval by Integrating TASI-600 Airborne Thermal Data, WorldView 2 Satellite Data and Field Measurements: Petacciato Case Study.

Soil moisture (SM) plays a fundamental role in the terrestrial water cycle and in agriculture, with key applications such as the monitoring of crop growing and hydrogeological management. In this study, a calibration procedure was applied to estimate SM based on the integration of in situ and airborne thermal remote sensing data. To this aim, on April 2018, two airborne campaigns were carried out with the TASI-600 multispectral thermal sensor on the Petacciato (Molise, Italy) area. Simultaneously, soil samples were collected in different agricultural fields of the study area to determine their moisture content and the granulometric composition. A WorldView 2 high-resolution visible-near infrared (VNIR) multispectral satellite image was acquired to calculate the albedo of the study area to be used together with the TASI images for the estimation of the apparent thermal inertia (ATI). Results show a good correlation (R² = 0.62) between the estimated ATI and the SM of the soil samples measured in the laboratory. The proposed methodology has allowed us to obtain a SM map for bare and scarcely vegetated soils in a wide agricultural area in Italy which concerns cyclical hydrogeological instability phenomena.

Sensitivity analysis of the Aquacrop and SAFYE crop models for the assessment of water limited winter wheat yield in regional scale applications.

Process-based models can be usefully employed for the assessment of field and regional-scale impact of drought on crop yields. However, in many instances, especially when they are used at the regional scale, it is necessary to identify the parameters and input variables that most influence the outputs and to assess how their influence varies when climatic and environmental conditions change. In this work, two different crop models, able to represent yield response to water, Aquacrop and SAFYE, were compared, with the aim to quantify their complexity and plasticity through Global Sensitivity Analysis (GSA), using Morris and EFAST (Extended Fourier Amplitude Sensitivity Test) techniques, for moderate to strong water limited climate scenarios. Although the rankings of the sensitivity indices was influenced by the scenarios used, the correlation among the rankings, higher for SAFYE than for Aquacrop, assessed by the top-down correlation coefficient (TDCC), revealed clear patterns. Parameters and input variables related to phenology and to water stress physiological processes were found to be the most influential for Aquacrop. For SAFYE, it was found that the water stress could be inferred indirectly from the processes regulating leaf growth, described in the original SAFY model. SAFYE has a lower complexity and plasticity than Aquacrop, making it more suitable to less data demanding regional scale applications, in case the only objective is the assessment of crop yield and no detailed information is sought on the mechanisms of the stress factors affecting its limitations.

Transport infrastructure surveillance and monitoring by electromagnetic sensing: the ISTIMES project.

The ISTIMES project, funded by the European Commission in the frame of a joint Call "ICT and Security" of the Seventh Framework Programme, is presented and preliminary research results are discussed. The main objective of the ISTIMES project is to design, assess and promote an Information and Communication Technologies (ICT)-based system, exploiting distributed and local sensors, for non-destructive electromagnetic monitoring of critical transport infrastructures. The integration of electromagnetic technologies with new ICT information and telecommunications systems enables remotely controlled monitoring and surveillance and real time data imaging of the critical transport infrastructures. The project exploits different non-invasive imaging technologies based on electromagnetic sensing (optic fiber sensors, Synthetic Aperture Radar satellite platform based, hyperspectral spectroscopy, Infrared thermography, Ground Penetrating Radar-, low-frequency geophysical techniques, Ground based systems for displacement monitoring). In this paper, we show the preliminary results arising from the GPR and infrared thermographic measurements carried out on the Musmeci bridge in Potenza, located in a highly seismic area of the Apennine chain (Southern Italy) and representing one of the test beds of the project.

Optimal spectral domain selection for maximizing archaeological signatures: Italy case studies.

Different landscape elements, including archaeological remains, can be automatically classified when their spectral characteristics are different, but major difficulties occur when extracting and classifying archaeological spectral features, as archaeological remains do not have unique shape or spectral characteristics. The spectral anomaly characteristics due to buried remains depend strongly on vegetation cover and/or soil types, which can make feature extraction more complicated. For crop areas, such as the test sites selected for this study, soil and moisture changes within near-surface archaeological deposits can influence surface vegetation patterns creating spectral anomalies of various kinds. In this context, this paper analyzes the usefulness of hyperspectral imagery, in the 0.4 to 12.8 µm spectral region, to identify the optimal spectral range for archaeological prospection as a function of the dominant land cover. MIVIS airborne hyperspectral imagery acquired in five different archaeological areas located in Italy has been used. Within these archaeological areas, 97 test sites with homogenous land cover and characterized by a statistically significant number of pixels related to the buried remains have been selected. The archaeological detection potential for all MIVIS bands has been assessed by applying a Separability Index on each spectral anomaly-background system of the test sites. A scatterplot analysis of the SI values vs. the dominant land cover fractional abundances, as retrieved by spectral mixture analysis, was performed to derive the optimal spectral ranges maximizing the archaeological detection. This work demonstrates that whenever we know the dominant land cover fractional abundances in archaeological sites, we can a priori select the optimal spectral range to improve the efficiency of archaeological observations performed by remote sensing data.

Road Asphalt Pavements Analyzed by Airborne Thermal Remote Sensing: Preliminary Results of the Venice Highway.

This paper describes a fast procedure for evaluating asphalt pavement surface defects using airborne emissivity data. To develop this procedure, we used airborne multispectral emissivity data covering an urban test area close to Venice (Italy).For this study, we first identify and select the roads' asphalt pavements on Multispectral Infrared Visible Imaging Spectrometer (MIVIS) imagery using a segmentation procedure. Next, since in asphalt pavements the surface defects are strictly related to the decrease of oily components that cause an increase of the abundance of surfacing limestone, the diagnostic absorption emissivity peak at 11.2µm of the limestone was used for retrieving from MIVIS emissivity data the areas exhibiting defects on asphalt pavements surface.The results showed that MIVIS emissivity allows establishing a threshold that points out those asphalt road sites on which a check for a maintenance intervention is required. Therefore, this technique can supply local government authorities an efficient, rapid and repeatable road mapping procedure providing the location of the asphalt pavements to be checked.

Hyperspectral Sensor Data Capability for Retrieving Complex Urban Land Cover in Comparison with Multispectral Data: Venice City Case Study (Italy).

This study aims at comparing the capability of different sensors to detect land cover materials within an historical urban center. The main objective is to evaluate the added value of hyperspectral sensors in mapping a complex urban context. In this study we used: (a) the ALI and Hyperion satellite data, (b) the LANDSAT ETM+ satellite data, (c) MIVIS airborne data and (d) the high spatial resolution IKONOS imagery as reference. The Venice city center shows a complex urban land cover and therefore was chosen for testing the spectral and spatial characteristics of different sensors in mapping the urban tissue. For this purpose, an object-oriented approach and different common classification methods were used. Moreover, spectra of the main anthropogenic surfaces (i.e. roofing and paving materials) were collected during the field campaigns conducted on the study area. They were exploited for applying band-depth and sub-pixel analyses to subsets of Hyperion and MIVIS hyperspectral imagery. The results show that satellite data with a 30m spatial resolution (ALI, LANDSAT ETM+ and HYPERION) are able to identify only the main urban land cover materials.