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Petermann Glacier (80.75N, 60.75W) terminates in one of the most extensive remaining ice tongues of the Greenland Ice Sheet. The glacier is grounded 600 meters below sea level on a downsloping bed and could significantly contribute to sea level rise during the 21st century. Recent observations showed an ongoing acceleration in ice flow and kilometric-scale grounding line retreat after decades of stable dynamic conditions. Warming of the ocean waters surrounding Greenland has been indicated as the main driver of this process. However, the melting regime of the glacier at the interface between ocean waters and grounded ice is not well known and needs to be investigated.In this study, we achieve this goal by employing a time series of satellite radar interferometry data available between 2011 and 2022. We document grounding line migration using high-frequency observations from the Italian COSMO-Skymed constellation and the Finnish ICEYE constellation. Furthermore, we use time-tagged digital elevation models from the German TanDEM-X mission to assess the ice shelf basal melt regime in a Lagrangian framework.InSAR observations reveal kilometer-size grounding line migrations - (2-6 km) grounding zones - during tidal cycles, with preferential seawater intrusions of 6 km along pre-existing subglacial channels. In addition, results from the Lagrangian approach indicate that the highest ice shelf melt rates occur at these locations, with values reaching peaks ranging from 60 to 80 meters per year.Such high melt rates concentrated in kilometer-wide grounding zones contrast with the traditional plume model adopted by physical models with zero melt at a fixed grounding line. Their inclusion in physical models will increase the glacier's sensitivity to ocean warming and double the projections of sea level rise from the glacier.This work was supported by a grant from NASA.

Petermann Gletscher drains 4% of the Greenland Ice Sheet that contains an estimated volume of ice equivalent to a 0.5 m global sea-level rise. It terminates in the longest floating ice shelves in the Northern Hemisphere. A significant portion of the glacier’s drainage basin is grounded below sea level on a downsloping bed, hence prone to rapid retreat if the glacier was pushed out of equilibrium by climate warming. Previous studies documented near-zero mass balance and a steady grounding line position during the last three decades. However, more recent observations revealed the transition to a new phase characterized by rapid grounding line retreat and accelerated ice flow after 2016. Increased basal melt due to warming ocean temperatures has been identified as the physical mechanism driving the retreat process. Nonetheless, a comprehensive evaluation of the magnitude and spatial variability of basal melt has not been performed yet. Its contribution to the ice mass loss remains, for this reason, poorly constrained.In this study, we achieve this goal by employing high-resolution digital elevation models acquired by the German Aerospace Centre (DLR) TanDEM-X mission between 2011 and 2021. We derive basal melt estimates from ice elevation changes computed in a Lagrangian framework. The extended temporal coverage provided by TanDEM-X data allows mapping changes in basal malt over different temporal scales with unprecedented resolution and highlights increased melt rates during the second part of the observation period. The melt rate spatial distribution is consistent with the recent inland migration of the grounding line with peak values above 60 meters per year measured along with the western, central, and eastern sectors of the grounding zone.

The Petermann ice shelf is one of the largest in Greenland, buttressing 4 % of the total ice sheet discharge, and is considered dynamically stable. In this study, we use differential synthetic aperture radar interferometry to reconstruct the grounding line migration between 1992 and 2021. Over the last thirty years, we find that the grounding line of Petermann retreated 4 km, 7.5 km and 4.5 km in the western, central and eastern sectors, respectively. However, it is only since 2017 that the glacier has undergone a significant retreat in its central section, receding more than 5 km along a retrograde bed grounded 500 m below sea level. Simultaneously, two large fractures developed, splitting the ice shelf in three sections, with partially decoupled flow regime. The retreat followed the warming of the ocean waters by 0.4 °C in Nares Strait. As a result, the glacier sped up by 15 % in 2016–2018. While the central sector stabilized on a sill, the eastern flank is sitting on top of a down-slopping bed, which might accentuate the glacier retreat in the coming years.

This study presents a preliminary assessment of the potentialities of the COSMO-SkyMed® (CSK®) satellite constellation to accurately classify different crops. The experiment is focused on the main crops grown in the agricultural region of Marchfeld (Austria) namely carrot, corn, potato, soybean and sugar beet. A Support Vector Machine (SVM) classifier was fed with temporally dense series of backscattering coefficients extracted from a stack of CSK® GTC products. In particular, twenty one CSK® dual polarization (11 HH, 10 VH) images were acquired over the site for the growing season (early April – mid October) in Stripmap Himage mode, with a nominal incidence angle at scene center of 40°. A comparison of the classifications obtained at the two different polarizations are reported and the result are analyzed in terms of the achieved accuracies. The SVM method was able to classify all five crop types with an overall accuracy of 81.6% (Kappa 0.77) at VH polarization and of 84.5% (Kappa 0.80) at HH polarization. Sugar beet, potato and carrot were accurately identified with OA never less than 83% at both polarizations, whereas corn and soybean showed remarkably differences in terms of producer’s and user’s accuracies, probably due to particular agricultural practices adopted for these two crop species. These first results show that the CSK® capability of acquiring temporally dense data sets can accurately identify several crop types.

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 commercial market offers several software packages for the registration of remotely sensed data through standard one-to-one image matching. Although very rapid and simple, this strategy does not take into consideration all the interconnections among the images of a multi-temporal data set. This paper presents a new scientific software, called Satellite Automatic Multi-Image Registration (SAMIR), able to extend the traditional registration approach towards multi-image global processing. Tests carried out with high-resolution optical (IKONOS) and high-resolution radar (COSMO-SkyMed) data showed that SAMIR can improve the registration phase with a more rigorous and robust workflow without initial approximations, user’s interaction or limitation in spatial/spectral data size. The validation highlighted a sub-pixel accuracy in image co-registration for the considered imaging technologies, including optical and radar imagery.

Land-cover/land-use thematic maps are a major need in urban and country planning. This paper demonstrates the capabilities of Object Based Image Analysis in multi-scale thematic classification of a complex sub-urban landscape with simultaneous presence of agricultural, residential and industrial areas using pan-sharpened very high resolution satellite imagery. The classification process was carried out step by step through the creation of different hierarchical segmentation levels and exploiting spectral, geometric and relational features. The framework returned a detailed land-cover/land-use map with a Cohen's kappa coefficient of 0.84 and an overall accuracy of 85%.

Spatial and temporal information of soil water content is of essential importance for modelling of land surface processes in hydrological studies and applications for operative systems of irrigation management. In the last decades, several remote sensing domains have been considered in the context of soil water content monitoring, ranging from active and passive microwave to optical and thermal spectral bands. In the framework of an experimental campaign in Southern Italy in 2007, two innovative methodologies to retrieve soil water content information from airborne earth observation (E.O.) data were exploited: a) analyses of the dependence of surface temperature of vegetation with soil water content using thermal infrared radiometer (TIR), and b) estimation of superficial soil moisture content using reflectance in the visible and near infrared regions acquired from optical sensors. The first method (a) is applicable especially at surfaces completely covered with vegetation, whereas the second method is preferably applicable at surfaces without or with sparse vegetation. The synergy of both methods allows the establishment of maps of spatially distributed soil water content. Results of the analyses are presented and discussed, in particular in view of an operative context in irrigation studies.

The ASI-SRV (Sistema Rischio Vulcanico) project is devoted to the development of an integrated system based on EO and Non EO data to respond to specific needs of the Italian Civil Protection Department (DPC). ASI-SRV provides the capability to import many different EO and Non EO data into the system, it maintains a repository where the acquired data have to be stored and generates selected products which will be functional to the different volcanic activity phases. The processing modules for Radar and EO Optical sensors data allow to estimate a number of parameters which include: surface thermal proprieties, gas, aerosol and ash emissions and to characterize the volcanic products in terms of composition and geometry, surface deformations in terms of displacements and velocity. All the generated products are related to Italian actives volcanoes and three test sites have been chosen to demonstrate the capability of this integrated system: Vesuvio, Campi Flegrei (Campania region) and Etna (Sicilia region). In this paper the first results obtained by means of modules developed within the ASI-SRV project and dedicated to the processing of EO historical series are presented.

Earth observation (EO) optical data represent one of the main sources of information in the retrieval of land surface parameters (i.e., leaf area index and surface albedo). These parameters are widely used in research and applications in agriculture for improving water and land resources management, especially in the field of precision farming, to monitor crop status, predict crop yield, detect disease and insect infestations, and support the management of farming tasks. During recent years, the technical capabilities of airborne and satellite remote sensing imagery have been improved to include hyperspectral and multiangular observations. In parallel with the advancement of observation techniques, there has been an important development in the study of the interaction of solar radiation with Earth’s surface. This process can be described by using canopy reflectance models of different complexity, which can also be used in operative applications in the field of agricultural water and land management. As such, enhanced EO data and canopy reflectance models can be combined together to reduce the empiricism of traditional methods based on simplified approaches and to increase the estimation accuracy.

The use of Earth Observation (EO) data to retrieve biophysical variables of vegetated surfaces has proved to be useful in many operative tools to gather information repetitively, at spatial and temporal resolution, for agricultural and water management applications. The launch of the European Space Agency (ESA) Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA) mission has provided an opportunity to study a multiangular and hyperspectral dataset of images with high spatial resolution. The objective of the study was to use the CHRIS/PROBA data, in both directional and spectral domains, to estimate the Leaf Area Index (LAI). For this purpose, inversion of a canopy reflectance model was performed against CHRIS data. LAI estimates were validated by using ground truth LAI measurements and compared, in terms of accuracy, to a semi-empirical approach. It was shown that, for a given spectral configuration, the directional information always improved the LAI estimation. For the best case (corn), this was achieved with an LAI root mean square error (RMSE) of 0.41 by using five angles and 62 spectral bands compared to a value of 1.42 by using one angle and four bands, as in the Landsat Thematic Mapper (TM) configuration.

Earth Observation (E.O.) technologies provide a valuable data base for the monitoring of crop and soil characteristics on a large scale, in a rapid, accurate and cost-effective way. The present work aims at evaluating different methods and models for the estimation of the Leaf Area Index (LAI) by means of hyperspectral data acquired by the optical airborne instrument CASI during the ESA AgriSAR 2006 campaign. Inversion of a physical model using an iterative optimization technique (SQP) and a fast look-up-table (LUT) approach is performed and results are compared with an empirical model based on the relationship between LAI and WDVI. Furthermore, the analyses carried out on the inversion of the physical models provide the opportunity to test the spectral bands proposed for the upcoming E.O. satellite Sentinel-2 developed by ESA in the framework of GMES (Global Monitoring for Environment and Security). The Sentinel-2 spectral sampling is compared with the one proposed by an independent study determining the wavebands best characterizing vegetation and crops. Accuracy of LAI estimation, evaluated with the AgriSAR 2006 field measurements, is discussed in the context of operational agricultural monitoring.

Leaf Area Index (LAI) is a key parameter for many biophysical and climatic models. In the field of interest of our research group, an accurate LAI estimation is needed for modelling crop water requirements for precision farming and agricultural resource management applications. The objective of this study is to assess the accuracy of LAI retrieval from EO data by means of a radiative transfer model inversion technique. To this aim multi‐angular CHRIS/PROBA data, from SPARC 2003 and 2004 campaigns, has been employed in the inversion of PROSPECT‐SAILH (P‐SH) model by using a numerical optimisation technique based on the Marquardt‐Levenberg (M‐L) algorithm. From the same data set, the closer to nadir reflectance in the red and near‐infrared bands has been selected in order to estimate LAI by using an empirical approach based on the CLAIR model. Such estimated LAI has been thus employed as prior information in the P‐SH model. LAI values retrieved with this combined approach have been estimated with good accuracy for some type of crops (e.g. R2 = 0.80, RMSE=0.51 m2m−2 for Alfalfa canopies). Ongoing and future work includes further improvements of the M‐L optimisation method and the implementation of a different optimisation method based on Genetic Algorithm GA.