We study the pairwise entanglement close to separable ground states of a class of one-dimensional quantum spin models. At $T=0$ we find that such ground states separate regions, in the space of the Hamiltonian parameters, which are characterized by qualitatively different types of entanglement, namely parallel and antiparallel entanglement; we further demonstrate that the range of the concurrence diverges while approaching separable ground states, therefore evidencing that such states, with uncorrelated fluctuations, are reached by a long range reshuffling of the entanglement. We generalize our results to the analysis of quantum phase transitions occurring in bosonic and fermionic systems. Finally, the effects of finite temperature are considered: At $T>0$ we evidence the existence of a region where no pairwise entanglement survives, so that entanglement, if present, is genuinely multipartite.

We study the two-spin entanglement distribution along the infinite S = 1/2 chain described by the XY model in a transverse field; closed analytical expressions are derived for the one-tangle and the concurrences Cr, with r being the distance between the two possibly entangled spins, for the values of the Hamiltonian parameters close to those corresponding to factorized ground states. The total amount of the entanglement, the fraction of such entanglement which is stored in pairwise entanglement and the way such fraction distributes along the chain are discussed, with attention focused on the dependence on the anisotropy of the exchange interaction. Near factorization a characteristic length scale naturally emerges in the system, which is specifically related with the entanglement properties and diverges at the critical point of the fully isotropic model. In general, we find that anisotropy rules a complex behavior of the entanglement properties, which results in the fact that more isotropic models, despite being characterized by a larger amount of the total entanglement, present a smaller fraction of the pairwise entanglement: the latter, in turn, is more evenly distributed along the chain, to the extent that, in the fully isotropic model at the critical field, the concurrences do not depend on r.

Early-season crop mapping provides decision-makers with timely information on crop types and conditions that are crucial for agricultural management. Current satellite-based mapping solutions mainly rely on optical imagery, albeit limited by weather conditions. Very few exploit long-time series of polarized synthetic aperture radar (SAR) imagery. To address this gap, we assessed the performance of COSMO-SkyMed X -band dual-polarized (HH, VV) data in a test area in Ponte a Elsa (central Italy) in January–September 2020 and 2021. A deep learning convolutional neural network (CNN) classifier arranged with two different architectures (1-D and 3-D) was trained and used to recognize ten classes. Validation was undertaken with in situ measurements from regular field campaigns carried out during satellite overpasses over more than 100 plots each year. The 3-D classifier structure and the combination of HH+VV backscatter provide the best classification accuracy, especially during the first months of each year, i.e., 80% already in April 2020 and in May 2021. Overall accuracy above 90% is always marked from June using the 3-D classifier with HH, VV, and HH+VV backscatter. These experiments showcase the value of the developed SAR-based early-season crop mapping approach. The influence of vegetation phenology, structure, density, biomass, and turgor on the CNN classifier using X -band data requires further investigations, along with the relatively low producer accuracy marked by vineyard and uncultivated fields.