Optical vegetation and soil indices are widely used in Earth observation, although their estimation is strongly affected by cloud coverage and illumination variability. Synthetic-aperture radar (SAR) has therefore attracted increasing interest as an alternative source for spectral index prediction. Most existing studies focus on directly estimating a single index from SAR observations. In this work, we investigate a more flexible formulation in which Sentinel-2 multispectral bands are first reconstructed from Sentinel-1 SAR data and subsequently used to derive multiple spectral indices. Experiments are conducted on the SEN12TP dataset, exploiting near-synchronous paired Sentinel-1 and Sentinel-2 acquisitions together with auxiliary elevation and land-cover information. Three SAR-to-multispectral reconstruction strategies are compared, namely, Efficient-UNet, Pix2Pix, and a conditional flow matching model. The resulting indices are then evaluated against those obtained through dedicated index-specific reconstruction models. The results show that Efficient-UNet achieves the best overall multispectral reconstruction performance among the evaluated architectures. Moreover, indices derived from reconstructed multispectral bands achieve performance comparable to dedicated index-specific models while offering substantially greater flexibility, as multiple indices can be computed within a single framework without retraining task-specific models. At the same time, the experiments highlight important intrinsic limitations of SAR-based spectral reconstruction. Although the reconstructed products preserve the large-scale spatial organization of the scenes, they do not fully recover fine spectral and vegetation-sensitive details. Consequently, SAR-derived spectral indices should be regarded as approximate proxies of optical observations rather than direct substitutes, particularly in applications requiring accurate biophysical interpretation.
Mirko Paolo Barbato, Roberto Cilli, Paolo Napoletano et al.· Remote Sensing· 0 citations
Background: Deep neural networks increasingly power language, vision, and decision systems, yet many deployments require explanations that are faithful, compositional, and governance-ready. Symbolic techniques promise these properties, but the literature mixes post-hoc extraction, knowledge injection, and intrinsically hybrid designs without a unifying view.
Objectives: We provide a systematic review and synthesis of symbolic explainable AI (XAI) for deep learning (January 2017– June 2025), organize the field around a three-part taxonomy—Symbolic Knowledge Extraction (SKE), Symbolic Knowledge Injection (SKI), and Hybrid neurosymbolic architectures—and propose a conceptual framework that clarifies training–inference flows, explanation interfaces, human feedback, and governance touchpoints.
Methods: Beginning from ≈50,000 records, we deduplicated and screened full texts, analyzed 393 PDFs, and included 273 primary studies in the synthesis. We coded each paper for model domain, modality, symbolic formalism, explanation scope and stage, evaluation protocol, and governance alignment. Analyses combine descriptive statistics with stratification by domain and formalism; we qualitatively assess evidence for faithfulness, robustness, data efficiency, and constraint satisfaction.
Results: Research activity accelerates after 2020, with a marked turn toward hybrids. Across the corpus, SKE, SKI, and Hybrid account for approximately 29%, 26%, and 45% of studies, respectively. Rule sets/decision trees remain the dominant explanation artifacts, while logic- and program-based formalisms grow in NLP and planning. SKI most often targets constraint satisfaction and robustness improvements; SKE emphasizes global surrogates and faithfulness auditing; hybrids report gains in sample efficiency and traceable reasoning. However, evaluation practices are heterogeneous, human-subject studies are scarce, and explicit links to policy/risk controls appear in a minority of works.
Conclusions: Our framework unifies how data, priors, and symbolic reasoning interact with neural learners, the explanation interface, human stakeholders, and governance. We distill actionable recommendations: (1) report faithfulness and constraintsatisfaction metrics alongside accuracy; (2) specify symbolic assumptions and training-time injections precisely; (3) include user studies or auditor-centric protocols for high-stakes use; and (4) develop benchmarks that couple tasks with machinereadable knowledge bases. We highlight open problems in scalable formal reasoning with foundation models, verifying generated rationales, and measuring causal faithfulness at scale.
Eduard Ionel Stan, G. Sciavicco, Paolo Napoletano· Journal of Artificial Intell...· 0 citations
