Dear Colleagues, The escalating impacts of climate change have disrupted natural processes, leading to shifts in precipitation and temperature patterns, alterations in water cycles, and perturbations in ecological dynamics. These changes have significant implications for global water resources and terrestrial biodiversity. Remote sensing techniques have emerged as invaluable tools for unraveling the complexities of environmental transformations, allowing for comprehensive evaluations of the multifaceted impacts of climate change on hydrological processes, water resources, and ecosystem structure and functioning. By fostering multidisciplinary discussions and embracing diverse methodological approaches, this Special Issue will offer transformative insights into the dynamic interplay between climate change, water cycle dynamics, and terrestrial ecosystems using remote sensing technologies. Our goal is to pave the way for effective strategies to safeguard our planet's environmental integrity and encourage submissions that address current gaps in the literature or propose novel applications of remote sensing technologies. We invite original research articles, reviews, technical notes, and communications that contribute to the advancement of knowledge in this field. Topics of interest include, but are not limited to, the following: Remote sensing applications in monitoring changes in water cycle components;; Assessment of climate change’s impacts on terrestrial ecosystems using remote sensing data;; Analysis of ecological responses to climate change using remote sensing technology;; Innovative remote sensing methodologies for studying climate change’s effects on hydrology, water resources, and terrestrial ecosystems.; Dr. Chun-Yu DongProf. Dr. Xufeng WangDr. Chang HuangGuest Editors

This Special Issue focuses on the networking and intelligence challenges that underpin scalable low-altitude aerial operations, highlighting the need for AI-driven decision-making, distributed computing, and coordinated multi-UAV systems to support safe and efficient real-world deployment.

Digital technologies have long played a central role in archaeology and cultural heritage, from documentation and visualization to analysis, interpretation, and dissemination. In recent years, however, the rapid maturation of Extended Reality (XR) technologies—encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR)—together with the emergence of immersive virtual environments, has introduced a qualitative shift in how heritage is designed, experienced, and learned. These technologies no longer function solely as representational tools, but rather, they increasingly function as interactive, experiential, and educational systems that actively shape interpretation, engagement, and meaning-making.

Dear Colleagues, Conventional sensing and imaging systems usually resort to capturing data/light reflected from target scenes. Hence, the captured/sensed data heavily depend on the field of view, and occluded regions remain uncaptured. In contrast, the complete three-dimensional (3D) imaging of such scenes may help to capture image data even from behind an obstacle. Advances in sensing and imaging modalities could therefore usher in a new era of high-quality sensing and high-resolution imaging, which would otherwise be unachievable using the conventional systems. The advent of quantum-inspired techniques, in conjunction with intelligent computational approaches, offers exceptional sensing and imaging solutions that can capture image data even in foggy environments or directly inside the human body. These quantum-inspired techniques have also enabled the design and development of high-end single-photon cameras capable of retrieving high-resolution 3D images. Such quantum-inspired sensing and imaging techniques possess immense potential in autonomous vehicles, automated medical diagnosis, and other sustainable applications. Topics of interest include (but are not limited to) the following: Quantum computational imaging techniques;; Quantum Sensors;Quantum chemical sensorsQuantum clocksQuantum gravimetersQuantum imaging sensorsQuantum interferometersQuantum magnetometersQuantum thermometers; Quantum chemical sensors; Quantum clocks; Quantum gravimeters; Quantum imaging sensors; Quantum interferometers; Quantum magnetometers; Quantum thermometers; Intelligent quantum sensing;; Single-pixel imaging;; Quantum-enabled reception, detection, and estimation;; First-photon imaging;; Quantum-inspired multispectral imaging;; Signal processing and data fusion in quantum sensors;; Quantum-enabled IoT sensing.; Quantum chemical sensors; Quantum clocks; Quantum gravimeters; Quantum imaging sensors; Quantum interferometers; Quantum magnetometers; Quantum thermometers; Dr. Siddhartha BhattacharyyaDr. Jan Plato?Dr. Avishek NagGuest Editors

Dear Colleagues, Nanosensor arrays, with their remarkable ability to detect subtle changes in complex mixtures, are revolutionizing multiple fields. In recent years, these arrays have benefited from new technologies, including molecular, bioconjugate, and nanomaterial innovations, which enhance their functionality and application scope. This technological synergy has resulted in a powerful set of tools pivotal for advancing biomedical objectives and improving quality of life. The exceptional sensitivity and specificity of nanosensor arrays enable them to detect and measure minute quantities of substances. In medical diagnostics, these arrays can identify biomarkers indicative of diseases at their earliest stages, such as detecting early signs of cancer or infectious diseases, facilitating early intervention and significantly improving patient outcomes. Their application extends to environmental monitoring, where they play a critical role in detecting low concentrations of pollutants, thereby ensuring environmental safety and compliance with regulatory standards. This Special Issue encompasses a comprehensive range of transductions in sensor design, including optical, electrical, and electrochemical methods, to highlight the versatility of nanosensor arrays. Additionally, the issue aims to address the application of nanosensor arrays in gas sensing, emphasizing their role in identification and quantification of various gas molecules in environmental. The description underscores the physical particularities of sensors at the nanoscale, such as high surface area-to-volume ratio, electrical conductivity, unique magnetic properties, and quantum effects, which can significantly impact the performance and sensitivity of bulk materials used in sensor applications. This Special Issue seeks innovative works on a wide range of research topics, spanning both the fundamental design of sensor arrays and novel applications of nanosensors, including results from industry and academic/industrial collaborations. Topics of interest include, but are not limited to, the following: Design of nanosensor arrays;; Nanosensor arrays in biomedical applications;; Nanosensor arrays in medical diagnoses;; Nanosensor arrays for high-throughput screening;; Nanosensor arrays in microfluidics;; Nanosensor arrays in environmental monitoring;; Nanosensor arrays for cell phenotyping;; Nanosensor arrays in drug discovery.; Dr. Yingying GengDr. Abdelhafed TalebGuest Editors

Dear Colleagues, The Internet of Things (IoT) is transforming both industry and society by connecting various physical objects to the Internet. IoT devices enable data collection through sensing, and can actuate based on instructions generated by AI models. Due to the deep integration of IoT in our daily lives, the cyber security of IoT devices remains a consistent concern. Though many IoT security mechanisms (e.g., intrusion detection, light weight cryptography, authentication and access control) or security guidelines (e.g., the guidelines from NIST) have been proposed, compromises of IoT network might still be inevitable, given the scale and complexity of the IoT network. Moreover, AI models, including large language models (LLMs), are increasingly utilized to analyze IoT data and manage IoT systems. However, AI models have their unique vulnerabilities, and a compromised AI model could issue malicious instructions to disrupt the operations of IoT devices. This Special Issue therefore seeks papers that address IoT security from a more holistic perspective, covering IoT devices and data analysis with AI/LLM models, improving the resilience of IoT systems even with attacked components, and designing mechanisms to facilitate recovery. Potential topics include but are not limited to: Attack tolerant distributed and cryptographic protocols;; Recovering mechanisms for IoT systems from attacks;; Resilience of AI models in IoT systems;; AI/LLM supported mechanism for IoT system robustness;; Trusted execution environment supported protocols for IoT resilience;; Data fusion mechanism to improve the trust of sensor values;; Trusted IoT implementation from LLM-based code generation;; Real world applications of resilient IoT systems.; Dr. Dongxi LiuDr. Nan WangGuest Editors

Dear Colleagues, Following the success of the previous Special Issue “AI-Enabled Sensing Technology and Data Analysis Techniques for Intelligent Human-Computer Interaction (https://www.mdpi.com/journal/sensors/special_issues/HCI_sensing)”, we are pleased to announce the next in the series, entitled “Artificial Intelligence for Sensing, Data Analytics, and Intelligent Human–Computer Interaction”. The concept of user experience (UX) has changed significantly in recent years due to advances in artificial intelligence (AI). Intelligent user interfaces (IUIs), which combine user interfaces (UI) with AI, now offer improved adaptability, usability, and interaction. The rapid growth of smart sensing technologies and Internet of Things (IoT) devices has opened novel possibilities for gathering and analyzing multimodal data about user preferences, interests, and behavior. Innovations in AI, including generative models, multimodal learning, and autonomous agents, now enable intelligent systems to analyze and respond to this data in real time, leading to interfaces that continuously evolve and adapt to individual needs. Such advancements pave the way for highly personalized, natural, and context-aware human–computer interaction (HCI) across diverse domains, from healthcare and education to industry and everyday life. This Special Issue is dedicated to new advances in developing innovative solutions for intelligent HCI and their applications in daily life. The key aim is to bring together state-of-the-art research in sensing, data analysis, and AI-enabled interaction, fostering discoveries, new ideas, and impactful improvements in IUIs. Submissions are welcome in (but not limited to) the following areas: Sensing and Data Analytics AI-enabled multimodal sensing (e.g., gesture, gaze, voice, bio-signals, etc.);; Edge AI and privacy-preserving sensing for HCI;; Sensor data modeling, analysis, and real-time processing pipelines (streaming, cloud-edge synergy);; Internet of Behaviors (IoB) and behavioral pattern mining;; Federated and distributed learning for HCI sensor data;; Adaptive sensing and context-aware data collection;; Digital twins of users for interaction modeling (virtual replicas combining sensor and behavioral data);; Emotion and affective computing from multimodal signals;; Cross-device and cross-environment sensing (seamless user interaction across heterogeneous ecosystems).; Next-Generation and Immersive Intelligent User Interfaces Generative AI for adaptive and personalized IUIs;; Multimodal large models (integrating text, speech, gesture, vision, and physiological data);; Conversational and agent-based interfaces (autonomous and multi-agent systems);; Generative UX (GenUX) and automated interface design;; AI in Extended Reality (AR, VR, MR) environments;; Brain–computer interfaces (BCIs) and neuroadaptive interaction;; AI-driven learning and educational IUIs;; IUIs for accessibility, inclusion, and assistive technologies.; Evaluation, Ethics, and Responsible AI in HCI AI-augmented usability testing and UX evaluation;; Synthetic user modeling, digital twin simulations, and AI-based UX research methods (ethical and longitudinal perspectives);; Human-in-the-loop paradigms for adaptive IUIs;; Explainable and interpretable AI in intelligent interfaces;; Responsible, ethical, and sustainable design of AI-driven IUIs;; Trust, confidence, reliance, and privacy in AI-driven HCI;; Fairness, bias mitigation, and inclusiveness in interaction design;; Longitudinal evaluation of adaptive and evolving interfaces.; Dr. Bo?tjan ?umakDr. Maja Pu?nikGuest Editors

Dear Colleagues, Recent advances in artificial intelligence (AI) and machine learning have revolutionized the field of signal processing, enabling unprecedented capabilities in radar systems and biomedical imaging technologies. This Special Issue focuses on innovative applications of deep learning and machine learning algorithms that enhance signal intelligence and imaging capabilities in these critical domains. We invite contributions that explore the ways in which AI methodologies can address challenges in signal acquisition, processing, and interpretation, specifically within radar systems and biomedical imaging contexts. Of particular interest are approaches that demonstrate improved target detection, classification, and tracking in radar applications, as well as enhanced tissue characterization, anomaly detection, and diagnostic accuracy in biomedical imaging. Dr. Hong TangDr. Hanxiang ZhangDr. Yu ShiGuest Editors

Dear Colleagues, The continuous evolution of micro-electro-mechanical systems (MEMSs) and micro-electro-optical- mechanical systems (MEOMSs) has significantly impacted the field of sensor design, offering unprecedented capabilities in precision sensing, actuation, and control. Within the domain of scanning probe microscopy (SPM), the integration of advanced MEMSs into MEOMSs has brought forth a new era of high-resolution imaging, manipulation, and sensing on the nanoscale level. By leveraging the unique characteristics of MEMSs and MEOMSs, researchers and engineers have been able to develop novel scanning probe technologies that push the boundaries of spatial resolution, sensitivity, and multifunctionality. As a result, SPM techniques, including atomic force microscopy (AFM) and scanning near-field optical microscopy (SNOM), have undergone remarkable enhancements, opening up new avenues for exploring biological systems, understanding material properties, and advancing nanotechnology applications. This Special Issue aims to provide a platform for researchers to share their latest findings, technological advancements, and innovative applications at the intersection of MEMSs/MEOMSs and scanning probe microscopy within the sensor community. Through this collection of contributions, we seek to foster collaboration, inspire new research directions, and propel the ongoing development of MEMS/MEOMS-based sensor technologies for SPM and beyond. Dr. Andrzej SikoraGuest Editor

Dear Colleagues, With the rapid advancement of remote sensing technology, remote sensing data have become an indispensable tool for global and regional atmospheric monitoring. To ensure the accuracy and reliability of remote sensing products, continuous research on data validation, calibration, and innovative applications is essential. This Special Issue aims to gather the latest progress in the field, promote algorithm improvements, uncertainty assessment, and application expansion, thereby providing a more solid data foundation for climate research, environmental monitoring, and weather forecasting. This Special Issue aims to provide a platform for researchers to share the latest achievements in the application, validation, calibration, and related methodological developments using remote sensing data in the field of atmospheric observation. The content must align with the aims and scope of the journal (Remote Sensing), focusing on high-quality research. Suggested themes for submissions: Novel retrieval algorithms for atmospheric components (e.g., aerosols, trace gases, and clouds) from remote sensing data;; Ground-based validation and inter-comparison studies of remote sensing atmospheric products;; On-orbit performance monitoring and calibration techniques for remote sensing sensors;; Atmospheric correction;; Polarimetric remote sensing of the atmosphere;; Applications of multi-source satellite data fusion and assimilation in atmospheric sciences;; Research on climate change and atmospheric environmental processes based on remote sensing data;; Applications of machine learning and artificial intelligence in processing remote sensing atmospheric data;; Data evaluation and application from emerging satellite missions (e.g., hyperspectral, LiDAR).; Dr. Bangyu GeDr. Zhenwei QiuDr. Sifeng ZhuGuest Editors