Innovative Sensors and Data Intelligence to Explore the Ecosystem Nexus: Celebrating the 25th Anniversary of Sensors
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 696
Special Issue Editors
Interests: hydrological processes; environmental monitoring; wireless sensor networks; geographical information systems; soil sensors; cosmic-ray neutron sensing; gamma-ray sensing; water quality sensors
Special Issues, Collections and Topics in MDPI journals
2. Dayekou Hydrological Process Observation and Research Station, Lanzhou University, Lanzhou 730000, China
Interests: development of hydrological process monitoring methods; ecohydrological processes; multi-scale hydrological processes; soil hydrological processes; hydrological model development; the intersection of artificial intelligence and hydrology
Special Issues, Collections and Topics in MDPI journals
Interests: hydraulic engineering; soil physics; eco-hydrological models; hydrological monitoring
Special Issues, Collections and Topics in MDPI journals
Interests: environmental sensor systems and sensor networks (in situ/mobile/multi-parameter); sensor deployment and calibration; sensor metadata and FAIR sensor infrastructures; sensor-near/edge data processing; automated QA/QC and uncertainty quantification; time-series analytics; ML/AI for drift correction, anomaly detection and gap filling; data integration and sensor fusion for ecosystem monitoring and decision-support
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Rapid environmental change driven by climate variability, land use change, pollution, and biodiversity loss has increased the need for robust, high-resolution, and scalable ecosystem monitoring. Advances in sensor technology over recent decades have fundamentally improved our ability to observe complex processes in terrestrial, aquatic, and atmospheric systems. In situ observation systems now enable continuous measurement of key environmental variables such as soil moisture, snow cover, groundwater dynamics, vegetation productivity, surface water quality, atmospheric fluxes, and pollutant concentrations. These observations are essential for understanding ecosystem functioning, detecting early signs of degradation, supporting sustainable resource management, and informing environmental policy.
Equally important is the transformation of raw sensor data into reliable, analysis-ready products. Sensor-near (edge) preprocessing, automated quality assurance/quality control (QA/QC), and modern machine learning and AI methods for calibration, drift correction, gap filling, and anomaly detection increasingly determine whether sensor observations become actionable information. Recent developments—including low-cost, non-invasive, and energy-efficient sensors, IoT-based monitoring systems, and advanced electromagnetic, acoustic, optical, and chemical sensors—have expanded spatial and temporal coverage while improving data quality and accessibility. In parallel, advances in embedded computing, standardized metadata, and scalable processing pipelines enable near-real-time data validation, harmonization, and fusion across heterogeneous sensor networks.
Despite this progress, challenges remain in sensor calibration, data integration across scales, uncertainty quantification, and long-term deployment in harsh or remote environments. Addressing these challenges requires interdisciplinary collaboration among sensor developers, environmental scientists, data analysts, and modelers.
This Special Issue presents recent advances in environmental monitoring systems for ecosystem applications, with a focus on measuring, integrating, and interpreting key environmental variables at the land–atmosphere–water interfaces, as well as robust sensor data processing and analytics (QA/QC, uncertainty quantification, and AI/ML-based methods). We welcome original research and review articles on novel sensor technologies, data fusion and AI approaches, and real-world applications that support ecosystem assessment, process understanding, and decision-making, including both in situ and remote sensing observations and their integration into modeling frameworks.
Topics of interest include, but are not limited to, the following:
- Sensors and sensor networks for soil moisture, snow cover, and groundwater monitoring;
- Measurement of vegetation dynamics, species composition, sap flow, and ecosystem productivity;
- Atmospheric sensing of energy, water, and matter fluxes (e.g., heat, humidity, greenhouse gases, air pollutants);
- Surface water monitoring, including water quality, river discharge, nutrient and pollutant fluxes;
- Sensor deployment strategies, calibration, validation, and uncertainty analysis;
- Applications of environmental sensing in ecosystem management, climate adaptation, and sustainability;
- Applications for sensor data fusion;
- Sensor-near (edge) data processing: filtering, compression, feature extraction, event detection, and on-device intelligence;
- Automated QA/QC for environmental sensor networks (plausibility checks, cross-sensor consistency, fault detection, and flagging strategies);
- ML/AI methods for calibration and drift correction, gap filling, and anomaly detection in environmental time series;
- Uncertainty quantification and propagation from sensors to derived ecosystem indicators.
Dr. Heye Bogena
Prof. Dr. Jie Tian
Dr. Paolo Nasta
Dr. Jan Bumberger
Guest Editors
Manuscript Submission Information
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Keywords
- environmental sensing
- ecosystem monitoring
- innovative environmental sensors
- low-cost environmental sensors
- sensor calibration and testing
- sensor networks
- groundwater–soil–landsurface–atmosphere interactions
- data fusion
- QA/QC
- edge computing/edge analytics
- machine learning/AI
- time-series analytics
- uncertainty quantification
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