Topic Editors

Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Prof. Dr. Yu Li
School of Geomatics, Liaoning Technical University, Fuxin 123000, China
Faculty of Geography, University of Belgrade, Studentski Trg 3/3, 11000 Belgrade, Serbia

Advances in Sensor Data Fusion and AI for Environmental Monitoring

Abstract submission deadline
30 July 2026
Manuscript submission deadline
30 September 2026
Viewed by
1439

Topic Information

Dear Colleagues,

Environmental monitoring increasingly demands high-resolution, real-time, and reliable information to guide sustainability, disaster response, and ecosystem management. Advances in sensor technologies—including satellites, airborne platforms, in situ stations, and Internet of Things (IoT) devices—have enabled the collection of vast amounts of heterogeneous data (e.g., spectral, structural, chemical, and meteorological observations). However, transforming these multimodal streams into actionable insights remains challenging, necessitating effective data fusion strategies and robust artificial intelligence frameworks. This Topic emphasizes research at the intersection of sensor data fusion and AI-driven analytics, aiming to highlight innovations that integrate multi-source data for accurate environmental assessment and predictive modeling.

Contributions are encouraged in areas such as:

(1) Novel fusion algorithms for heterogeneous sensor integration, especially those improving spatial and temporal resolution;

(2) Deep learning models tailored to fused data for applications such as land-cover change, air and water quality, forest health, and disaster prediction;

(3) Scalable and efficient architectures—e.g., edge-to-cloud systems or federated learning—for near‑real‑time monitoring;

(4) Case studies demonstrating improved decision-making in forestry, agriculture, urban planning, or conservation. Ultimately, this Topic seeks to showcase multidisciplinary approaches that leverage sensor fusion and AI to advance environmental science and inform sustainable resource management.

Dr. Zhenyu Yu
Prof. Dr. Mohd Yamani Idna Idris
Prof. Dr. Yu Li
Dr. Aleksandar Dj Valjarević
Topic Editors

Keywords

  • artificial intelligence (ai)
  • sensor data fusion
  • remote sensing
  • forest resource assessment
  • environmental monitoring
  • smart agriculture
  • machine learning
  • multisource data integration
  • ecological modeling
  • sustainable management

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Geosciences
geosciences
2.1 5.1 2011 23.4 Days CHF 1800 Submit
ISPRS International Journal of Geo-Information
ijgi
2.8 7.2 2012 34.2 Days CHF 1900 Submit
Remote Sensing
remotesensing
4.1 8.6 2009 24.9 Days CHF 2700 Submit
Sensors
sensors
3.5 8.2 2001 19.7 Days CHF 2600 Submit
Data
data
2.0 5.0 2016 25.2 Days CHF 1600 Submit

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Published Papers (1 paper)

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27 pages, 16753 KB  
Article
A 1°-Resolution Global Ionospheric TEC Modeling Method Based on a Dual-Branch Input Convolutional Neural Network
by Nian Liu, Yibin Yao and Liang Zhang
Remote Sens. 2025, 17(17), 3095; https://doi.org/10.3390/rs17173095 - 5 Sep 2025
Viewed by 928
Abstract
Total Electron Content (TEC) is a fundamental parameter characterizing the electron density distribution in the ionosphere. Traditional global TEC modeling approaches predominantly rely on mathematical methods (such as spherical harmonic function fitting), often resulting in models suffering from excessive smoothing and low accuracy. [...] Read more.
Total Electron Content (TEC) is a fundamental parameter characterizing the electron density distribution in the ionosphere. Traditional global TEC modeling approaches predominantly rely on mathematical methods (such as spherical harmonic function fitting), often resulting in models suffering from excessive smoothing and low accuracy. While the 1° high-resolution global TEC model released by MIT offers improved temporal-spatial resolution, it exhibits regions of data gaps. Existing ionospheric image completion methods frequently employ Generative Adversarial Networks (GANs), which suffer from drawbacks such as complex model structures and lengthy training times. We propose a novel high-resolution global ionospheric TEC modeling method based on a Dual-Branch Convolutional Neural Network (DB-CNN) designed for the completion and restoration of incomplete 1°-resolution ionospheric TEC images. The novel model utilizes a dual-branch input structure: the background field, generated using the International Reference Ionosphere (IRI) model TEC maps, and the observation field, consisting of global incomplete TEC maps coupled with their corresponding mask maps. An asymmetric dual-branch parallel encoder, feature fusion, and residual decoder framework enables precise reconstruction of missing regions, ultimately generating a complete global ionospheric TEC map. Experimental results demonstrate that the model achieves Root Mean Square Errors (RMSE) of 0.30 TECU and 1.65 TECU in the observed and unobserved regions, respectively, in simulated data experiments. For measured experiments, the RMSE values are 1.39 TECU and 1.93 TECU in the observed and unobserved regions. Validation results utilizing Jason-3 altimeter-measured VTEC demonstrate that the model achieves stable reconstruction performance across all four seasons and various time periods. In key-day comparisons, its STD and RMSE consistently outperform those of the CODE global ionospheric model (GIM). Furthermore, a long-term evaluation from 2021 to 2024 reveals that, compared to the CODE model, the DB-CNN achieves average reductions of 38.2% in STD and 23.5% in RMSE. This study provides a novel dual-branch input convolutional neural network-based method for constructing 1°-resolution global ionospheric products, offering significant application value for enhancing GNSS positioning accuracy and space weather monitoring capabilities. Full article
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