Advances in Sensor Data Fusion and AI for Environmental Monitoring

Abstract submission deadline

30 July 2026

Manuscript submission deadline

30 September 2026

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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

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  

Open AccessArticle

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 934

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

(This article belongs to the Topic Advances in Sensor Data Fusion and AI for Environmental Monitoring)

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