Multi-Source Remote Sensing Data Fusion for Hydrological Modelling

Dear Colleagues,

Remote sensing has becoming increasingly important in enhancing hydrological modelling, providing diverse large-scale geospatial information that can be used as model inputs, parameter estimates, or proxy hydrological observations for model calibration and validation. Integrating multisource observations from satellites, UAVs, radar systems, and ground-based measurements shows the strength to improve the understanding of hydrological processes and enhance model performance from local to regional and global scales. Recent developments in data assimilation, machine learning, artificial intelligence, and cloud computing further accelerate the application of remote sensing fusion in hydrology. Nevertheless, significant challenges remain in the effective fusion of multiple remote sensing datasets. In particular, inconsistencies and potential conflicts among datasets may arise from uncertainties associated with sensors and retrieval algorithms, as well as mismatches between the physical meanings of remotely sensed variables and their representations within hydrological models. Next-generation hydrological modelling frameworks incorporating novel data fusion methods are expected to provide more accurate and reliable hydrological predictions of developing adaptative strategies to manage water stresses or crises under a changing environment.

This Special Issue aims to bring together inspiring concepts, innovative methodologies, advanced tools, and practical applications of multiple remote sensing fusion for hydrological modelling. In particular, the Special Issue encourages studies that integrate multisource remote sensing products with hydrological, hydraulic, ecohydrological, and hydrogeological models to enhance process understanding, improve model representation, and strengthen predictive performance across different spatial and temporal scales. The topic aligns closely with the scope of Remote Sensing by focusing on the development and application of remote sensing technologies, geospatial analysis, environmental monitoring, and Earth system modelling. It supports interdisciplinary research bridging hydrology, remote sensing, artificial intelligence, and water management.

Suggested themes and article types for submissions.

• Novel multisource remote sensing data fusion approaches for generating consistent and reliable hydrological model inputs, parameters and outputs;
• Next-generation hydrological modelling frameworks seamlessly integrating multisource remote sensing datasets;
• Application, evaluation, and benchmarking of multisource remote sensing products for improving hydrological simulation and prediction;
• Data assimilation, uncertainty quantification, and error propagation analysis using multisource remote sensing datasets in hydrological modelling;
• Machine learning, deep learning, and hybrid AI–physics approaches for hydrological modelling driven by multisource remote sensing data;
• Multiscale hydrological prediction using integrated satellite, UAV, radar, and ground-based observations;
• Remote sensing fusion for flood inundation forecasting, drought monitoring and water resources assessment;
• Review and synthesis studies on recent advances, challenges, and future directions in multisource remote sensing for hydrological modelling and prediction.

The Special Issue welcomes original research articles, review papers, methodological developments, technical notes, case studies, and interdisciplinary applications related to remote sensing, hydrological modelling, and water resources assessment.

Dr. Hongxing Zheng
Dr. Guobin Fu
Dr. Ruirui Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• hydrological modelling
• multisource remote sensing
• data fusion
• machine learning and deep learning
• data assimilation
• uncertainty
• flood inundation
• drought