Advancing Hydrometeorological Modeling Through Multi-Source Remote Sensing and Advanced Learning Approaches

Dear Colleagues,

Hydrometeorological modeling is critical for understanding the complicated interactions among the atmosphere, land surface, and water cycle processes. Hydrological extremes, such as droughts and floods, must be accurately monitored and predicted in order to provide appropriate water management, agricultural planning, and climate resilience. With the rapid advancement of remote sensing technologies and machine learning techniques, multi-source Earth observation data now offer unparalleled opportunity to improve model accuracy, spatial coverage, and temporal resolution.

Integrating data from optical, thermal, microwave, and radar sensors with ground-based and reanalysis data improves the calculation of critical variables such as precipitation, soil moisture, evapotranspiration, snow cover, and surface temperature. These findings contribute to the creation of novel hydrometeorological models that combine process-based and data-driven techniques, thereby improving our understanding of land–atmosphere feedback and water resource dynamics in the face of a changing climate.

The purpose of this Special Issue is to compile recent breakthroughs in the application of multi-source remote sensing data to hydrometeorological modeling, monitoring, and forecasting. We welcome contributions that explore novel methodologies, improve model integration, or show practical applications in water resource management, drought and flood risk assessment, and climate adaptation strategies. Topics of interest include, but are not limited to, the following:

• Integrating multi-source satellite, aircraft, and in situ data for hydrometeorological modeling.
• Remote sensing of precipitation, evapotranspiration, and soil moisture.
• Data assimilation and machine learning approaches for hydrological and meteorological forecasting.
• Coupled land–atmosphere modeling and feedback analysis.
• High-resolution drought and flood monitoring systems.
• Validation and uncertainty analysis of satellite-derived hydrological products.
• Applications including water resource management, agriculture, and climate resiliency.

Dr. Abdelhakim Amazirh
Prof. Dr. Salah Er-Raki
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly 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 2400 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.