Remote Sensing for Terrestrial Hydrologic Variables
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing and Geo-Spatial Science".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 593
Special Issue Editors
Interests: remote sensing precipitation; hydrological modeling; error analysis; bias correction; error propagation
Interests: remotely sensed evapotranspiration, irrigation, and ecosystem resilience
Interests: multi-source remote sensing data processing; glacier change
Interests: Soil moisture retrieval; calibriation and fusion of microwave remote sensing
Special Issue Information
Dear Colleagues,
In the context of global warming and the worsening water resource crisis, the accurate monitoring and modeling of terrestrial hydrologic variables have become increasingly crucial. Terrestrial hydrologic variables such as precipitation, evapotranspiration, soil moisture, and cryosphere elements (e.g., glaciers and snow) are fundamental components of the water cycle. However, traditional methods of measuring these variables often require in situ observations, which can be costly, time-consuming, and limited to specific locations. Remote sensing technologies have revolutionized this field by enabling extensive spatial and temporal coverage, providing data that are critical for hydrologic modeling, water resource management, and disaster monitoring.
This Special Issue seeks to showcase innovative research on the development of new remote sensing techniques, the provision of better products, the improvement in hydrologic models, and the application of these advancements to addressing global challenges in hydrology and water resource management. By assembling such cutting-edge research, this issue aims to foster a deeper understanding of terrestrial hydrologic processes and provide new insights into water cycle dynamics.
Submitted articles may address, but are not limited to, the following topics:
- Extreme-event monitoring via remote sensing;
- Remotely sensed evapotranspiration;
- Soil moisture monitoring and downscaling;
- Remote sensing in agricultural water management;
- Glacier mass balance;
- Glacier dynamics;
- Soil freezing and thawing;
- Terrain analysis;
- Remote sensing product assessment;
- The calibration and validation of remote sensing data and the derived products;
- Multi-source data fusion;
- Algorithm development.
Dr. Jianbin Su
Dr. Kun Zhang
Dr. Yushan Zhou
Dr. Zhiqing Peng
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 100 words) can be sent to the Editorial Office for announcement on this website.
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. 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.
Keywords
- precipitation
- soil moisture
- evapotranspiration
- snow and glaciers
- extreme events
- microwave remote sensing
- mass balance
- water budget
