Calibration and Assimilation of Multisensor Satellite Data for Hydrology Estimation
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".
Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 9384
Special Issue Editors
Interests: water cycle; remote sensing hydrology; land surface modeling; global change; water resources
Special Issues, Collections and Topics in MDPI journals
Interests: hydrology; satellite remote sensing; climate change; water resources
2. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing of snow and ice; mocrowave remote sensing; global change
Special Issues, Collections and Topics in MDPI journals
Interests: quantitative retrieval of land surface parameters from remote sensing data; radiative transfer in soil–vegetation–atmosphere systems; process-based modeling and data-driven methods; hydroclimatic extremes
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are undergoing the “golden age” of Earth observations. Over the last several decades, satellites equipped with different types of sensors (e.g., Radar, microwave radiometer and scatterometer, optical imager, laser, gravity) have provided a variety of perspectives for hydrological monitoring. Leveraging these globally available satellite data and numerical modeling, data assimilation has been widely used as an effective tool to facilitate forecast, hindcast, reanalysis, and other applications with spatiotemporally continuous and unbiased simulations. In particular, data assimilation using multisensor satellite data and physically based hydrological or land surface models has emerged to deliver accurate estimates for various hydrological components, including precipitation, soil moisture, evapotranspiration, groundwater, streamflow, snow water equivalent, glacial mass balance, and others. With many new satellites launched (and planned) in the last several years, the unprecedented wealth of new data, together with long-term historical records, opens numerous opportunities for multisensor data assimilation research. Meanwhile, a new challenge for our scientific community has also arisen: How can we maximize the value of satellite data from different sensors and calibrate/assimilate such data for hydrological estimates?
This Special Issue aims to advance the science and technology on multisensor data assimilation for the hydrological cycle. We invite submissions from the perspectives of theoretical, methodological, and applicational advancements. The topics of interest include but are not limited to the data assimilation framework, operational hydrological forecast, hydrological reanalysis, data and model calibration/validation, uncertainty assessments, methodological comparison, data intercomparison, spatiotemporal downscaling, and hybrid machine learning/hydrological modeling.
Prof. Dr. Qiuhong Tang
Dr. Gang Zhao
Dr. Yubao Qiu
Prof. Dr. Jian Peng
Guest Editors
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Keywords
- satellite remote sensing
- data assimilation
- multi-sensor
- hydrological cycle
- numerical modeling
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