Remote Sensing for Water Storage and Soil Moisture Estimates
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".
Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 2781
Special Issue Editors
2. Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Interests: GRACE; terrestrial water storage; data assimilation; land surface model; satellite remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: data assimilation; remote sensing; soil moisture; land-atmosphere coupling; land surface modeling; hydrological modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The accuracy of terrestrial water storage measurement (comprising, e.g., soil moisture, groundwater, surface water, and canopy interception) is crucial for a sufficient understanding of the terrestrial water cycle and land–atmosphere interaction. Remotely sensed terrestrial water storage (from, e.g., GRACE) and surface soil moisture (from, e.g., ASCAT, SMOS) observations with varied spatial and temporal characteristics have been successfully exploited to improve our ability to assess water resource availability and the climate/anthropogenic influence. The present challenge is the coarse spatiotemporal resolution and uncertainty of the observations. Innovative development, together with new datasets (from, e.g., GRACE-FO, Swarm, SMAP, Sentinel-1), may maximize the observations’ spatial-temporal detail and accuracy. With these ideas in mind, we would like to invite international research communities involved in remotely sensed terrestrial water storage and soil moisture observations to submit their recent developments for publication. The topics of this Special Issue include, but are not limited to, the following:
- Review of remote sensing techniques for terrestrial water storage and soil moisture;
- Applications in water resource assessment, climate variability, and natural hazards;
- Spatiotemporal resolution enhancement and the development or result of the downscaling approach;
- Accuracy assessment, validating remote sensing data against ground measurement or model outputs, including intercomparison between observations;
- Development of data processing techniques, e.g., filtering, retrieval algorithm;
- Univariate or multivariate data assimilation or data fusion of remotely sensed terrestrial water storage or soil moisture observations;
- Applying machine learning techniques for time series reconstruction or spatial resolution enhancement.
Dr. Natthachet Tangdamrongsub
Dr. Jianzhi Dong
Guest Editors
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Keywords
- terrestrial water storage
- soil moisture
- satellite remote sensing
- water resource and climate variation
- spatiotemporal resolution enhancement
- data fusion and data assimilation
- comparison and accuracy assessment
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