Cross-Platform Remote Sensing for Enhanced Land Surface Characterization
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".
Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 3872
Special Issue Editors
Interests: satellite remote sensing; CubeSats; UAVs; plant biophysical traits and function; machine-learning; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Interests: surface energy balance modeling; soil moisture retrieval; hydrologic data assimilation and drought monitoring
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Earth observing capacity is continually expanding as a result of conventional large satellite missions, in combination with a growing number of sensor constellations comprised of smaller and comparatively inexpensive satellites. The increasing volume, dimensionality, and capability of remote sensing data provide unprecedented opportunities for advancing retrieval capabilities and overcoming traditional constraints associated with space-borne monitoring. Single platform sensor data is associated with a unique set of compromising attributes with enhanced capability in one domain countered by reduced capability in another. The traditional trade-off between spatial resolution and temporal frequency is a classic example of this conundrum. It follows, that critical requirements regarding radiometric quality and spectral and spatiotemporal resolution often cannot be met based on a single source. Only through complimentary cross-platform methodologies can the full potential of these enhanced observational resources be realized.
The focus of this Special Issue is to promote novel synergistic cross-platform approaches to advance retrieval capabilities and enhance the versatility and robustness of retrieved biophysical properties. We will be accepting submissions within the following broadly-defined topics:
- Multi-sensor integration and data fusion to enhance the spatiotemporal resolution of retrievable surface properties (e.g., spectral vegetation indices, vegetation traits, land surface temperature, soil moisture).
- Adaptation of models and retrieval algorithms to take advance of cross-sensor synergies in sensor observations across the electromagnetic spectrum (e.g., visible to shortwave infrared, sun-induced fluorescence, thermal, microwave) in order to improve land surface monitoring and modeling.
- Combination of observations from multi-scale platforms (e.g., geostationary, polar-orbiting, unmanned aerial vehicles, proximal) to advance retrieval capability and enhance retrieval robustness.
- Advancing the use of machine-learning for analyzing and interpreting multi-sensor data streams.
- Advancing the use of data assimilation to take advantage of multi-sensor and multi-resolution data streams.
Dr. Christopher R. Hain
Guest Editors
Manuscript Submission Information
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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.
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Keywords
- multi-sensor integration
- land surface characterization
- remote sensing
- data fusion
- data assimilation
- machine-learning
- biophysical properties
- cross-sensor synergies
- multi-scale platforms
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