Understanding Snow Hydrology Through Remote Sensing Technologies
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 463
Special Issue Editors
Interests: microwave remote sensing; soil moisture; vegetation biomass; snow water equivalent; SAR and microwave radiometers; GNSS-R, retrieval algorithms development; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: microwave remote sensing; soil moisture; vegetation biomass; snow water equivalent; SAR and microwave radiometers; GNSS-R, retrieval algorithms development
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With a maximum average areal extent of ≃47 million km2, which is mostly located in the boreal hemisphere, snow is the second most important component of the Cryosphere. Snow plays a fundamental role in the hydrological cycle, and it represents an important source of terrestrial water. The need for accurate and spatially distributed measurements of the properties of snow is therefore pivotal for many hydrological applications, especially in this era of climate change. Owing to the significant progress made in remote sensing techniques and the number of satellite missions launched in recent years, remote sensing is offering ever-increasing potential for the monitoring of snow properties at a high spatial and temporal resolution. Now, the mapping of snow-covered areas (SCA) is operationally achieved at different resolutions by multispectral sensors, while microwave satellite radiometers, owing to their penetration capabilities, all-weather observations, and night-time viewing, can provide information about snow depth and snow water equivalent (SD/SWE) at a global scale and almost daily, although their potential is limited by their spatial resolution in the order of dozens of km. Recent studies have also highlighted the capabilities of SAR (especially at X band) in mapping SD/SWE with good accuracy at the basin scale and with relatively high resolution (≃10 m).
Some issues remain, however; these include the possibility of mapping SD/SWE in wet snow conditions using SAR/microwave radiometers; the acquisition of snow grain size information, which is critical for enhancing the accuracy of SD/SWE estimations; improvements in the spatial resolution of passive microwave observations from space; and the correction of weather effects in multispectral images in the mapping of SCA.
This Special Issue, entitled “Understanding Snow Hydrology through Remote Sensing Technologies”, aims to provide an overview of the most recent advances in the remote sensing of snow properties in support of hydrological applications. Contributions that present both applications and processing techniques involving microwave and multispectral sensors alone or in combination are welcome; this is in order to take advantage of the complementarity of various techniques. Attention will be paid to model-based approaches and machine learning (ML), which is gaining ever-increasing popularity regarding this type of application.
Dr. Emanuele Santi
Dr. Simone Pettinato
Guest Editors
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Keywords
- snow remote sensing
- SAR/SAR interferometry
- microwave radiometers (AMSR2, SSM/I, …)
- snow cover area
- snow water equivalent
- snow depth
- machine learning
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