Understanding Snow Hydrology through Remote Sensing Technologies

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

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.

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.

• snow remote sensing
• SAR/SAR interferometry
• microwave radiometers (AMSR2, SSM/I, …)
• snow cover area
• snow water equivalent
• snow depth
• machine learning