Special Issue on Remote Sensing of Snow and Its Applications

Abstract

Snow cover is an essential climate variable directly affecting the Earth’s energy balance. Snow cover has a number of important physical properties that exert an influence on global and regional energy, water, and carbon cycles. Remote sensing provides a good understanding of snow cover and enable snow cover information to be assimilated into hydrological, land surface, meteorological, and climate models for predicting snowmelt runoff, snow water resources, and to warn about snow-related natural hazards. The main objectives of this Special Issue, “Remote Sensing of Snow and Its Applications” in Geosciences are to present a wide range of topics such as (1) remote sensing techniques and methods for snow, (2) modeling, retrieval algorithms, and in-situ measurements of snow parameters, (3) multi-source and multi-sensor remote sensing of snow, (4) remote sensing and model integrated approaches of snow, and (5) applications where remotely sensed snow information is used for weather forecasting, flooding, avalanche, water management, traffic, health and sport, agriculture and forestry, climate scenarios, etc. It is very important to understand (a) differences and similarities, (b) representativeness and applicability, (c) accuracy and sources of error in measuring of snow both in-situ and remote sensing and assimilating snow into hydrological, land surface, meteorological, and climate models. This Special Issue contains nine articles and covers some of the topics we listed above.

A Probabilistic, Parcel‐Level Inundation Prediction Tool for Medium‐Range Flood Forecasting in Large Lake Systems

“…This effort requires that data and models be seamlessly integrated across the international border of the U.S. and Canada (Gronewold et al 2018), as demonstrated in the expansion of the National Water Model across the Great Lakes region (Mason et al 2019). Forecasting efforts could also benefit from the assimilation of state‐of‐the‐art measurements of antecedent conditions (e.g., snowpack, Arslan and Akyürek 2019, soil moisture, Entekhabi et al 2010), as well as additional runoff model intercomparisons (Gaborit et al 2017) and improvements in models of open‐water evapotranspiration (Charusombat et al 2018). Medium‐range water level forecasts would further improve with increased skill in precipitation and temperature forecasts at subseasonal to seasonal lead times (Vitart et al 2017); recent efforts in the Great Lakes region have focused on developing a suite of seasonal forecast tools for this purpose (Bolinger et al 2017).…”

Characterizing Snow Dynamics in Semi-Arid Mountain Regions with Multitemporal Sentinel-1 Imagery: A Case Study in the Sierra Nevada, Spain

“…Traditionally, in situ monitoring has been the principal tool used for observing and, therefore, understanding snow dynamics [16]. However, the intrinsic limitations of accessibility, measurement representativeness, and area coverage have made remote sensing one of the techniques extensively used for snow monitoring [10,11,[17][18][19][20]. Optical sensors have been widely exploited to derive snow cover maps [8,13,18,[20][21][22][23], as well as to compute snow albedo [24][25][26] and grain size [27].…”

“…However, the intrinsic limitations of accessibility, measurement representativeness, and area coverage have made remote sensing one of the techniques extensively used for snow monitoring [10,11,[17][18][19][20]. Optical sensors have been widely exploited to derive snow cover maps [8,13,18,[20][21][22][23], as well as to compute snow albedo [24][25][26] and grain size [27]. The revisiting time of some days in the case of high resolution imagery, and the presence of clouds are the main limitation for these sensors in the case of snow monitoring [28,29].…”