Abstract
Land Surface Temperature (LST) is a parameter related to multiple Earth surface processes. For example, some of the most well-known applications are used on vegetation studies aiming to understand the role of LST in the evapotranspiration process; or on studies related with surface temperatures of the oceans (Sea Surface Temperature – SST) trying to comprehend the energetic exchanges between oceans and atmosphere and its impact on the climates. This parameter is often analyzed through ground data but can be also observed by means of remote sensing data. The LST monitoring by remote sensing data is also of great importance in the cryosphere field, as it allows us to better understand the energy exchanges between the atmosphere and the snow/glaciated surfaces of Earth on a larger scale. Snow surfaces have a relevant role in the global energy balance of the planet since due to its bright color it reflects a large extent of the incident radiation to the atmosphere, thus avoiding the fast melting of the mountain glaciers, seasonal snow, polar ice caps and sea ice.
Snow processes and metamorphosis are very sensitive to air temperature changes. A variation from 0° to 1° C can trigger the beginning of the snow melt. During this melting process snowflakes undertake changes in grain size and shape and thus the capacity for reflecting the incident radiation, which means it changes the albedo. In this sense, studying the relation between LST and snow grain size help us to better understand in which way the variation of these two parameters is correlated.
As many studies in the past have demonstrated, snow albedo is a very relevant parameter for many earth processes, as Earth energy balance. At the hydrological basin level, it can influence the conditions and timing on which snow releases fresh liquid water during melting season. Thus, its accurate knowledge is extremely important to better understand many subsystems depending on the seasonal snow cycle as the vegetation, fauna but also many economic sectors such as hydropower and agriculture.
Within the frame of the ESA Alpine Regional Initiative project AlpSnow (2020-2022), we aim at developing snow albedo and snow grain size retrieval methods using two different approaches proposed by Painter et al. (2009) and Kokhanovsky et al. (2019). The first method is an empirical approach based on spectral indices, and the second method is a physical approach. Both approaches are applied to Sentinel-3 OLCI satellite data. To test both algorithms, a short timeseries has been analyzed from the beginning of the 2018 hydrological year until the melting season of 2021. For grain size, the results from the comparison between ground data and satellite estimates indicate a high representativeness of the class with low grain size values. This is especially evident in the months January and February. In these months, the in-situ measurements also show large grain size in exceptional dry snow conditions. Indeed, it is known that the snow temperature gradient can change shape and grain size where mass transfer from warmer to colder grains causing grain growth, typically forming faceted and surface hoar grains (Colbeck, 1983; 1989). In March, the snow grain sizes are quite variable, without any clear trends, while in April, satellite estimates show a high percentage (around 85%) of high values of grain size. To further assess the behavior of snow grain size, the satellite estimates were compared with LST obtained from both ground measurements (available from snow pits) and satellite imagery (MODIS and ECOSTRESS). The comparison indicates a strong relationship of the grain size evolution from winter to spring with LST changes, thus clearly revealing the aging process (as shown in Figure 1).
In this direction, LST can be seen as relevant parameter for understanding the snow grain size metamorphism (and consequently albedo changes) and due to this strong relationship as a kind of predictors in the evolution of the snowpack especially during the melting phase.
In the presentation, we will present the results obtained by the two proposed algorithms for albedo and grain size by exploiting Sentinel-3 OLCI imagery from 2018 to 2021. Moreover, we will show and discuss the correlation of grain size variability in relation with LST on both temporal and spatial scales.