Abstract
Abstract. The capability of time-lapse photography to retrieve snow depth time series was tested. Historically, snow depth has been measured manually by rulers, with a temporal resolution of once per day, and it is a time-consuming activity. In the last few decades, ultrasonic and/or optical sensors have been developed to obtain automatic and regular measurements with higher temporal resolution and accuracy. The Finnish Meteorological Institute Image Processing Toolbox (FMIPROT) has been used to retrieve the snow depth time series from camera images of a snow stake on the ground by implementing an algorithm based on the brightness difference and contour detection. Three case studies have been illustrated to highlight potentialities and pitfalls of time-lapse photography in retrieving the snow depth time series: Sodankylä peatland, a boreal forested site in Finland, and Gressoney-La-Trinité Dejola and Careser Dam, two alpine sites in Italy. This study presents new possibilities and advantages in the retrieval of snow depth in general and snow depth time series specifically, which can be summarized as follows: (1) high temporal resolution – hourly or sub-hourly time series, depending on the camera's scan rate; (2) high accuracy levels – comparable to the most common method (manual measurements); (3) reliability and visual identification of errors or misclassifications; (4) low-cost solution; and (5) remote sensing technique – can be easily extended in remote and dangerous areas. The proper geometrical configuration between camera and stake, highlighting the main characteristics which each single component must have, has been proposed. Root mean square errors (RMSEs) and Nash–Sutcliffe efficiencies (NSEs) were calculated for all three case studies comparing with estimates from both the FMIPROT and visual inspection of images directly. The NSE values were 0.917, 0.963 and 0.916, while RMSEs were 0.039, 0.052 and 0.108 m for Sodankylä, Gressoney and Careser, respectively. In terms of accuracy, the Sodankylä case study gave better results. The worst performances occurred at Careser Dam located at 2600 m a.s.l., where extreme weather conditions and a low temporal resolution of the camera occur, strongly affecting the clarity of the images.
Highlights
Seasonal snow has an important role in the Earth’s climate system and a strong influence on its energy balance (Henderson et al, 2018), as well as providing a fundamental contribution to the river discharge in catchments located in alpine and cold regions (Mastrotheodoros et al, 2020)
We can say that the algorithm of retrieval may fail in snow depth detection when the geometrical configuration of the camera and the stake were not defined for this aim
As previously, considering as reference the visual snow depth estimations, we found for the ultrasonic snow depth sensor RMSEUS = 0.052 m and NSEUS = 0.881, while for the Finnish Meteorological Institute Image Processing Toolbox (FMIPROT) retrievals RMSEFMI = 0.039 m and NSEFMI = 0.917
Summary
Seasonal snow has an important role in the Earth’s climate system and a strong influence on its energy balance (Henderson et al, 2018), as well as providing a fundamental contribution to the river discharge in catchments located in alpine and cold regions (Mastrotheodoros et al, 2020). Remote sensing is becoming the most widespread technique to evaluate snow cover (Da Ronco et al, 2020), snow depth (De Michele et al, 2016; Avanzi et al, 2018; Lievens et al, 2019) and snow water equivalent (Takala et al, 2011, 2017). From a hydrological point of view, the main variable of snowpack is the snow water equivalent (SWE), rather than snow depth (SD), or snow density (ρs) (De Michele et al, 2013; Avanzi et al, 2015, 2014). M. Bongio et al.: Snow depth time series retrieval by time-lapse photography and Rango, 2008) given in Eq (1) below: SWE(t) = SD(t) ρs(t) ,
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
