The development of new algorithms for remote sensing of snow conditions based on data from the catchment of Øvre Heimdalsvatn and the vicinity

Abstract

The catchment of Øvre Heimdalsvatn and the surrounding area was established as a site for snow remote sensing algorithm development, calibration and validation in 1997. Information on snow cover and snowmelt are important for understanding the timing and scale of many lake ecosystem processes. Field campaigns combined with data from airborne sensors and spaceborne high-resolution sensors have been used as reference data in experiments over many years. Several satellite sensors have been utilised in the development of new algorithms, including Terra MODIS and Envisat ASAR. The experiments have been motivated by operational prospects for snow hydrology, meteorology and climate monitoring by satellite-based remote sensing techniques. This has resulted in new time-series multi-sensor approaches for monitoring of snow cover area (SCA) and snow surface wetness (SSW). The idea was to analyse, on a daily basis, a time series of optical and radar satellite data in multi-sensor models. The SCA algorithm analyses each optical and synthetic aperture radar (SAR) image individually and combines them into a day product based on a set of confidence functions. The SSW algorithm combines information about the development of the snow surface temperature and the snow grain size (SGS) in a time-series analysis. The snow cover algorithm is being evaluated for application in a global climate monitoring system for snow variables. The successful development of these algorithms has led to operational applications of snow monitoring in Norway and Sweden, as well as enabling the prediction of the spring snowmelt flood and thus the initiation of many lake production processes.

Disentangling the mechanisms behind winter snow impact on vegetation activity in northern ecosystems

“…Compared to GlobSnow, the SWE retrieval based solely on passive microwave radiometer data shows spurious features during snowmelt and would overestimate SWE because of strong thermal gradients and erroneous forest cover correction factors (Hancock, Huntley, Ellis, & Baxter, ). In this study, we use the GlobSnow SWE product with mountainous regions unmasked, and restrict our analysis to pixels with SWE estimates larger than a previously suggested minimum detectable value of 15 mm since shallow snowpack could be missed by microwave sensors (Solberg et al., ).…”

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

“…Owing to the unique sensing characteristics of SAR, the snow information recorded in SAR imagery is fundamentally different when compared to optical/multispectral imagery. The former records surface characteristics related to the roughness and dielectric properties; the latter records the reflection/absorption of the incoming solar irradiation at the top layer of the surface [68]. As snow, ice, and clouds are characterized by comparatively similar reflection properties in the visible and-depending on the cloud phase-the near to medium infrared part of the spectrum, confusions can occur when attempting to classify snow cover and discriminate it from ice or clouds [69,70].…”

The Potential of Earth Observation for the Analysis of Cold Region Land Surface Dynamics in Europe—A Review

“…To provide snow monitoring service in Scandinavia and the European Alps, the concept has been applied in the EO-HYDRO project within ESA's Earth Observation Market Development (EOMD) program [252]. Later, Solberg et al [253] presented and summarized the confidence index based multi-sensor algorithm for snow cover and snowmelt monitoring for Norway and Sweden using MODIS-Terra and ENVISAT -ASAR data time series.…”