A prototype AMSR-E global snow area and snow depth algorithm

Abstract

Abstract-A methodologically simple approach to estimate snow depth from spaceborne microwave instruments is described. The scattering signal observed in multifrequency passive microwave data is used to detect snow cover. Wet snow, frozen ground, precipitation, and other anomalous scattering signals are screened using established methods. The results from two different approaches (a simple time and continentwide static approach and a space and time dynamic approach) to estimating snow depth were compared. The static approach, based on radiative transfer calculations, assumes a temporally constant grain size and density. The dynamic approach assumes that snowpack properties are spatially and temporally dynamic and requires two simple empirical models of density and snowpack grain radius evolution, plus a dense media radiative transfer model based on the quasicrystalline approximation and sticky particle theory. To test the approaches, a four-year record of daily snow depth measurements at 71 meteorological stations plus passive microwave data from the Special Sensor Microwave Imager, land cover data and a digital elevation model were used. In addition, testing was performed for a global dataset of over 1000 World Meteorological Organization meteorological stations recording snow depth during the 2000-2001 winter season. When compared with the snow depth data, the new algorithm had an average error of 23 cm for the one-year dataset and 21 cm for the four-year dataset (131% and 94% relative error, respectively). More importantly, the dynamic algorithm tended to underestimate the snow depth less than the static algorithm. This approach will be developed further and implemented for use with the Advanced Microwave Scanning Radiometer-Earth Observing System aboard Aqua.Index Terms-Dense media radiative transfer model, microwave radiometry, remote sensing, snow depth.

Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016

“…Studies have noted that SWE estimates from the Chang equation have high uncertainties (e.g., Kelly et al, 2003;Kelly, 2009;Tedesco and Narvekar, 2010;Daly et al, 2012), particularly in dense forests. However, as much of our study area is non-forested -and we use SWE only as a rough estimate of snow volume -we choose to rely on the simple Chang equation rather than a more complex algorithm for SWE estimation.…”

“…To track the end of snowmelt, we leverage two additional datasets: (1) the raw Tb 37V time series, which rapidly increases as snowpack thins, and (2) a SWE time series calculated from the Tb 19 and Tb 37 GHz channels (Chang et al, 1987;Kelly et al, 2003;Tedesco et al, 2015;Smith and Bookhagen, 2016).…”

“…We choose the yearly SWE minimum instead of zero as our SWE threshold for snow clearance because some regions in HMA have a defined melt season but rarely reach zero SWE. This also helps control for uncertainty in shallow SWE measurements, as detecting shallow snow (< 5 cm) with PM data is still difficult (Kelly et al, 2003;Armstrong and Brodzik, 2001). A full description of our melt detection algorithm is available in the Supplement (Figs.…”

Evaluating global snow water equivalent products for testing land surface models

“…The larger the snow grains the greater the scattering for a given SWE and so a, in equation 1, is tuned for a particular snow grain size. The value of 1.59 was found by assuming a grain size of 0.35 mm (Chang et al 1982), but as snow grains change through the winter an assumption of constant grain size will cause errors (Kelly et al 2003). Liquid water affects the snow-pack's dielectric constant, increasing attenuation, potentially preventing the retrieval of SWE (Stiles and Ulaby 1980).…”

Soil Moisture & Snow Properties Determination with GNSS in Alpine Environments: Challenges, Status, and Perspectives

“…These two parameters are particularly important in alpine and sub-alpine regions, where snow is often around its melting point and temperature gradients within the snowpack may be large. These parameters also change on a spatial scale of tens of meters [25] and throughout the snowpack respectively, as well as temporally [76,78,79] on a scale of hours. As HS and HNS are calculated from SWE, these are also affected by the constant assumptions.…”