[1] Accurate prediction of snowpack status is important for a range of environmental applications, yet model estimates are typically poor and in situ measurement coverage is inadequate. Moreover, remote sensing estimates are spatially and temporally limited due to complicating effects, including distance to open water, presence of wet snow, and presence of thick snow. However, through assimilat...

A novel methodology is introduced for quantifying the added value of remotely sensed soil moisture products for global land surface modeling applications. The approach is based on the assimilation of soil moisture retrievals into a simple surface water balance model driven by satellite-based precipitation products. Filter increments (i.e., discrete additions or subtractions of water suggested b...

In Australia, remotely sensed Landsat data is routinely used for mapping and monitoring changes in the extent of woody perennial vegetation. Time series remotely sensed satellite imagery and ground information is used to form multi-temporal classifications of presence/absence of woody cover. Two broad-scale operational land cover change and monitoring projects are based on a series of algorithm...