 |
| Titel |
Monitoring the Landscape Freeze/Thaw State by Fusion of Ku and C Band Scatterometer Data |
| VerfasserIn |
Simon Zwieback, Annett Bartsch, Thomas Melzer, Wolfgang Wagner |
| Konferenz |
EGU General Assembly 2011
|
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 13 (2011) |
| Datensatznummer |
250049137
|
|
|
|
|
|
| Zusammenfassung |
| The landscape Freeze/Thaw (F/T) state is closely related to various bio- and geophysical
processes, such as vegetation dynamics and the hydrologic cycle. Due to the scarcity of
in-situ monitoring sites, remote sensing methods have been applied to observe the F/T state at
large scales; in particular, microwave remote sensing has been shown to be an apt tool.
Knowledge of the F/T state can be used for, for example, global models of the carbon cycle
and vegetation phenology, as well as for other earth observation techniques, e.g. soil moisture
retrieval.
Scatterometers are active radar instruments that measure the backscatter Ïă0. Our aim is to
infer the F/T state based on scatterometer data: SeaWinds on QuikScat in Ku band and
ASCAT on MetOp in C band. The key advantage of the sensor fusion lies in the fact that Ïă0
is sensitive to different factors at different frequencies, the most striking example being the
backscatter of dry snow, which strongly increases with increasing frequency. We thus propose
a novel algorithm for retrieving the landscape F/T state on a large scale based on the
aforementioned input data. It is based on a probabilistic time series model and allows us to
estimate the probability of each of three states at a given time: frozen, non-frozen and
thawing.
The probabilistic model is an adaptation of the well-known Hidden Markov model
(HMM) – the F/T state is assumed to be a Markov chain, whose value is not directly
observable. At each epoch, however, its current state influences the measurements: Ïă0 at both
frequency bands. The simple structure assures that inference can be done efficiently, e.g. the
calculation of the probability of the state on a given day. The algorithm does not use training
data; the parameters are estimated for each time series in an unsupervised fashion. This is
achieved by maximizing the marginal likelihood in the framework of the Expectation
Maximization algorithm.
The algorithm is analyzed and tested in a study area in Russia and northern China, which
encompasses the region of 120 - 130E and 50 - 75N and covers a variety of biomes such
as Tundra, Taiga and Steppe. The time series of the probability of the state are validated with
in-situ snow and temperature data as well as global climate models. In general, the accuracy
exceeds 90%, but the algorithm can fail in agriculturally used land (fields, pastures) and bare
rock outcrops in mountainous regions. On a more qualitative level, the study affirms the
importance of using two distinct frequencies, as particularly dry snow, vegetation
and the freezing of the soil water manifest themselves differently at Ku and C
band.
The proposed algorithm yields satisfactory results in general, particularly in the Tundra
and Taiga. The validation with external data has revealed weaknesses, such as rock
outcrops in mountainous areas. The benefits associated with the systematic fusion of
different data sources include the improved temporal coverage and the sensitivity to
various influence factors such as snow cover. Overall, the study demonstrates that
probabilistic time-series model such as the HMM are a promising tool for remote
sensing data analysis in general and for determining the F/T state in particular. |
|
|
|
|
|
|