Investigating the MetOp ASCAT vegetation parameters

Investigating the MetOp ASCAT vegetation parameters

Jooste, Nicael (TU Delft Civil Engineering and Geosciences)

Steele-Dunne, S.C. (mentor)
Lhermitte, S.L.M. (graduation committee)
Coenders-Gerrits, Miriam (graduation committee)

Delft University of Technology

Water Management

2020-03-20

In this study, an unsupervised classification approach is used to investigate and characterize the spatial and temporal variability of MetOp-A ASCAT backscatter (σ◦) and the TUW SMR vegetation parameters across mainland France between 2007 and 2017. Currently, soil moisture data is retrieved from ASCAT backscatter measurements using the TU Wien Soil Moisture Retrieval (TUW SMR) approach. To correct for the influence of vegetation on soil moisture, two so-called ’vegetation parameters’ are also estimated from the backscatter measurements. These vegetation parameters are the slope (σ′) and curvature (σ′′) of a second-order Taylor polynomial which describes the incidence angle dependence of backscatter. Recently, Steele-Dunne et al. (2019) showed that the slope and curvature contain significant information about vegetation phenology and vegetation water dynamics across the North-American grasslands, suggesting that the vegetation parameters are a potentially valuable source of information on vegetation dynamics. This study further investigates the value of the vegetation parameters as a source of information about vegetation dynamics for land cover types present in France. 3492 ASCAT grid points were clustered using agglomerative hierarchical clustering based on σ′ and subsequently analysed. The results show that clusters based on σ′ are contiguous and can resemble distinct land cover features; areas like Paris and the Alps are clearly visible in cluster maps. While the clusters differ in terms of σ′ – which follows from a clustering based on σ′ – the results show that the clusters generally also have distinct σ◦ and σ′′ characteristics. This suggests that the clusters represent ’scattering surfaces’ that differ in terms of their seasonal scattering characteristics. It was found that grid points with a heterogeneous land cover footprint tend to have noisy seasonal backscatter signatures, while homogeneous land cover footprints have more recognizable seasonal behavior. Additionally, certain backscatter signatures tend to correspond to certain land cover footprints; in particular the agricultural area around Paris produced clear σ◦ , σ′, and σ′′ signatures corresponding to specific growth stages of wheat and the rapid land cover change during the agricultural season. In general, the results are consistent with the existing assumptions that σ′ is a measure for vegetation density and σ′′ is a measure for the relative dominance of ground-bounce and direct scattering from vertical vegetation constituents. Finally, clustering was performed on ten years of dynamically estimated σ′ and a measure for robustness was introduced to quantify the clustering certainty for each grid point. Robust grid points are found in areas that have relatively stable land cover like the Alps or Paris, suggesting that these areas exhibit predictable seasonal backscatter behavior with low interannual variability. Poor robustness scores are mainly found in north-west France, where land cover is heterogeneous and seasonal backscatter behavior is highly variable. This study confirms that the TUW SMR vegetation parameters contain valuable information about vegetation phenology across different land cover footprints. Furthermore, it was shown that unsupervised classification methods based on the vegetation parameters are able to identify areas with similar scattering characteristics, and are able to show how these areas change over time.

ASCAT
Backscatter
C-band
Scatterometry
Soil Moisture
Machine Learning
Unsupervised classification
Clustering

http://resolver.tudelft.nl/uuid:dfb07fda-286e-4920-a0f5-b6f9ec7b3e50

2021-03-20

Student theses

master thesis

© 2020 Nicael Jooste