H21H-1484
Investigating Baseline, Alternative and Copula-based Algorithm for combining Airborne Active and Passive Microwave Observations in the SMAP Context

Tuesday, 15 December 2015
Poster Hall (Moscone South)
Carsten Montzka1, Christof Lorenz2, Thomas Jagdhuber3, Patrick Laux4, Irena Hajnsek5, Harald Kunstmann2, Dara Entekhabi6 and Harry Vereecken7, (1)Forschungszentrum Jülich GmbH, Jülich 52428, Germany, (2)Karlsruhe Institute of Technology, Karlsruhe, Germany, (3)German Aerospace Center DLR Oberpfaffenhofen, Microwaves and Radar Institute, Oberpfaffenhofen, Germany, (4)Karlsruhe Institute of Technology (KIT), Institute for Meteorology and Climate Research (IMK-IFU), Garmisch-Partenkirchen, Germany, (5)ETH Swiss Federal Institute of Technology Zurich, Zurich, Switzerland, (6)Massachusetts Institute of Technology, CEE, Cambridge, MA, United States, (7)Institute of Bio- and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
Abstract:
The objective of the NASA Soil Moisture Active & Passive (SMAP) mission is to provide global measurements of soil moisture and freeze/thaw states. SMAP integrates L-band radar and radiometer instruments as a single observation system combining the respective strengths of active and passive remote sensing for enhanced soil moisture mapping.

Airborne instruments will be a key part of the SMAP validation program. Here, we present an airborne campaign in the Rur catchment, Germany, in which the passive L-band system Polarimetric L-band Multi-beam Radiometer (PLMR2) and the active L-band system F-SAR of DLR were flown simultaneously on the same platform on six dates in 2013. The flights covered the full heterogeneity of the area under investigation, i.e. all types of land cover and experimental monitoring sites with in situ sensors. Here, we used the obtained data sets as a test-bed for the analysis of three active-passive fusion techniques: A) The SMAP baseline algorithm: Disaggregation of passive microwave brightness temperature by active microwave backscatter and subsequent inversion to soil moisture, B), the SMAP alternative algorithm: Estimation of soil moisture by passive sensor data and subsequent disaggregation by active sensor backscatter and C) Copula-based combination of active and passive microwave data. For method C empirical Copulas were generated and theoretical Copulas fitted both on the level of the raw products brightness temperature and backscatter as well as two soil moisture products.

Results indicate that the regression parameters for method A and B are dependent on the radar vegetation index (RVI). Similarly, for method C the best performance was gained by generating separate Copulas for individual land use classes. For more in-depth analyses longer time series are necessary as can obtained by airborne campaigns, therefore, the methods will be applied to SMAP data.

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