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With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Ob-serving System for Europe L-band SAR (ROSE-L) and its integration into existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. The SARSense campaign was conducted between June and August 2019 to investigate the potential for estimating soil and plant parameters at the agricultural test site in Selhausen (Germany). It included C- and L-band air- and space-borne observations accompanied by extensive in situ soil and plant sampling as well as unmanned aerial system (UAS) based multi-spectral and thermal infrared measurements. In this regard, we introduce a new publicly available SAR data set and present the first analysis of C- and L-band co- and cross-polarized backscattering signals regarding their sensitivity to soil and plant parameters. Results indicate that a multi-fre-quency approach is relevant to disentangle soil and plant contributions to the SAR signal and to identify specific scattering mechanisms associated with the characteristics of different crop type, especially for root crops and cereals.

A method of physical integration of scatteringmodels is presented here to estimate the backscattering coefficient(BSC) for L-band, brightness temperature (TB) for L- and C-Bandand the reflectance for visible (VIS) and near-infrared (NIR)region for dynamic vegetated terrain. The SPIN (SpectrumInvariant Interaction) model is obtained by solving vectorradiative transfer (VRT) equations kernel-based and therefore fordifferent wave interaction mechanisms. To demonstrate itsapplication for the microwave region, the measurements duringthe growing cycle of corn from the Eleventh Microwave, Water,and Energy Balance Experiment (MicroWEX-11) have been used.For the optical part the results are compared with the PROSAILmodel. By applying the SPIN model in the radar regime, it couldbe shown that the modeled backscattering coefficients (BSC)correlate strongly with the vertical polarization measurements(Pearson 0.83, R20.69) and are less correlated with the horizontalmeasurements (Pearson 0.45, R20.20). In addition, the modeledbrightness temperatures (L- and C-band) in both polarizationstates are also highly correlated with the MicroWEX-11measurements (L-band: Pearson 0.755, R2 0.57; C-band: Pearson0.73, R2 0.53). Finally, the optical results are consistent with theresults of other optical models (Pearson 0.99, R2 0.98).

Many attempts have been made and are undertaken to model the electromagnetic (EM) interaction with vegetation-coveredsoils at different frequencies. The modelling concepts and architectures range from empirical correlations to simple andsophisticated physical models [1, 2]. However, a general understanding of the physical properties and interactions along frequencyand for a wider part of the EM spectrum is difficult if the different models are treating the single frequencies and acquisitioncharacteristics (e.g. active or passive recording) separately. Due to the increasing fleet of earth observation sensors operating inmultiple frequency bands, demand for a spectrum-overarching EM modelling is becoming increasingly important. An integrativemodeling approach offers the chance that the different insights into biophysical and biochemical processes can be consideredconsistently including the respective dielectric properties and structural characteristics. With this objective we developed a kernel-driven electromagnetic interaction model using vector radiative transfer (VRT), called SPIN (Spectrum Invariant Interaction)model, which represents an integrated model approach for active and passive microwaves and passive optical as well as thermalsignatures [3]. Preparatory research for this study was the development of the RadOptics Model [4]. The main advantage of a kernel-driven approach is its analytical invertibility and the representation of the VRT as sum of linear equations, which leads toan enlargement of the information by including multiple frequencies. In addition, the model does not require semi-empirical or statistical calibration (i.e. all model parameters have physical meaning and none just for model adjustment.).