Search Results (2)

The Radar Vegetation Index (RVI) is a well-established microwave metric of vegetation cover. The index utilizes measured linear scattering intensities from co- and cross-polarization and is normalized to ideally range from 0 to 1, increasing with vegetation cover. At long wavelengths (L-band) microwave scattering does not only contain information coming from vegetation scattering, but also from soil scattering (moisture & roughness) and therefore the standard formulation of RVI needs to be revised. Using global level SMAP L-band radar data, we illustrate that RVI runs up to 1.2, due to the pre-factor in the standard formulation not being adjusted to the scattering mechanisms at these low frequencies. Improvements on the RVI are subsequently proposed to obtain a normalized value range, to remove soil scattering influences as well as to mask out regions with dominant soil scattering at L-band (sparse or no vegetation cover). Two purely vegetation-based RVIs (called RVII and RVIII), are obtained by subtracting a forward modeled, attenuated soil scattering contribution from the measured backscattering intensities. Active and passive microwave information is used jointly to obtain the scattering contribution of the soil, using a physics-based multi-sensor approach; simulations from a particle model for polarimetric vegetation backscattering are utilized to calculate vegetation-based RVI-values without any soil scattering contribution. Results show that, due to the pre-factor in the standard formulation of RVI the index runs up to 1.2, atypical for an index normally ranging between zero and one. Correlation analysis between the improved radar vegetation indices (standard RVI and the indices with potential improvements RVII and RVIII) are used to evaluate the degree of independence of the indices from surface roughness and soil moisture contributions. The improved indices RVII and RVIII show reduced dependence on soil roughness and soil moisture. All RVI-indices examined indicate a coupled correlation to vegetation water content (plant moisture) as well as leaf area index (plant structure) and no single dependency, as often assumed. These results might improve the use of polarimetric radar signatures for mapping global vegetation. Full article

The passive bistatic radar (PBR) system can utilize the illuminator of opportunity to enhance radar capability. By utilizing the forward scattering technique and procedure into the specific mode of PBR can provide an improvement in target detection and classification. The system is known as passive Forward Scattering Radar (FSR). The passive FSR system can exploit the peculiar advantage of the enhancement in forward scatter radar cross section (FSRCS) for target detection. Thus, the aim of this paper is to show the feasibility of passive FSR for moving target detection and classification by experimental analysis and results. The signal source is coming from the latest technology of 4G Long-Term Evolution (LTE) base station. A detailed explanation on the passive FSR receiver circuit, the detection scheme and the classification algorithm are given. In addition, the proposed passive FSR circuit employs the self-mixing technique at the receiver; hence the synchronization signal from the transmitter is not required. The experimental results confirm the passive FSR system’s capability for ground target detection and classification. Furthermore, this paper illustrates the first classification result in the passive FSR system. The great potential in the passive FSR system provides a new research area in passive radar that can be used for diverse remote monitoring applications. Full article