Search

Microwave radiometers measure weak thermal emission from the Earth, which is broadband in nature. Radio frequency interference (RFI) originates from active transmitters and is typically narrow band, directional, and continuous or intermittent. The Global Precipitation Measurement (GPM) Microwave Imager (GMI) has seen RFI caused by ocean reflections from direct broadcast and communication satellites in the shared 18.7-GHz allocated band. This paper focuses on the use of a complex signal kurtosis algorithm to detect direct broadcast satellite (DBS) signals at 18.7 GHz. An experiment was conducted in August 2017 at the Harvest oil platform, located about 10 km off the coast of central California. Data were collected for direct and ocean reflected DBS transmissions in the K- and Ku-bands from a commercial geostationary satellite. Results are presented for the complex kurtosis performance for a five-channel quadrature phase-shift keying (QPSK) signal versus the seven-channel case. As the spectrum becomes more occupied, detector performance decreases. Filtering of RFI in the fully occupied spectrum is very difficult, and detection using the complex kurtosis detector is only possible for very large interference-to-noise ratio (INR) values at −5 dB and higher. This corresponds to over 100 K in a real system such as GMI; therefore, other detection approaches might be more appropriate. [ABSTRACT FROM AUTHOR]

As compared with conventional methods for measuring ocean surface currents, spaceborne synthetic aperture radar (SAR) offers cloud-penetrating ocean-current observation capability at high spatial resolution. While some studies have shown the potential of SAR for studying ocean surface currents through feature tracking, they have only analyzed a few images to demonstrate the basic measurement technique, and no concise general technique has been developed. This paper shows the application of the maximum cross correlation (MCC) method to generate ocean surface currents from nearly two years of available sequential spaceborne C-band SAR imagery from the Envisat ASAR and ERS-2 Advanced Microwave Instrument SAR sensors over the coastal California Current System. The data processing strategies are discussed in detail, and results are compared with HF radar measured currents. One-dimensional wavenumber spectra of the SAR-derived surface currents agree with the k-2 power law, as predicted by sub mesoscale resolution models. Comparisons with HF radar currents show encouraging results with MCC SAR vectors oriented slightly counterclockwise relative to HF radar vectors. MCC SAR surface currents are found to have larger magnitudes than HF radar currents (≈11 cm/s), which may be due to the fact that SAR penetrates only a few centimetres into the ocean surface whereas HF radar currents are averaged over the top 1 m of the ocean surface. The larger part of this magnitude difference is contained in the along-shore component, which can be attributed to higher HF radar accuracy in the direct radial cross-shore measurements as compared with along-shore components derived from multiple cross-shore radial measurements. [ABSTRACT FROM PUBLISHER]

The Cerro Prieto geothermal field (CPGF) lies at the step-over between the imperial and the Cerro Prieto faults in northern Baja California, Mexico. While tectonically this is the most active section of the southern San Andreas Fault system, the spatial and temporal deformation in the area is poorly resolved by the sparse global positioning system (GPS) network coverage. Moreover, interferograms from satellite observations spanning more than a few months are decorrelated due to the extensive agricultural activity in this region. Here we investigate the use of frequent, short temporal baseline interferograms offered by the new Sentinel-1A satellite to recover two components of deformation time series across these faults. Following previous studies, we developed a purely geometric approach for image alignment that achieves better than 1/200 pixel alignment needed for accurate phase recovery. We construct interferometric synthetic aperture radar time series using a coherence-based small baseline subset method with atmospheric corrections by means of common-point stacking. We did not apply enhanced spectral diversity because the burst discontinuities are generally small (<1.4 mm) and can be effectively captured during the atmospheric corrections. With these algorithms, the subsidence at CPGF is clearly resolved. The maximum subsidence rate of 160 mm/yr, due to extraction of geothermal fluids and heat, dominates the ~40 mm/yr deformation across the proximal ends of the imperial, the Cerro Prieto, and the indiviso faults. [ABSTRACT FROM PUBLISHER]

Sea surface nitrate (SSN) is an important parameter to characterize physical and biogeochemical processes, particularly to quantify oceanic new primary production, yet its remote estimation from satellite has been difficult due to the complex relationships between environmental variables and SSN. In the central and southern sections of the California Current System (CSCCS), this challenge is attempted through modeling, validation, and extensive tests in different oceanic scenarios. Specifically, using extensive SSN datasets collected by many cruises spanning 40 years (1978–2018) and Moderate Resolution Imaging Spectroradiometer (MODIS) estimated sea surface temperature (SST) and chlorophyll-a (Chl), a stacking random forest (SRF) model of SSN has been developed and validated with a spatial resolution of ~4 km. The model showed an overall performance of root mean square difference (RMSD) $=0.83\,\,\mu $ mol/kg, with coefficient of determination ($R^{2}) =0.87$ , mean bias $= -0.11\,\,\mu $ mol/kg, and mean ratio = 1.15 for SSN ranging between 0.05 and $19.90~\mu $ mol/kg ($N =1034$). Furthermore, tests of the model with its original parameterization for the upwelling period, oceanic period, and winter period all showed satisfactory performance with an overall RMSD of 1.95 $\mu $ mol/kg. The sensitivity of the SRF model to uncertainties of MODIS SST and Chl was examined, with induced uncertainties of $\le 2.22~\mu $ mol/kg. The extensive evaluation and sensitivity tests indicated the robustness of the SRF model in estimating SSN in the study area of the CSCCS, and it could serve as a robust approach for other regions once sufficient in situ SSN data are available for model calibration. [ABSTRACT FROM AUTHOR]