4.7 Article

An Assessment of CYGNSS Normalized Bistatic Radar Cross Section Calibration

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2018.2849323

Keywords

Geophysical measurements; global positioning system (GPS); microwave reflectometry; radar measurements; remote sensing; scattering; sea surface; wind

Funding

  1. NASA Cyclone Global Navigation Satellite System (CYGNSS) project (NASA Science Mission Directorate Contract) [NNL13AQ00C]

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A cyclone global navigation satellite system (CYGNSS) sigma(o) calibration analysis is presented using version 2.0 of the Level 1 dataset available on PO. DAAC. Three separate analyzes are conducted, namely, an examination of the specular bin location (in delay) and sigma(o) relationship, an investigation of the impact of recently improved characterizations of the GPS effective isotropically radiated power on CYGNSS sigma(o), and an intersatellite sigma(o) calibration analysis. We first noted a correlation between the specular delay bin location and sigma(o), where an increase in the specular delay bin resulted in an increase in sigma(o) regardless of the wind speed level; a specular delay bin location ranging from 4.75 to 5.00 and from 7.00 to 7.25 resulted in a 14.74 and 17.72 dB median sigma(o), respectively. Noticeable improvements in the median sigma(o) were present in the version 2.0 dataset, when separating the data by GPS block type: for blocks IIR, IIF, and IIR-M, median sigma(o) were 15.39, 15.42, and 15.10 dB, respectively (compared to 19.38, 20.53, and 21.38 dB in version 1.1). Finally, an unexpected correlation between the instrument noise floor and sigma(o) was observed for all eight observatories while conducting the intersatellite sigma(o) calibration analysis. Approximately 0-1 dB absolute sigma(o) difference biases (with up to similar to 1 dB standard deviation) between spacecrafts were observed. A report of this analysis was presented to CYGNSS scientists and engineers, who eventually found an issue with the D/A DDM scaling algorithm. We expect better statistical performances in future releases of the data.

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