Development and Prospect of Satellite Remote Sensing Technology for Ocean Dynamic Environment

Zhang, Qingjun; Zhang, Liqiang; Jia, Yongjun; Wang, Beichao; Li, Shuang

doi:10.2514/1.a35710

Cited by 1 publication

(2 citation statements)

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“…The output of an interferometric radiometer instrument is a series of visibility function samples, which are cross-correlations of the signals collected by two antennas [12][13][14]. In the spatial-frequency domain, the visibility V i,j between the antenna pairs (i, j), where i ̸ = j, is a complex function, as expressed in (1), which is related to T B in the antenna coordinate through Fourier transform [12,14,16].…”

Section: Theory Of Interferometric Radiometermentioning

confidence: 99%

“…Sea surface salinity (SSS), along with other ocean dynamic environment elements such as sea surface height and temperature, are crucial elements of oceanographic research [1][2][3]. These elements play a pivotal role in the balance of ocean energy and global climate change [1,4,5]. Therefore, ocean salinity observation has always been a significant aspect of ocean remote sensing.…”

Section: Introductionmentioning

confidence: 99%

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Fusion Method of RFI Detection, Localization, and Suppression by Combining One-Dimensional and Two-Dimensional Synthetic Aperture Radiometers

Zhang,

Jin,

Zhang

et al. 2024

Remote Sensing

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Ocean salinity is a pivotal aspect of the ocean dynamic environment. Spaceborne L-band radiometers, like SMOS, Aquarius, and SMAP, offer a comprehensive approach to mapping out large-scale ocean salinity patterns. As China prepares for the launch of the Chinese Ocean Salinity and Soil Moisture Mission (COSM), it is essential to delve into the intricacies of radio frequency interference (RFI) detection, localization, and mitigation. The L-band, in particular, is highly susceptible to RFI. COSM will carry not one but two advanced instruments: a 2-D L-band aperture synthesis microwave radiometer (LASMR) and a 1-D L-C-K band microwave imager combined active and passive (MICAP). This article delves into the current state of RFI research, particularly in recent years, and introduces a fusion method that integrates MICAP and LASMR for more accurate RFI detection, localization, and mitigation. This fusion method relies on an algorithm that constructs localization and intensity objective functions based on the principle of least squares. By optimizing these functions, we can pinpoint the precise location and intensity of RFI. The resulting minimum mitigation residual offers a blueprint for achieving optimal RFI detection, localization, and mitigation. The experimental results, achieved in a controlled anechoic chamber, confirm that this fusion method—when weighted by variance—boosts detection accuracy, refines localization precision, and minimizes residual mitigation errors compared with standalone MICAP or LASMR techniques.

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