Significant Wave Height Retrieval Based on Multivariable Regression Models Developed With CYGNSS Data

Wang, Changyang; Yu, Kegen; Zhang, Kefei; Bu, Jinwei; Qu, Fangyu

doi:10.1109/tgrs.2022.3233102

Cited by 4 publications

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Drawing upon this model, the application of GNSS-R technology has commenced for monitoring environmental characteristics within the realms of both oceans and land [15]. This encompasses the measurement of ocean wind speed [16], [17], SWH [18], [19], and soil moisture [20], [21], showcasing exceptional performance. Sea surface roughness directly affects GNSS-R observations.…”

Section: Introductionmentioning

confidence: 99%

Correction of the SMAP Sea Surface Brightness Temperature and Retrieval of Sea Surface Salinity Incorporating CYGNSS Observables

Li,

Guo,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

The correction of sea surface brightness temperature is crucial for improving the accuracy of sea surface salinity (SSS) retrieval by L-band microwave radiometer. However, the traditional method of correcting brightness temperature using only wind speed and significant wave height (SWH) is inadequate, as sea surface roughness is affected by multiple factors. The Global Navigation Satellite System Reflectometer (GNSS-R) observables, which directly respond to sea surface roughness, have been preliminarily validated in groundbased experiments for their potential to correct sea surface brightness temperature. Compared to ground-based GNSS-R, spaceborne GNSS-R has a wider coverage and can better support the brightness temperature correction of spaceborne L-band microwave radiometers. This paper has preliminarily verified the correlation between Cyclone GNSS (CYGNSS) observables and brightness temperature variations, and found that the incidence angle of the observable needs to be taken into account when retrieving SSS jointly with Soil Moisture Active and Passive (SMAP) and CYGNSS. A multilayer perceptron (MLP) model was established to assess the SSS retrieval performance of SMAP combined with different parameters. The results show that the retrieval performance based on the MLP model is better than that based on the geophysical model function (GMF) model. Compared to joint wind speed and SWH, joint CYGNSS observables performs better in retrieving SSS. The root mean square error (RMSE) of retrieval salinity decreased from 0.58 to 0.46 psu, and the correlation coefficient (R) increased from 0.83 to 0.90. This provides reference for future joint retrieval of SSS using Lband microwave radiometers and spaceborne GNSS-R.

show abstract