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Validation of Sea Surface Salinity Products of HY–4A LASMR Based on Argo Observations: Results of First On-Orbit Year
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DOI：10.3390/jmse14161492.png)
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Abstract
En 中文
HY–4A is China’s first ocean salinity remote-sensing satellite, launched into orbit in November 2024 and currently in operational service. The LASMR (L-Band Aperture Synthesis Microwave Radiometer) is the L-band synthetic aperture radiometer onboard the HY–4A satellite. This study validates the LASMR Level-2 SSS (sea surface salinity) product using in situ salinity observations from Argo floats, covering the period from November 2024 to December 2025. Global analysis indicates that the LASMR SSS retrieval uncertainties show a distinct zonal distribution, which primarily reflects the impact of sea surface temperature (SST) and sea surface wind speed on SSS retrieval accuracy. A lower SST reduces the sensitivity of brightness temperature (TB) to SSS variations, and a high wind speed degrades the sea surface roughness correction. Both factors lead to increasing uncertainties in SSS retrieval. Furthermore, atmospheric parameters including water vapor content and precipitation also affect the SSS retrieval uncertainty. The influence of water vapor may originate from its coupling with SST/wind speed and inherent uncertainties in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The effect of precipitation is more complex: it increases ocean TB through rain-induced surface freshening and additional rain-induced roughening, which aliases into the satellite signal. Moreover, precipitation-enhanced vertical salinity gradients amplify the vertical representativeness error arising from the depth difference between satellite sensing and Argo measurements. Meanwhile, impacted by land brightness temperature contamination and radio-frequency interference (RFI), the SSS retrieval accuracy of HY–4A decreases significantly in coastal waters compared with the open ocean. Since the traditional buoy–satellite dual-matching method tends to overestimate uncertainties in satellite data, an Argo/HY–4A/SMAP (Soil Moisture Active Passive) triple-collocation dataset is used to estimate the LASMR SSS retrieval uncertainties. The triple-collocation method yields robust uncertainty estimates for both satellites (HY–4A and SMAP) over the global ocean and high-salinity-variability regions. In conclusion, the global uncertainty of the HY–4A LASMR SSS product is 0.35 psu. These results provide a reference for future product refinement and improvements in HY–4A SSS retrieval algorithms.
Keywords:
HY–4A
SSS
validation
retrieval uncertainty
triple-collocation
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