Sea Surface Salinity Products Validation Based on Triple Match Method

Abstract

Since satellites have observed the sea surface temperature (SSS) from space for years, the scientific community has devoted many efforts to the validation of satellite SSS products. Typically, this validation procedure is based on the 'double match' method between the in situ and remote-sensed measurements. However, this direct comparison has its limitations because it does not take into account sampling error of different SSS sources. Actually, the in situ method presents the pointwise measurements and the satellite data are the spatial average within its footprint, so the in situ data contain the true small-scale SSS signal which cannot be resolved by satellite data. Researchers introduce the representativeness error to describe the small-scale signal. However, the estimation of representativeness error remains challenging. In this study, based on the constancy of salinity variance, we develop a new method to estimate the representativeness error and apply it to the triple collocation dataset of Argo data and L3 SSS product of soil moisture active/passive (SMAP) and soil moisture and ocean salinity (SMOS). The representativeness error is estimated to be 0.093 psu2 in global oceans. The random error of Argo data is better than 0.21 psu which is superior to SMAP and SMOS. Considering the different sampling resolution of SMAP and SMOS, the quality of SMAP SSS product (0.33 psu) is slightly better than SMOS (0.41 psu).