Subfootprint Variability of Sea Surface Salinity Observed during the SPURS-1 and SPURS-2 Field Campaigns

Abstract

Subfootprint variability (SFV), variability within the footprint of a satellite measurement, is a source of error associated with the validation process, especially for a satellite measurement with a large footprint such as those measuring sea surface salinity (SSS). This type of error has not been adequately quantified in the past. In this study, I have examined SFV using in situ ocean data from the SPURS-1 (Salinity Processes in the Upper ocean Regional Studies-1) and SPURS-2 field campaigns in the subtropical North Atlantic and eastern tropical North Pacific respectively. I computed SFV from these data over two one-year periods of intense sampling. The results show that SFV is highly seasonal. I have computed SFV errors in several different forms, a median value of the weekly snapshot error, a total snapshot error, an absolute error of the Aquarius and SMAP (Soil Moisture Active Passive) measurement, a part of that error associated with SFV and a bias due to the skewness of the distribution of SSS. These results are characteristic only of the particular regions studied. However, comparison of the results with high resolution models, and in situ data from moorings gives the possibility of getting global estimates of SFV from these other more common sources of SSS data.

Freshwater Variability and Transport in the Labrador Sea From In Situ and Satellite Observations

“…10.1029/2020JC016751 5 of 17 and fall (Castelao et al, 2019;Luo et al, 2016). Some of the differences between the patterns observed with in situ and satellite measurements (e.g., the northeastern corner of the Labrador Sea is fresher in November-January than in February-April based on in situ observations, Figure 2, but the opposite is true based on SMOS data, Figure 3, supporting information Figure S2) may be related to the different time periods covered by the observations, to subfootprint variability (Bingham, 2019;Boutin et al, 2016), and to the fact that SMOS data represent surface values, while NODC data represents observations at 10 m.…”

Sea Surface Salinity Seasonal Variability in the Tropics from Satellites, Gridded In Situ Products and Mooring Observations

“…We have done more detailed comparisons of amplitude (Figure 9) and phase (Figure 7) in the discrete locations defined by the moorings (Figure 1) than was done by Yu et al [20] or any previous studies. The disadvantage is the limited geographical expanse of the mooring array-most are equatorward of 10 • especially in the Pacific, and the limited coverage of a point measurement from a mooring relative to the spatial averages from a satellite or gridded in situ product [45].…”

Statistical Assessment of Sea-Surface Salinity from SMAP: Arabian Sea, Bay of Bengal and a Promising Red Sea Application

“…Besides that, there is also Radio Frequency Interference (RFI) that results from the unauthorized use of the protected L-band that may measurements, such as from Argo floats; others can be hourly or daily averages, such as time series from moored buoys, and these differences may affect comparison statistics between satellite and in situ observations. Moreover, there is variability within the satellite footprint that is captured by in situ measurements but not by the satellite, in which SSS can be interpreted as a spatial-average over the footprint [33][34][35]. Because of the inconsistency between satellite and in situ measurements, the present work uses the term "difference" instead of "error" to refer to it (see [4] for a discussion on this matter).…”