How well do we estimate particulate backscatter from satellites as compared to autonomous profiling floats in the open ocean?

Particulate backscatter (b_bp) has been used to as a proxy of particulate organic carbon, phytoplankton carbon, and as an input to estimate particle size, particle export, and net primary production on global scales using satellites. Satellite derived b_bp has been historically difficult to validate due to sparse in situ observations, but it is important (especially for global ecosystem studies) to know where, when, and how satellite derived b_bp may be biased. The ongoing deployment of BGC-Argo floats with backscattering sensors (at 700 nm) makes it now possible to compare satellite estimates of b_bp with a large number of coincident in situ b_bp matchups. Depending on the sensor, location, or b_bp algorithm used, there is a wide spread in performance (Spearman’s rank correlation varies from r = 0.06 to r = 0.79). The performance differences allow us to 1) investigate the influence that R_rs noise, spatial sampling bias, and the assumed b_bp spectral slope have on b_bp retrievals across the satellite sensors, and 2) provide recommendations for future BGC-Argo sampling and satellite validation efforts. Our results broadly demonstrate the utility of using BGC-Argo floats for validation of ocean satellite products.