Requirement of Minimal Signal-to-noise Ratios of Ocean Color Sensors and Uncertainties of Ocean Color Products

Lin Qi, Zhongping Lee, Chuanmin Hu, Menghua Wang

Research output: Contribution to journalArticlepeer-review

Abstract

Using simulations, error propagation theory, and measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS), we determined the minimal signal-to-noise ratio (SNR) required for ocean color measurements and product uncertainties at different spatial and temporal scales. First, based on typical top-of-atmosphere (TOA) radiance over the ocean, we evaluate the uncertainties in satellite-derived R rs in the visible wavelengths (Δ R rs (vis)) due to sensor noise in both the near-infrared (NIR) and the visible bands. While the former induces noise in R rs (vis) through atmospheric correction, the latter has a direct impact on R rs (vis). Such estimated uncertainties are compared with inherent Δ R rs (vis) uncertainties from in situ measurements and from the operational atmosphere correction algorithm. The comparison leads to a conclusion that once SNR(NIR) is above 600:1, an SNR(vis) better than 400:1 will not make a significant reduction in product uncertainties at pixel level under typical conditions for a solar zenith angle of 45°. Then, such uncertainties are found to decrease significantly in data products of oceanic waters when the 1 km pixels from individual images are binned to lower spatial resolution (e.g., 4 km) or temporal resolution (e.g., monthly). Although these findings do not suggest that passive ocean color sensors should have SNR(vis) around 400:1, they do support the argument for more trade space in higher spatial and/or spectral resolutions once this minimal 400:1 SNR(vis) requirement is met.

Original languageAmerican English
JournalJournal of Geophysical Research: Oceans
Volume122
DOIs
StatePublished - Jan 1 2017

Keywords

  • remote sensing
  • sensor design
  • signal-to-noise ratio
  • ocean color
  • atmospheric correction
  • noise reduction
  • uncertainties
  • remote sensing reflectance
  • chlorophyll a

Disciplines

  • Life Sciences

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