Cross-Sensor Continuity of Satellite-Derived Water Clarity in the Gulf of Mexico: Insights Into Temporal Aliasing and Implications for Long-Term Water Clarity Assessment

Addressing critical earth science questions often requires time scales beyond the life of any single satellite sensor. Overlap between satellite-based datasets allows for the quantification of continuity (and discrepancies) between sensors. Toward that end, collocated matchups between Sea-viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imager Radiometer Suite (VIIRS) water clarity data from the Gulf of Mexico were analyzed at simultaneous, daily, and monthly time scales. Simultaneous data indicated strong agreement between sensors, with unbiased percent difference (UPD) generally less than 10% for both SeaWiFS/MODIS and VIIRS/MODIS matchups, with no apparent temporal trends. Spatially, UPD was highest near frontal boundaries and at high sensor zenith angles, while bias showed nearshore/offshore trends. UPD and bias statistics did not diminish for daily matchups; however, large degradation was seen for comparisons of monthly means between sensors, particularly SeaWiFS/MODIS matchups. Data coverage represented an important factor contributing to uncertainties in monthly mean data, as higher UPD was observed when fewer valid satellite measurements were recorded. Requiring a minimum of 15 samples per pixel per month minimizes the uncertainties in monthly mean products, with UPD between satellites roughly equivalent to that for simultaneous matchups. Overall, these findings demonstrate high consistency between three satellite instruments for most locations, while several “hot spots” of inconsistency are also revealed, which should be avoided in time-series studies. The findings also highlight the need to quantify uncertainties in often-used satellite products (particularly monthly mean composites) as well as the need to have a sufficient number of observations to assure the fidelity of monthly means.