Abstract
It is common in estuarine waters to place fixed monitoring stations, with the advantages of easy maintenance and continuous measurements. These two features make fixed monitoring stations indispensable for understanding the optical complexity of estuarine waters and enable an improved quantification of uncertainties in satellite-derived water quality variables. However, comparing the point-scale measurements of stationary monitoring systems to time-snapshots of satellite pixels suffers from additional uncertainties related to temporal/spatial discrepancies. This research presents a method for validating satellite-derived water quality variables with the continuous measurements of a fixed monitoring station in the Ems Dollard estuary on the Dutch-German borders. The method has two steps; first, similar in-situ measurements are grouped. Second, satellite observations are upscaled to match these point measurements in time and spatial scales. The upscaling approach was based on harmonizing the probability distribution functions of satellite observations and in-situ measurements using the first and second moments. The fixed station provided a continuous record of data on suspended particulate matter (SPM) and chlorophyll-a (Chl-a) concentrations at 1 min intervals for 1 year (2016–2017). Satellite observations were provided by Sentinel-2 (MultiSpectral Instrument, S2-MSI) and Sentinel-3 (Ocean and Land Color Instrument, S3-OLCI) sensors for the same location and time of in-situ measurements. Compared to traditional validation procedures, the proposed method has improved the overall fit and produced valuable information on the ranges of goodness-of-fit measures (slope, intercept, correlation coefficient, and normalized root-mean-square deviation). The correlation coefficient between measured and derived SPM concentrations has improved from 0.16 to 0.52 for S2-MSI and 0.14 to 0.84 for S3-OLCI. For the Chl-a matchup, the improvement was from 0.26 to 0.82 and from 0.14 to 0.63 for S2-MSI and S3-OLCI, respectively. The uncertainty in the derived SPM and Chl-a concentrations was reduced by 30 and 23% for S2-SMI and by 28 and 16% for S3-OLCI. The high correlation and reduced uncertainty signify that the matchup pairs are observing the same fluctuations in the measured variable. These new goodness-of-fit measures correspond to the results of the performed sensitivity analysis, previous literature, and reflect the inherent accuracy of the applied derivation model.
Highlights
With the planned launch of the Pre-Aerosol Clouds and ocean Ecosystem (PACE) in 2022, and the data streaming from the Sentinel missions, Earth Observation (EO) technology is moving toward a long-term sustainable data flow, forming the basis for its operational use (Groom et al, 2019)
Water quality variables that can be derived from optical observations are those with the property of changing the visible sunlight through absorption and/or scattering (Mobley et al, 1993), namely phytoplankton pigment, suspended particulate matter (SPM) and colored dissolved organic matter (CDOM) (Kirk, 1994)
This study has presented a validation procedure for water quality products derived from the Sentinel missions in estuarine waters
Summary
With the planned launch of the Pre-Aerosol Clouds and ocean Ecosystem (PACE) in 2022, and the data streaming from the Sentinel missions, Earth Observation (EO) technology is moving toward a long-term sustainable data flow, forming the basis for its operational use (Groom et al, 2019). The added value of satellite products in estuarine waters is determined, primarily, by their accuracy, which needs to be validated against reference data In this regard, continuous data measured from a fixed monitoring station are indispensable for understanding estuarine systems (Benway et al, 2019) and quantifying the uncertainties of satellite products of water quality. Groetsch et al (2014, 2016) have shown a mismatch between fluorescence measurements from ship-ofopportunity, taken at 5 m below the water surface, and satellite-derived concentrations of chlorophyll-a (Chl-a) The authors attributed this mismatch to the different sample sizes of a satellite pixel vs a point in-situ measurement and the depthintegrated satellite observation vs in-situ measurement taken at a specific depth
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