1 November 2010 Assessment of particulate absorption properties in the southeastern Bering Sea from in-situ and remote sensing data
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J. of Applied Remote Sensing, 4(1), 043561 (2010). doi:10.1117/1.3525572
Particulate absorption (aP(λ)) including phytoplankton (aPHY(λ)) and non-algal particles (NAP) (aNAP(λ)) were measured in southeastern Bering Sea during a cruise in July 2008. This study analyzes the aP(λ) properties through in-situ and quasi analytical algorithm (QAA) derived ocean color satellite Medium Resolution Imaging spectrometer (MERIS) and Moderate resolution Imaging Spectroradiometer (MODIS) observations. We found that the aP(λ) and aPHY(λ) correlated well with chlorophyll-a and were lower as a function of chlorophyll-a as compared to low latitudes. The specific phytoplankton absorption (a*PHY(λ)) showed more variability in the blue as compared to the red part of the spectrum indicating pigment packaging and/or change in pigment composition. The remote sensing reflectance (Rrs(λ)) showed significant variability in spectral shape and magnitude which was consistent with the variable total absorption minus pure water absorption (aT-W(λ)) spectra observed in the study area. Simple satellite retrieved Rrs(λ) ratios were related to in-situ aPHY(λ) and aDG(λ) by applying an inverse power fit; Rrs(490)/Rrs(510) gave the best results for aPHY(443) and aDG(443) (R2 - 0.80 and 0.75) respectively. The match-ups of in-situ and MERIS retrieved aPHY(λ) and NAP plus colored dissolved organic matter (aDG(λ)) using QAA after log-transformation showed reasonable agreement with R2 of 0.71 and 0.61 and RMSE of 0.316 and 0.391 at 443 nm, respectively. Although the QAA derived aPHY(λ) and aDG(λ) from MERIS overestimated and underestimated, respectively the in-situ measurements at all wavelengths, the match-up analysis was encouraging.
Puneeta Naik, Eurico D'Sa, Joaquim I. Goes, Helga do Rosario Gomes, "Assessment of particulate absorption properties in the southeastern Bering Sea from in-situ and remote sensing data," Journal of Applied Remote Sensing 4(1), 043561 (1 November 2010). http://dx.doi.org/10.1117/1.3525572




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