LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Performance of existing QAAs in Secchi disk depth retrieval in phytoplankton and dissolved organic matter dominated inland waters

Photo by julianhochgesang from unsplash

Abstract. A semianalytical model developed to estimate the Secchi disk depth (ZSD) was used in eutrophic-to-hypereutrophic reservoirs (Ibitinga, Ibi, and Barra Bonita, BB) placed in the cascade system of the… Click to show full abstract

Abstract. A semianalytical model developed to estimate the Secchi disk depth (ZSD) was used in eutrophic-to-hypereutrophic reservoirs (Ibitinga, Ibi, and Barra Bonita, BB) placed in the cascade system of the Tietê River, Brazil. The model was evaluated using the simulated remote sensing reflectance based on the Ocean and Land Color Instrument/Sentinel-3A and the Operational Land Imager/Landsat-8 from both reservoirs. Three quasianalytical algorithm (QAA) versions (QAAv5, QAAM14, and QAAW16) were evaluated to derive the absorption and backscattering coefficients, and then used for ZSD retrieval. For BB, where the chlorophyll-a concentration exceeded 200  mg m  −  3, the model based on QAAv5 showed high uncertainties while the QAAW16, which was originally parameterized for BB showed better performance regarding the ZSD retrieval (mean absolute percentage errors—MAPE of 22%). However, QAAW16 did not perform satisfactorily for Ibi, which is dominated by colored dissolved organic matter (CDOM). For Ibi, QAAv5 provided the best result with MAPE of 34.60%, followed by QAAM14 with 34.65%. QAA-based ZSD models tend to perform poorly in waters with high concentration of chlorophyll-a possibly due to phytoplankton package effect, whereas the same models may require additional parameterization in waters dominated by CDOM. Landsat-8 data showed significant potential for ZSD retrieval in inland waters.

Keywords: zsd; retrieval; secchi disk; organic matter; dissolved organic; disk depth

Journal Title: Journal of Applied Remote Sensing
Year Published: 2018

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.