研究目的
Evaluating the performance and potential of the OLCI/Sentinel-3A in retrieving the IOPs in a non-productive tropical reservoir, with specific objectives to validate existing QAAs, re-parameterize key steps, propose an alternative method for aφ estimation, link IOP variability to physical and meteorological conditions, and characterize factors affecting bio-optical properties.
研究成果
The re-parameterized QAAOMW significantly improved the accuracy of IOP retrieval in inorganic matter-dominated inland waters, reducing errors in total absorption, CDOM and detritus absorption, and phytoplankton absorption. The model shows promise for monitoring water quality in oligo-to-mesotrophic environments, but further refinements are needed, especially for aφ estimation and scaling up to satellite sensors like OLCI.
研究不足
The study is limited to a specific oligo-to-mesotrophic reservoir in Brazil, and the model's performance may vary in other environments. The retrieval of phytoplankton absorption (aφ) remains challenging in low chlorophyll-a waters. The use of simulated OLCI data instead of actual satellite data due to unavailability during the study period, and the need for additional tuning and validation with data from broader geographic regions.
1:Experimental Design and Method Selection:
The study involved evaluating and re-parameterizing quasi-analytical algorithms (QAAs) for retrieving inherent optical properties (IOPs) in oligo-to-mesotrophic inland waters. The methodology included field data collection, laboratory analysis, and algorithm modifications based on radiative transfer theory and empirical steps.
2:Sample Selection and Data Sources:
Water samples and radiometric data were collected from the Nova Avanhandava reservoir in Brazil during three field trips in 2014 and 2016, totaling 51 samples for calibration, validation, and temporal comparison.
3:List of Experimental Equipment and Materials:
Hyperspectral radiometers (RAMSES TriOS?), spectrophotometer (Shimadzu UV-2600 UV-Vis), filters (Whatman GF/F), cuvettes, and other laboratory equipment for water quality analysis.
4:Experimental Procedures and Operational Workflow:
Radiometric measurements were taken between 10 a.m. and 2 p.m. using RAMSES sensors to estimate remote sensing reflectance (Rrs). Water samples were collected, filtered, and analyzed for chlorophyll-a, suspended particulate matter, and IOPs using standard methods. The QAA algorithms were applied and modified, with data convolved to OLCI bands for simulation.
5:Data Analysis Methods:
Statistical indicators such as root mean square difference (RMSD), mean absolute percentage error (MAPE), normalized bias (B*), normalized standard deviation (σ*), linear correlation (R), and normalized unbiased RMSD (uRMSD*) were used for validation. Taylor and target diagrams were employed for error analysis.
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