PROJECT TITLE :

Issues and Potential Improvement of Multiband Models for Remotely Estimating Chlorophyll-a in Complex Inland Waters

ABSTRACT:

Remote estimation of chlorophyll-a (chl-a) in advanced freshwaters remains a challenging drawback due to the speedy spatial variability and wide selection as influenced by terrestrial constituents. A controversial issue is whether or not or not 2-B models possess sufficient wavelength data for accurately estimating Chl-a concentrations from remote sensing knowledge for freshwater environments. This study introduced a systemic approach and proved that adding extra wavelength data to 2-B model might not considerably improve the estimation of freshwater chl-a, but acted to increase model uncertainty. This convincing solution was primarily based on a massive synthetic data set (38 937 samples) combined with a group of in situ information (51 samples) collected in 3 cruises in Lake Huron. The artificial data set has 2 distinct features: one) large knowledge things and 2) covers a broad vary of chl-a (zero-a thousand mg/m3), coloured dissolved organic matter (CDOM) (0-50 m-one), and NAP (nonalgal particles) (zero-500 mg/l). Additionally, this study reveals how hyperspectral wavelength selection, number of bands, bandwidth, and parameter calibration are associated with the uncertainty in remote sensing of chl-a. The systematic analysis approach was used to guage 34 chl-a algorithms by using optimal location and range of wavelengths along with calibrated parameters. The study introduced a collection of recent 2-B, 3-B, and 4-B models derived conjointly from using optimized parameters, suggested wavelengths, and bands on the market in MERIS and MODIS satellite images. Validation results demonstrated that these models are appropriate to general freshwater environments because of broad ranges of biochemical and physical properties in each synthetic and in situ information.


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