Remote estimation of chl-a concentration in turbid productive waters — Return to a simple two-band NIR-red model?

Today the water quality of many inland and coastal waters is compromised by cultural eutrophication in consequence of increased human agricultural and industrial activities. Remote sensing is widely applied to monitor the trophic state of these waters. This study investigates the performance of near infrared-red models for the remote estimation of chlorophyll-a concentrations in turbid productive waters and evaluates several near infrared-red models developed within the last 34 years. Three models were calibrated for a dataset with chlorophyll-a concentrations from 0 to 100 mg m−3 and validated for independent and statistically different datasets with chlorophyll-a concentrations from 0 to 100 mg m−3 and 0 to 25 mg m−3 for the spectral bands of the MEdium Resolution Imaging Spectrometer (MERIS) and MODerate resolution Imaging Spectroradiometer (MODIS). The MERIS two-band model estimated chlorophyll-a concentrations slightly more accurately than the more complex models, with mean absolute errors of 2.3 mg m−3 for chlorophyll-a concentrations from 0 to 100 mg m−3 and 1.2 mg m−3 for chlorophyll-a concentrations from 0 to 25 mg m−3. Comparable results from several near infrared-red models with different levels of complexity, calibrated for inland and coastal waters around the world, indicate a high potential for the development of a simple universally applicable near infrared-red algorithm.

► NIR-red models have a great potential for chlorophyll-a estimation.
► Three NIR-red models with MERIS and MODIS bands were calibrated and validated.
► Two-band NIR-red algorithm was very accurate in estimating chlorophyll-a.
► Mean absolute estimation error was only 1.2 mg m−3 for chlorophyll-a ≤ 25 mg m−3.