Analysis of MERIS Reflectance Algorithms for Estimating Chlorophyll-a Concentration in a Brazilian Reservoir

Remote Sensing

Pétala B. Augusto-Silva⁴  Igor Ogashawara⁴  Cláudio C. F. Barbosa²  Lino A. S. de Carvalho⁴  Daniel S. F. Jorge⁴  Celso Israel Fornari³  José L. Stech⁴  Deepak Mishra¹ 

[1] Remote Sensing Division, National Institute for Space Research, Avenida dos Astronautas, 1758, São José dos Campos SP 12227-010, Brazil;;Image Processing Division, National Institute for Space Research, Avenida dos Astronautas, 1758, São José dos Campos SP 12227-010, Brazil; E-Mail:;Associate Laboratory of Sensors and Materials, National Institute for Space Research, Avenida dos Astronautas, 1758, São José dos Campos SP 12227-010, Brazil; E-Mail:;Remote Sensing Division, National Institute for Space Research, Avenida dos Astronautas, 1758, São José dos Campos SP 12227-010, Brazil; E-Mails:

关键词: chlorophyll-a;    remote sensing reflectance;    bio-optical models;    MERIS;    OLCI;   

DOI  :  10.3390/rs61211689

来源: mdpi

【 摘 要 】

Chlorophyll-a (chl-a) is a central water quality parameter that has been estimated through remote sensing bio-optical models. This work evaluated the performance of three well established reflectance based bio-optical algorithms to retrieve chl-a from in situ hyperspectral remote sensing reflectance datasets collected during three field campaigns in the Funil reservoir (Rio de Janeiro, Brazil). A Monte Carlo simulation was applied for all the algorithms to achieve the best calibration. The Normalized Difference Chlorophyll Index (NDCI) got the lowest error (17.85%). The in situ hyperspectral dataset was used to simulate the Ocean Land Color Instrument (OLCI) spectral bands by applying its spectral response function. Therefore, we evaluated its applicability to monitor water quality in tropical turbid inland waters using algorithms developed for MEdium Resolution Imaging Spectrometer (MERIS) data. The application of OLCI simulated spectral bands to the algorithms generated results similar to the in situ hyperspectral: an error of 17.64% was found for NDCI. Thus, OLCI data will be suitable for inland water quality monitoring using MERIS reflectance based bio-optical algorithms.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.