Performance Analysis of MODIS 500-m Spatial Resolution Products for Estimating Chlorophyll-a Concentrations in Oligo- to Meso-Trophic Waters Case Study: Itumbiara Reservoir, Brazil

Remote Sensing

Igor Ogashawara⁵  Enner H. Alcântara³  Marcelo P. Curtarelli⁵  Marcos Adami²  Renata F. F. Nascimento⁴  Arley F. Souza¹  José L. Stech⁵ 

[1] ETEP Faculdades, Avenida Barão Rio Branco, 882, São José dos Campos-SP 12242-800, Brazil; E-Mail:;Regional Center for the Amazon, National Institute for Space Research (INPE), Parque da Ciência e Tecnologia do Guamá, Belém-PA 2651, Brazil; E-Mail:;Cartography Engineering Department, State University of São Paulo, Rua Roberto Simonsen, 305, Presidente Prudente-SP 19060-900, Brazil; E-Mail:;Geopixel-Soluções em Geotecnologias, Rua Maestro Egydio Pinto, 190, São José dos Campos-SP 12245-902, Brazil; E-Mail:;Remote Sensing Division, National Institute for Space Research (INPE), Avenida dos Astronautas, São José dos Campos-SP 1758, Brazil; E-Mails:

关键词: chlorophyll-a;    bio-optic modeling;    time-series;    MODIS;   

DOI  :  10.3390/rs6021634

来源: mdpi

【 摘 要 】

Monitoring chlorophyll-a (chl-a) concentrations is important for the management of water quality, because it is a good indicator of the eutrophication level in an aquatic system. Thus, our main purpose was to develop an alternative technique to monitor chl-a in time and space through remote sensing techniques. However, one of the limitations of remote sensing is the resolution. To achieve a high temporal resolution and medium space resolution, we used the Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m reflectance product, MOD09GA, and limnological parameters from the Itumbiara Reservoir. With these data, an empirical (O14a) and semi-empirical (O14b) algorithm were developed. Algorithms were cross-calibrated and validated using three datasets: one for each campaign and a third consisting of a combination of the two individual campaigns. Algorithm O14a produced the best validation with a root mean square error (RMSE) of 30.4%, whereas O14b produced an RMSE of 32.41% using the mixed dataset calibration. O14a was applied to MOD09GA to build a time series for the reservoir for the year of 2009. The time-series analysis revealed that there were occurrences of algal blooms in the summer that were likely related to the additional input of nutrients caused by rainfall runoff. During the winter, however, the few observed algal blooms events were related to periods of atmospheric meteorological variations that represented an enhanced external influence on the processes of mixing and stratification of the water column. Finally, the use of remote sensing techniques can be an important tool for policy makers, environmental managers and the scientific community with which to monitor water quality.

【 授权许可】

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