This article is
- freely available
Simultaneous Measurements of Chlorophyll Concentration by Lidar, Fluorometry, above-Water Radiometry, and Ocean Color MODIS Images in the Southwestern Atlantic
Instituto Nacional de Pesquisas Espaciais (INPE), PO Box 515, 12201-970, São José dos Campos, SP, Brazil
PETROBRAS/CENPES, Cidade Universitária, Q.7, Ilha do Fundão, 21949-900, Rio de Janeiro, RJ, Brazil
Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), PO Box 38008, 22453-900, Rio de Janeiro, RJ, Brazil
Universidade Federal do Rio de Janeiro (UFRJ), Cidade Universitária, Av. Pau Brasil 211, Ilha do Fundão, 21941-590, Rio de Janeiro, RJ, Brazil
* Author to whom correspondence should be addressed.
Received: 27 November 2008; in revised form: 8 January 2009 / Accepted: 15 January 2009 / Published: 16 January 2009
Abstract: Comparisons between in situ measurements of surface chlorophyll-a concentration (CHL) and ocean color remote sensing estimates were conducted during an oceanographic cruise on the Brazilian Southeastern continental shelf and slope, Southwestern South Atlantic. In situ values were based on fluorometry, above-water radiometry and lidar fluorosensor. Three empirical algorithms were used to estimate CHL from radiometric measurements: Ocean Chlorophyll 3 bands (OC3MRAD), Ocean Chlorophyll 4 bands (OC4v4RAD), and Ocean Chlorophyll 2 bands (OC2v4RAD). The satellite estimates of CHL were derived from data collected by the MODerate-resolution Imaging Spectroradiometer (MODIS) with a nominal 1.1 km resolution at nadir. Three algorithms were used to estimate chlorophyll concentrations from MODIS data: one empirical - OC3MSAT, and two semi-analytical - Garver, Siegel, Maritorena version 01 (GSM01SAT), and CarderSAT. In the present work, MODIS, lidar and in situ above-water radiometry and fluorometry are briefly described and the estimated values of chlorophyll retrieved by these techniques are compared. The chlorophyll concentration in the study area was in the range 0.01 to 0.2 mg·m-3. In general, the empirical algorithms applied to the in situ radiometric and satellite data showed a tendency to overestimate CHL with a mean difference between estimated and measured values of as much as 0.17 mg/m3 (OC2v4RAD). The semi-analytical GSM01 algorithm applied to MODIS data performed better (rmse 0.28, rmse-L 0.08, mean diff. -0.01 mg/m3) than the Carder and the empirical OC3M algorithms (rmse 1.14 and 0.36, rmse-L 0.34 and 0.11, mean diff. 0.17 and 0.02 mg/m3, respectively). We find that rmsd values between MODIS relative to the in situ radiometric measurements are < 26%, i.e., there is a trend towards overestimation of RRS by MODIS for the stations considered in this work. Other authors have already reported over and under estimation of MODIS remotely sensed reflectance due to several errors in the bio-optical algorithm performance, in the satellite sensor calibration, and in the atmospheric-correction algorithm.
Keywords: Chlorophyll; Lidar; MODIS; Above-water radiometry; Fluorometry
Article StatisticsClick here to load and display the download statistics.
Notes: Multiple requests from the same IP address are counted as one view.
Cite This Article
MDPI and ACS Style
Kampel, M.; Lorenzzetti, J.A.; Bentz, C.M.; Nunes, R.A.; Paranhos, R.; Rudorff, F.M.; Politano, A.T. Simultaneous Measurements of Chlorophyll Concentration by Lidar, Fluorometry, above-Water Radiometry, and Ocean Color MODIS Images in the Southwestern Atlantic. Sensors 2009, 9, 528-541.
Kampel M, Lorenzzetti JA, Bentz CM, Nunes RA, Paranhos R, Rudorff FM, Politano AT. Simultaneous Measurements of Chlorophyll Concentration by Lidar, Fluorometry, above-Water Radiometry, and Ocean Color MODIS Images in the Southwestern Atlantic. Sensors. 2009; 9(1):528-541.
Kampel, Milton; Lorenzzetti, João A.; Bentz, Cristina M.; Nunes, Raul A.; Paranhos, Rodolfo; Rudorff, Frederico M.; Politano, Alexandre T. 2009. "Simultaneous Measurements of Chlorophyll Concentration by Lidar, Fluorometry, above-Water Radiometry, and Ocean Color MODIS Images in the Southwestern Atlantic." Sensors 9, no. 1: 528-541.