Simultaneous Measurements of Chlorophyll Concentration by Lidar, Fluorometry, above-Water Radiometry, and Ocean Color MODIS Images in the Southwestern Atlantic

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/m3. 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.

Evaluation of MODIS Products in the Great Australian Bight and Adjacent Coastal Waters

“…To generate a locally improved algorithm, sampling efforts should be directed to collecting a comprehensive bio-optical dataset, including in situ remote sensing reflectance and spectral absorption coefficients, that would help to determine whether the performance of the products is related to the effect of the spectral ratios considered or to the determination of the spectral contributions of different seawater constituents [8] [9] [58].…”

Laser Remote Sensing of Lake Kinneret by Compact Fluorescence LiDAR

“…In recent years, phytoplankton distribution monitoring by various techniques (satellite image processing [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ], radar probing [ 11 ], laser remote sensing [ 12 , 13 , 14 , 15 , 16 , 17 ] and contact measurements with sample analysis [ 18 , 19 , 20 , 21 ]) has revealed an increased contamination of freshwater [ 22 , 23 ] and seawater areas [ 24 ] by blue–green algae. Phytoplankton blooms deplete dissolved oxygen, thus suppressing biodiversity in shallow waters and also poisoning them with biotoxins.…”