Accurate remote assessment of phytoplankton chlorophyll a (chla) concentration is particularly challenging in turbid, productive waters. Recently a conceptual model containing reflectance in three spectral bands in the red and near infra-red range of the spectrum was suggested for retrieving chla concentrations in turbid productive waters; it was calibrated and validated in lakes and reservoirs in Nebraska and Iowa. The objective of this paper is to evaluate the performance of this three band model as well as its special case, the two-band model to estimate chla concentration in Chesapeake Bay, as representative of estuarine Case II waters, and to assess the accuracy of chla retrieval. To evaluate the model performance, dual spectroradiometers were used to measure subsurface spectral radiance reflectance in the visible and near infra-red range of the spectrum. Water samples were collected concurrently and contained widely variable chla (9 to 77.4 mg/m3) and total suspended solids (7-65 mg/L dry wt). Colored dissolved organic matter (CDOM) absorption at 440 nm was 0.20 to 2.50 m- 1; Secchi disk transparency ranged from 0.28 to 1.5 m. The two- and three-band models were spectrally tuned to select the spectral bands for most accurate chla estimation. Strong linear relationships were established between analytically measured chla and both the three-band model [R- 1(675)-R- 1(695)] × R(730) and the two-band model R(720)/R(670), where R(λ) is reflectance at wavelength λ. The three-band model accounted for 81% of variation in chla and allowed estimation of chla with a root mean square error (RMSE) of less than 7.9 mg/m3, whereas the two-band model accounted for 79% of chla variability and RMSE of chla estimation was below 8.4 mg/m3. The three-band model with MERIS spectral bands allows accurate chla estimation with RMSE below 9.1 mg/m3. Two-band model with SeaWiFS bands and MODIS 667 nm and 748 nm bands can estimate chla with RMSE below 11 mg/m3. The findings underlined the rationale behind the conceptual model and demonstrated the robustness of this algorithm for chla retrieval in turbid, productive estuarine waters.
All Science Journal Classification (ASJC) codes
- Soil Science
- Computers in Earth Sciences