Bio-Optical Modeling in a Tropical Hypersaline Lagoon Environment

In this chapter, we attempted to present an overview of the use of remote sensing to monitor water quality parameters, mainly chlorophyll-a (chl-a) and turbidity. We summarized the main concepts of bio-optical modeling and presented a case study of the application of the Hyperspectral Imager for the Coastal Ocean (HICO) for the monitoring of water quality in a tropical hypersaline aquatic environment. Using HICO, we evaluated a set of different semi-empirical bio-optical algorithms for chl-a and turbidity estimation developed for inland and oceanic waters in the Araruama Lagoon, RJ, Brazil, which is an extreme environment due to its high salinity values. We also developed an empirical algorithm for both water quality parameters and compared the performances. Results showed that for chl-a estimation all models have a low performance with a normalized root mean square error (NRMSE) varying from 24.13 to 30.46. For turbidity, the bio-optical algorithms showed a better performance with the NRMSE between 15.49 and 28.04. Overall, these results highlight the importance of including extreme environments, such as the Araruama Lagoon, on the validation of bio-optical algorithms as well as the need for new orbital hyperspectral sensors which will improve the development of the field

Influence of summertime mesoscale convective systems on the heat balance and surface mixed layer dynamics of a large Amazonian hydroelectric reservoir

We evaluated the impacts of summertime mesoscale convective systems (MCS) on the heat balance and diel surface mixed layer (SML) dynamics of the Brazilian Amazon's Tucuruí Hydroelectric Reservoir (THR). We used a synergistic approach that combines in situ data, remote sensing data, and three-dimensional (3-D) modeling to investigate the typical behavior of the components of the heat balance and the SML dynamics. During the study period (the austral summer of 2012–2013), 22 days with MCS activity were identified. These events occurred approximately every 4 days, and they were most frequent during January (50% of the observations). An analysis of local meteorological data showed that when MCS occur, the environmental conditions at THR change significantly (p-value < 0.01). The net longwave flux, which was the heat balance component most strongly impacted by MCS, increased more than 32% on days with MCS activity. The daily integrated heat balance became negative (−54 W m−2) on MCS days, while the balance was positive (19 W m−2) on non-MCS days. In response to the changes in the heat balance, the SML dynamics changed when a MCS was over the THR. The SML depth was typically 28% higher on the days with MCS (∼1.6 m) compared with the days without MCS (∼1.3 m). The results indicate that MCS are one of the main meteorological disturbances driving the heat balance and the mixing dynamics of Amazonian hydroelectric reservoirs during the summer. These events may have implications for the water quality and greenhouse gas emissions of Amazonian reservoirs

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

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