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The quality and quantity of water available for both economic growth and life sustainability is one of the major challenges for the sustainable development in the 21st century. This challenge requires research focused on the monitoring of time changes in water properties in several spatial scales. Satellite remote sensing has been applied as an alternative for providing information on optically active components, which act as indicators of water quality. Satellite remote sensing performance, however, varies from one aquatic system to another depending on several factors, such as size, depth, optical properties. This study, therefore, aims to explore the viability of applying remote sensing for monitoring the UHE Mauá reservoir, located in Paraná State. For that, an experiment was carried out to obtain water samples at 24 random samples distributed into the reservoir. Those samples were analyzed in laboratory and optically active components, namely, total suspended solids (TSS) and chlorophyll-a (Chl-a) concentration determined. Surface remote sensing reflectance provided by Landsat/OLI images almost concurrently to satellite overpass was computed for each sample in order to assess the best set of spectral bands and band combinations for estimating the concentrations of TSS and Chl-a. Results indicate that Chl-a was the optically active component spanning the widest range of variability in the Mauá reservoir and having the highest potential to be estimated using remote sensing OLI band 3 (green) explained more than 70 % in chlorophyll-a concentration. This paper is an extended version of Pereira et al. (2017), presented in XVIII Brazilian Symposium on GeoInformatics (GEOINFO 2017).

ABSTRACT Accurate estimation of chlorophyll-a (Chl-a) concentration in inland waters through remote-sensing techniques is complicated by local differences in the optical properties of water. In this study, we applied multiple linear regression (MLR), artificial neural network (ANN), nonparametric multiplicative regression (NPMR) and four models (Appel, Kahru, FAI and O14a) to estimate the Chl -a concentration from combinations of spectral bands from the MODIS sensor. The MLR, NPMR and ANN models were calibrated and validated using in-situ Chl -a measurements. The results showed that a simple and efficient model, developed and validated through multiple linear regression analysis, offered advantages (i.e., better performance and fewer input variables) in comparison with ANN, NPMR and four models (Appel, Kahru, FAI and O14a). In addition, we observed that in a large shallow subtropical lake, where the wind and hydrodynamics are essential factors in the spatial heterogeneity (Chl-a distribution), the MLR model adjusted using the specific point dataset, performed better than using the total dataset, which suggest that would not be appropriate to generalize a single model to estimate Chl-a in these large shallow lakes from total datasets. Our approach is a useful tool to estimate Chl -a concentration in meso-oligotrophic shallow waters and corroborates the spatial heterogeneity in these ecosystems.