Abstract
Recently, the Poti river mouth region has experienced environmental impacts that resulted in a change of landscape in its dry season, highlighting the eutrophication and proliferation of phytoplankton, algae, cyanobacteria and aquatic plants. Considering the aspects related to water-quality monitoring in the semiarid region of Brazil from remote sensing, this study aimed to evaluate the performance of Sentinel-2A satellite data in the retrieval of chlorophyll-a concentration in Poti River in Teresina, Piaui, Brazil. The chlorophyll-a concentration retrieval and mapping methodology involved the study of the water surface reflectance in Sentinel-2A images and their correlation with the chlorophyll-a data collected in situ during the years 2016 and 2017. The results generated by the Chl-1, Ha et al. (2017), Chl-2, Page et al. (2018), and Chl-3, Kuhn et al. (2019) equations show the need for calibrating the algorithms used for the Poti River water components. However, the empirical algorithm Chl-2 shows a correlation has been established to identify the spatiotemporal variation of chlorophyll-a concentration along the Poti River broadly and not punctually. The spatial distribution of this pigment in maps derived from Sentinel-2A is consistent with the pattern of occurrence determined by the in situ data. Therefore, the MSI sensor proved to be a tool suitable for the retrieval and monitoring of chlorophyll-a concentration along the Poti River.
Highlights
Development in large urban centres, devoid of any planning and with increasing levels of environmental degradation, drastically affects water availability and especially its quality (Vargas et al, 2018)
This study evaluated the performance of the Sentinel-2A satellite data in the retrieval of chlorophyll-a concentrations in Poti River in Teresina, Piaui, Brazil
This study is the first attempt to evaluate the use of Sentinel-2 in Poti River remote sensing
Summary
Development in large urban centres, devoid of any planning and with increasing levels of environmental degradation, drastically affects water availability and especially its quality (Vargas et al, 2018). Water-quality monitoring is a challenging process as data collection is insufficient or nonexistent for most bodies of water. Punctual samples do not always portray the dynamics of water constituents, because the river is a highly dynamic lotic ecosystem that requires the collection of a large number of quality parameters to understand spatiotemporal variations and monitor changes (Prasad et al, 2018; Kuhn et al, 2019; Martins, 2019). Some authors have adopted an efficient approach that integrates diverse in situ data with remote sensing images to monitor the factors that affect water quality and understand the limnological processes, because satellite imagery provide the synoptic, continuous and long-term observation (Ha et al, 2017; Pinardi et al, 2018; Martins, 2019)
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