AlgaeMAp: Algae Bloom Monitoring Application for Inland Waters in Latin America

Abstract

Due to increasing algae bloom occurrence and water degradation on a global scale, there is a demand for water quality monitoring systems based on remote sensing imagery. This paper describes the scientific, theoretical, and methodological background for creating a cloud-computing interface on Google Earth Engine (GEE) which allows end-users to access algae bloom related products with high spatial (30 m) and temporal (~5 day) resolution. The proposed methodology uses Sentinel-2 images corrected for atmospheric and sun-glint effects to generate an image collection of the Normalized Difference Chlorophyll-a Index (NDCI) for the entire time-series. NDCI is used to estimate both Chl-a concentration, based on a non-linear fitting model, and Trophic State Index (TSI), based on a tree-decision model classification into five classes. Once the Chl-a and TSI algorithms had been calibrated and validated they were implemented in GEE as an Earth Engine App, entitled Algae Bloom Monitoring Application (AlgaeMAp). AlgaeMAp is the first online platform built within the GEE platform that offers high spatial resolution of water quality parameters. The App benefits from the huge processing capability of GEE that allows any user with internet access to easily extract detailed spatial (30 m) and long temporal Chl-a and TSI information (from August 2015 and with images every 5 days) throughout the most important reservoirs in the State of São Paulo/Brazil. The application will be adapted to extend to other relevant areas in Latin America.

Detection and Mapping of Eutrophication and Aquatic Plants in a River in the Northeast Region of Brazil Using Sentinel-2 Data

“…According to these results, the relationship between the B04 and B05 bands, is the most suitable for the recovery of chlorophyll-a in the Poti River, which is in conjunction with observations made by Pereira et al (2020). NDCI values obtained from the Sentinel-2 showed good results in the Tietê River Basin (Lobo et al, 2021).…”

Spatio-temporal water quality variability and regionalization of tropical reservoirs based on Landsat imagery

“…This is reflected in the growing number of publications with a wide variety of applications for the GEE, including water quality remote sensing 18 – 23 …”

“…Remote sensing has been applied in reservoirs worldwide to assess spatial and temporal variability of bio-optical parameters and temperature of the water to determine water quality 8 , 22 , 24 – 30 In Brazil, there has been a growing effort to study continental waters, always combining in situ measurements with the processing of satellite images 4 , 31 …”

“…This is reflected in the growing number of publications with a wide variety of applications for the GEE, including water quality remote sensing. [18][19][20][21][22][23] Remote sensing has been applied in reservoirs worldwide to assess spatial and temporal variability of bio-optical parameters and temperature of the water to determine water quality. 8,22,[24][25][26][27][28][29][30] In Brazil, there has been a growing effort to study continental waters, always combining in situ measurements with the processing of satellite images.…”

The Estimation of Water Quality Parameters in Lentic Environments Through Remote Sensing Technologies: a Review of the Past Two Decades

“…GEE is a remote sensing information portal launched in 2010 that consists in a data catalogue providing time series of satellite images from various missions as well as vector data allowing to apply processing algorithms developed by the user or the remote sensing community (GORELICK et al, 2017;MUTANGA, 2018). Especially in the last five years, a number of studies have been carried out using GEE to monitor WQ parameters such as TSM (MARKERT et al, 2018;CAO et al, 2019;CABALLERO;NAVARRO, 2021), SDD (PAGE;OLMANSON;MISHRA, 2019;LIU et al, 2020) and chla (LIN et al, 2018;WANG et al, 2020;CABALLERO;NAVARRO, 2021;LOBO et al, 2021).…”