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

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.

[1]  Jean-Michel Martinez,et al.  Atmospheric and sunglint correction for retrieving chlorophyll-a in a productive tropical estuarine-lagoon system using Sentinel-2 MSI imagery , 2021, ISPRS Journal of Photogrammetry and Remote Sensing.

[2]  D. Mishra,et al.  Water quality assessment of the Ganges River during COVID-19 lockdown , 2021, International Journal of Environmental Science and Technology.

[3]  M. Bonansea,et al.  Assessing land use and land cover change in Los Molinos reservoir watershed and the effect on the reservoir water quality , 2021 .

[4]  Rogério Flores Júnior,et al.  Mapping of diffuse attenuation coefficient in optically complex waters of amazon floodplain lakes , 2020 .

[5]  I. Caballero,et al.  Using Copernicus Sentinel-2 and Sentinel-3 data to monitor harmful algal blooms in Southern Chile during the COVID-19 lockdown , 2020, Marine Pollution Bulletin.

[6]  Marcelo Pedroso Curtarelli,et al.  Diffuse Attenuation of Clear Water Tropical Reservoir: A Remote Sensing Semi-Analytical Approach , 2020, Remote. Sens..

[7]  Willibroad Gabila Buma,et al.  Evaluation of Sentinel-2 and Landsat 8 Images for Estimating Chlorophyll-a Concentrations in Lake Chad, Africa , 2020, Remote. Sens..

[8]  I. Caballero,et al.  New capabilities of Sentinel-2A/B satellites combined with in situ data for monitoring small harmful algal blooms in complex coastal waters , 2020, Scientific Reports.

[9]  M. Achkar,et al.  Assessing the origin of a massive cyanobacterial bloom in the Río de la Plata (2019): Towards an early warning system. , 2020, Water research.

[10]  B. Matsushita,et al.  Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach , 2020, Remote Sensing of Environment.

[11]  W. Verhoef,et al.  Integration of in-situ and multi-sensor satellite observations for long-term water quality monitoring in coastal areas , 2020, Remote Sensing of Environment.

[12]  Nadine Gobron,et al.  Validation of Space-Based Albedo Products from Upscaled Tower-Based Measurements Over Heterogeneous and Homogeneous Landscapes , 2020, Remote. Sens..

[13]  N. Ha,et al.  Inland harmful cyanobacterial bloom prediction in the eutrophic Tri An Reservoir using satellite band ratio and machine learning approaches , 2020, Environmental Science and Pollution Research.

[14]  Carolline Cairo,et al.  Hybrid Chlorophyll-a Algorithm for Assessing Trophic States of a Tropical Brazilian Reservoir Based on MSI/Sentinel-2 Data , 2019, Remote. Sens..

[15]  D. Mishra,et al.  Risks for cyanobacterial harmful algal blooms due to land management and climate interactions. , 2019, The Science of the total environment.

[16]  J C Ho,et al.  Widespread global increase in intense lake phytoplankton blooms since the 1980s , 2019, Nature.

[17]  Rencai Dong,et al.  Long-Term Spatial and Temporal Monitoring of Cyanobacteria Blooms Using MODIS on Google Earth Engine: A Case Study in Taihu Lake , 2019, Remote. Sens..

[18]  Thanan Rodrigues,et al.  Mapping the chlorophyll-a horizontal gradient in a cascading reservoirs system using MSI Sentinel-2A images , 2019, Advances in Space Research.

[19]  Jun Ma,et al.  Increasing Outbreak of Cyanobacterial Blooms in Large Lakes and Reservoirs under Pressures from Climate Change and Anthropogenic Interferences in the Middle-Lower Yangtze River Basin , 2019, Remote. Sens..

[20]  Felipe de Lucia Lobo,et al.  Retrieving Total and Inorganic Suspended Sediments in Amazon Floodplain Lakes: A Multisensor Approach , 2019, Remote. Sens..

[21]  Quinten Vanhellemont,et al.  Adaptation of the dark spectrum fitting atmospheric correction for aquatic applications of the Landsat and Sentinel-2 archives , 2019, Remote Sensing of Environment.

[22]  C. Giardino,et al.  Assessment of atmospheric correction algorithms for the Sentinel-2A MultiSpectral Imager over coastal and inland waters , 2019, Remote Sensing of Environment.

[23]  Jose Gomez-Dans,et al.  A sensor-invariant atmospheric correction method: application to Sentinel-2/MSI and Landsat 8/OLI , 2019 .

[24]  Robert J. Brederveld,et al.  Towards restoring urban waters: understanding the main pressures , 2019, Current Opinion in Environmental Sustainability.

[25]  V. Brotas,et al.  Temporal patterns of phytoplankton phenology across high latitude lakes unveiled by long-term time series of satellite data , 2019, Remote Sensing of Environment.

[26]  J. S. Arcanjo,et al.  Continental-scale surface reflectance product from CBERS-4 MUX data: Assessment of atmospheric correction method using coincident Landsat observations , 2018, Remote Sensing of Environment.

[27]  Erin A. Urquhart,et al.  Mobile device application for monitoring cyanobacteria harmful algal blooms using Sentinel-3 satellite Ocean and Land Colour Instruments , 2018, Environ. Model. Softw..

[28]  T. Rodrigues,et al.  Remote sensing of the chlorophyll-a based on OLI/Landsat-8 and MSI/Sentinel-2A (Barra Bonita reservoir, Brazil). , 2018, Anais da Academia Brasileira de Ciencias.

[29]  Jennifer N. Hird,et al.  Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping , 2017, Remote. Sens..

[30]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[31]  Emmanuel Branche,et al.  The multipurpose water uses of hydropower reservoir: The SHARE concept , 2017 .

[32]  Cláudio C. F. Barbosa,et al.  SNR (Signal-To-Noise Ratio) Impact on Water Constituent Retrieval from Simulated Images of Optically Complex Amazon Lakes , 2017, Remote. Sens..

[33]  R. Congalton,et al.  Automated cropland mapping of continental Africa using Google Earth Engine cloud computing , 2017 .

[34]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[35]  C. Barbosa,et al.  Parametrization and calibration of a quasi-analytical algorithm for tropical eutrophic waters , 2016 .

[36]  S. Matthews.  Novel treatment solutions turns industrial wastewater into commodity : industrial water treatment - feature , 2016 .

[37]  W. Dodds,et al.  Land use influence on raw surface water quality and treatment costs for drinking supply in Sao Paulo State (Brazil) , 2016 .

[38]  Nilton Nobuhiro Imai,et al.  Estimation of Chlorophyll-a Concentration and the Trophic State of the Barra Bonita Hydroelectric Reservoir Using OLI/Landsat-8 Images , 2015, International journal of environmental research and public health.

[39]  E. Novo,et al.  Time-series analysis of Landsat-MSS/TM/OLI images over Amazonian waters impacted by gold mining activities , 2015 .

[40]  Igor Ogashawara,et al.  Analysis of MERIS Reflectance Algorithms for Estimating Chlorophyll-a Concentration in a Brazilian Reservoir , 2014, Remote. Sens..

[41]  D. Mishra,et al.  Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters , 2012 .

[42]  A. Wüest,et al.  Impact of a large tropical reservoir on riverine transport of sediment, carbon, and nutrients to downstream wetlands , 2011 .

[43]  Juan C. Jiménez-Muñoz,et al.  Atmospheric correction of optical imagery from MODIS and Reanalysis atmospheric products , 2010 .

[44]  A. Gitelson,et al.  A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation , 2008 .

[45]  Menghua Wang,et al.  The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing. , 2007, Optics express.

[46]  J. Gower,et al.  Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor , 2004 .

[47]  Anatoly A. Gitelson,et al.  The peak near 700 nm on radiance spectra of algae and water: relationships of its magnitude and position with chlorophyll concentration , 1992 .

[48]  E. Alcântara,et al.  A single semi-analytical algorithm to retrieve chlorophyll-a concentration in oligo-to-hypereutrophic waters of a tropical reservoir cascade , 2021 .

[49]  Thorp and Covich's Freshwater Invertebrates , 2019, Thorp and Covich's Freshwater Invertebrates.

[50]  Conduto António Diana Sofia,et al.  Algal bloom and its economic impact , 2016 .

[51]  D. Roy,et al.  Continental-scale Validation of MODIS-based and LEDAPS Landsat ETM+ Atmospheric Correction Methods , 2012 .

[52]  V. Istvánovics.  Eutrophication of Lakes and Reservoirs , 2009 .

[53]  K. Shadan,et al.  Available online: , 2012 .