4.7 Article

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

期刊

REMOTE SENSING
卷 13, 期 15, 页码 -

出版社

MDPI
DOI: 10.3390/rs13152874

关键词

Google Earth Engine; Sentinel-2; water quality; chlorophyll-a; Trophic State Index; Earth Engine App

资金

  1. Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-Brasil (CAPES) [001]
  2. Remote Sensing Program, Graduate Division, National Institute for Space Research (INPE)

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This paper discusses the development of a water quality monitoring system based on remote sensing imagery, specifically focusing on creating a cloud-computing interface on Google Earth Engine. This system allows users to access algae bloom related products with high spatial and temporal resolution. The proposed methodology uses Sentinel-2 images to generate an image collection of the Normalized Difference Chlorophyll-a Index, estimating Chl-a concentration and Trophic State Index.
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 (similar to 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 Sao Paulo/Brazil. The application will be adapted to extend to other relevant areas in Latin America.

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