Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

Wang, Lei; Xu, Min; Liu, Yang; Liu, Hongxing; Beck, Richard; Reif, Molly; Emery, Erich; Young, Jade; Wu, Qiusheng

doi:10.3390/rs12203278

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Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

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Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA

College of Marine Science, University of South Florida, St. Petersburg, FL 33701, USA

Department of Geography, University of Alabama, Tuscaloosa, AL 35487, USA

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Department of Geography and GIScience, University of Cincinnati, Cincinnati, OH 45221, USA

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U.S. Army Corps of Engineers, ERDC, JALBTCX, Kiln, MS 39556, USA

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U.S. Army Corps of Engineers, Great Lakes and Ohio River Division, Cincinnati, OH 45202, USA

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U.S. Army Corps of Engineers, Louisville District, Water Quality, Louisville, KY 40202, USA

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Department of Geography, University of Tennessee, Knoxville, TN 37996, USA

Author to whom correspondence should be addressed.

Remote Sens. 2020, 12(20), 3278; https://doi.org/10.3390/rs12203278

Received: 31 July 2020 / Revised: 27 September 2020 / Accepted: 7 October 2020 / Published: 9 October 2020

(This article belongs to the Special Issue Google Earth Engine and Cloud Computing Platforms: Methods and Applications in Big Geo Data Science)

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Abstract

Monitoring harmful algal blooms (HABs) in freshwater over regional scales has been implemented through mapping chlorophyll-a (Chl-a) concentrations using multi-sensor satellite remote sensing data. Cloud-free satellite measurements and a sufficient number of matched-up ground samples are critical for constructing a predictive model for Chl-a concentration. This paper presents a methodological framework for automatically pairing surface reflectance values from multi-sensor satellite observations with ground water quality samples in time and space to form match-up points, using the Google Earth Engine cloud computing platform. A support vector machine model was then trained using the match-up points, and the prediction accuracy of the model was evaluated and compared with traditional image processing results. This research demonstrates that the integration of multi-sensor satellite observations through Google Earth Engine enables accurate and fast Chl-a prediction at a large regional scale over multiple years. The challenges and limitations of using and calibrating multi-sensor satellite image data and current and potential solutions are discussed. View Full-Text

Keywords: Google Earth Engine; water quality; freshwater Chl-a; multi-sensor integration

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MDPI and ACS Style

Wang, L.; Xu, M.; Liu, Y.; Liu, H.; Beck, R.; Reif, M.; Emery, E.; Young, J.; Wu, Q. Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine. Remote Sens. 2020, 12, 3278. https://doi.org/10.3390/rs12203278

AMA Style

Wang L, Xu M, Liu Y, Liu H, Beck R, Reif M, Emery E, Young J, Wu Q. Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine. Remote Sensing. 2020; 12(20):3278. https://doi.org/10.3390/rs12203278

Chicago/Turabian Style

Wang, Lei, Min Xu, Yang Liu, Hongxing Liu, Richard Beck, Molly Reif, Erich Emery, Jade Young, and Qiusheng Wu. 2020. "Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine" Remote Sensing 12, no. 20: 3278. https://doi.org/10.3390/rs12203278

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Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

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