Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine
1
Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA
2
College of Marine Science, University of South Florida, St. Petersburg, FL 33701, USA
3
Department of Geography, University of Alabama, Tuscaloosa, AL 35487, USA
4
Department of Geography and GIScience, University of Cincinnati, Cincinnati, OH 45221, USA
5
U.S. Army Corps of Engineers, ERDC, JALBTCX, Kiln, MS 39556, USA
6
U.S. Army Corps of Engineers, Great Lakes and Ohio River Division, Cincinnati, OH 45202, USA
7
U.S. Army Corps of Engineers, Louisville District, Water Quality, Louisville, KY 40202, USA
8
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)
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.
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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.
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