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

Remote Sensing

卷:12

Min Xu¹  Lei Wang²  Richard Beck³  Hongxing Liu⁴  Yang Liu⁴  Qiusheng Wu⁵  Molly Reif⁶  Erich Emery⁷  Jade Young⁸ 

[1] College of Marine Science, University of South Florida, St. Petersburg, FL 33701, USA;

[2] Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA;

[3] Department of Geography and GIScience, University of Cincinnati, Cincinnati, OH 45221, USA;

[4] Department of Geography, University of Alabama, Tuscaloosa, AL 35487, USA;

[5] Department of Geography, University of Tennessee, Knoxville, TN 37996, USA;

[6] U.S. Army Corps of Engineers, ERDC, JALBTCX, Kiln, MS 39556, USA;

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

[8] U.S. Army Corps of Engineers, Louisville District, Water Quality, Louisville, KY 40202, USA;

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

DOI  :  10.3390/rs12203278

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown