Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data.

MLA

Ruescas, Ana Belen, et al. “Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data.” Remote Sensing, vol. 10, no. 5, May 2018, p. 786. EBSCOhost, https://doi.org/10.3390/rs10050786.

APA

Ruescas, A. B., Hieronymi, M., Mateo-Garcia, G., Koponen, S., Kallio, K., & Camps-Valls, G. (2018). Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data. Remote Sensing, 10(5), 786. https://doi.org/10.3390/rs10050786

Chicago

Ruescas, Ana Belen, Martin Hieronymi, Gonzalo Mateo-Garcia, Sampsa Koponen, Kari Kallio, and Gustau Camps-Valls. 2018. “Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data.” Remote Sensing 10 (5): 786. doi:10.3390/rs10050786.