Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units,

Landsat Thematic Mapper data were acquired for four¯yby dates that coincided with ® eld data collection of chlorophyll a and Secchi disc depth for Bull Shoals Reservoir, Arkansas, USA. Predictive models were developed for chlorophyll a from July 1994 (R 2 = 0.80) and December 1994 (R 2 = 0.84) data and for Secchi disc depth (R 2 = 0.96) from February 1995 data. These models were then tested using historic chlorophyll a data compiled from the previous ten years. The July 1994 model satisfactorily predicted chlorophyll a levels in six of the ten years and this improved to ® ve out of ® ve years when only summer seasons were considered. Phytoplankton analyses suggested an in¯uence on algorithm development with respect to species composition at the time of satellite¯yby.

Machine learning has found many applications in remote sensing. These applications range from retrieval algorithms to bias correction, from code acceleration to detection of disease in crops, from classification of pelagic habitats to rock type classification. As a broad subfield of artificial intelligence, machine learning is concerned with algorithms and techniques that allow computers to "learn" by example. The major focus of machine learning is to extract information from data automatically by computational and statistical methods. Over the last decade there has been considerable progress in developing a machine learning methodology for a variety of Earth Science applications involving trace gases, retrievals, aerosol products, land surface products, vegetation indices, and most recently, ocean applications. In this chapter, we will review some examples of how machine learning has already been useful for remote sensing and some likely future applications.

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