New Article in Eos: Advancing AI for Earth Science: A Data Systems Perspective

A new article authored by members of NASA's Earth Science Data Systems program and the Radiant Earth Foundation was published in the American Geophysical Union's Eos.

By Manil Maskey, Hamed Alemohammad, Kevin J. Murphy, and Rahul Ramachandran

The Earth sciences present uniquely challenging problems, from detecting and predicting changes in Earth’s ecosystems in response to climate change to understanding interactions among the ocean, atmosphere, and land in the climate system. Helping address these problems, however, is a wealth of data sets—containing atmospheric, environmental, oceanographic, and other information—that are mostly open and publicly available. This fortuitous combination of pressing challenges and plentiful data is leading to the increased use of data-driven approaches, including machine learning models, to solve Earth science problems.

Graph shows the yearly number of Earth science papers from major publishers that discuss or use supervised machine learning.
Fig. 1. The yearly number of Earth science papers from major publishers that discuss or use supervised machine learning. AMS = American Meteorological Society, IEEE = Institute of Electrical and Electronics Engineers, SPIE = International Society for Optics and Photonics. Credit: Katrina Virts and Rahul Ramachandran

Machine learning, a type of artificial intelligence (AI) in which computers learn from data, has been applied in many domains of Earth science (Figure 1). Such applications include land cover and land use classification [Jin et al., 2019], precipitation and soil moisture estimation [Kolassa et al., 2018], cloud process representations in climate models [Rasp et al., 2018], crop type detection and crop yield prediction [Wang et al., 2019], estimations of water, carbon, and energy fluxes between the land and atmosphere [Alemohammad et al., 2017], spatial downscaling of satellite observations, ocean turbulence modeling [Sinha, 2019], and tropical cyclone intensity estimation [Pradhan et al., 2018], among others [Zhu et al., 2017].

Read the full article in Eos.

Maskey, M., H. Alemohammad, K. J. Murphy, and R. Ramachandran (2020), Advancing AI for Earth science: A data systems perspective, Eos, 101, doi: 10.1029/2020EO151245. Published on 06 November 2020.

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