This paper presents a classification approach, its expected performance, the dependence of this performance on radar system performance, and expected ice scattering characteristics. Results using both aircraft and simulated ERS-1 SAR data are presented. The results are compared to limited field ice property measurements and coincident passive microwave imagery. An algorithm based on this experimental approach has been implemented in the geophysical processor system at the Alaska SAR Facility for classification of sea ice data in ERS-1 C band SAR data. The importance of an integrated postlaunch program for validation and improvement of this approach is discussed. -from Authors

Wildfires increase runoff and sediment yields that impact downstream ecosystems. While the effects of wildfire on stream water quality are well documented, oceanic responses to wildfire remain poorly understood. Therefore, this study investigated oceanic responses to the 2018 Woolsey Fire using satellite remote sensing and in situ data analyses. We examined 2016-2020 turbidity plume (n = 192) and 2008-2020 fecal indicator bacteria (FIB, n = 15,015) measurements at variable proximity to the Woolsey Fire. Shifts in coastal water quality were more pronounced in the "inside" region, which drained the burn area. The inside region experienced 2018-2019 plume surface area monthly means that were 10 and 9 times greater than 2016-2017 and 2017-2018 monthly means, respectively. Further, linear regressions showed that 2018-2019 three-day precipitation totals produced plumes of greater surface area. We also noted statistically significant increases in the inside region in 2018-2019 total coliform and Enterococcus monthly means that were 9 and 53 times greater than 2008-2018 monthly means, respectively. These results indicate that sediment and microbial inputs to coastal ecosystems can increase substantially post-wildfire at levels relevant to public and environmental health, and underscore the benefit of considering remote sensing and in situ measurements for water quality monitoring.

In recent years, Earth system sciences are urgently calling for innovation on improving accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in many subdomains amid the exponentially accumulated datasets and the promising artificial intelligence (AI) revolution in computer science. This paper presents work led by the NASA Earth Science Data Systems Working Groups and ESIP machine learning cluster to give a comprehensive overview of AI in Earth sciences. It holistically introduces the current status, technology, use cases, challenges, and opportunities, and provides all the levels of AI practitioners in geosciences with an overall big picture and to “blow away the fog to get a clearer vision” about the future development of Earth AI. The paper covers all the majorspheres in the Earth system and investigates representative AI research in each domain. Widely used AI algorithms and computing cyberinfrastructure are briefly introduced. The mandatory steps in a typical workflow of specializing AI to solve Earth scientific problems are decomposed and analyzed. Eventually, it concludes with the grand challenges and reveals the opportunities to give some guidance and pre-warnings on allocating resources wisely to achieve the ambitious Earth AI goals in the future.