This paper explores the evolution of geoscientific inquiry, tracing the progression from
traditional physics-based models to modern data-driven approaches facilitated by significant …

This paper begins with a review of the history of remote sensing algorithms for the
determination of particulate inorganic carbon (PIC; aka calcium carbonate), primarily …

The biological sciences community is increasingly recognizing the value of open,
reproducible and transparent research practices for science and society at large. Despite …

Recent progress in self-supervision has shown that pre-training large neural networks on
vast amounts of unsupervised data can lead to substantial increases in generalization to …

The introduction of deep learning (DL)(LeCun et al., 2015) into hydrology around 2016–
2018 (Tao et al., 2016; Laloy et al., 2017, 2018; Shen, 2018; Shen et al., 2018), especially …

Satellite data has the potential to inspire a seismic shift for machine learning--one in which
we rethink existing practices designed for traditional data modalities. As machine learning …

Remote sensing datasets pose a number of interesting challenges to machine learning
researchers and practitioners, from domain shift (spatially, semantically and temporally) to …

Satellite data has the potential to inspire a seismic shift for machine learning---one in which
we rethink existing practices designed for traditional data modalities. As machine learning …

The need for adequate data on the spatial and temporal variability of freshwater resources is
a significant challenge to the water managers of the world in water resource planning and …

This article is composed of three independent commentaries about the state of Integrated,
Coordinated, Open, Networked (ICON) principles in the American Geophysical Union …