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Satellites imaged an underwater volcano erupting — but scientists have no idea what's actually happening on the seafloor
Closely spaced volcanic plumes, surrounded by clouds, stream from a growing underwater volcanic platform in this natural-color image captured by the OLI (Operational Land Imager) on Landsat 9 on May 11, 2026, three days after the eruption began.
Amplified Arctic iceberg traffic reshapes benthic biodiversity
Abstract The Arctic is undergoing rapid warming, resulting in retreating sea ice and glaciers1, yet how cryospheric changes propagate into the deep ocean remains poorly understood2. Here we identify a climate-driven mechanism linking accelerating glacier disintegration to an increase in deep-sea hard-bottom habitats far beyond calving fronts. Seafloor observations in Fram Strait show a localized increase in the density and patchiness of dropstones delivered by debris-laden icebergs.
Satellite images reveals mangroves rebounding worldwide — but here's why they could still 'drown'
Satellite images reveals mangroves rebounding worldwide — but here's why they could still 'drown' A new study finds mangrove forests are no longer shrinking worldwide, offering hope for coastal protection and climate resilience. But other research warns sea level rise could reduce their ability to store carbon. Mangrove forests, long considered among the world's most threatened ecosystems, are now showing signs of global rebound, a new study reports.
Satellites reveal cities' 'urban pulse,' tracking neighborhood growth in near real time
Satellites reveal cities' 'urban pulse,' tracking neighborhood growth in near real time Robert Egan Associate Editor For over a century, doctors have used electrocardiograms (EKGs) to render the invisible electrical activity of the human heart visible, using the pulse to diagnose disease before it becomes fatal. Now, scientists have invented a way to do the exact same thing for the places where most of humanity lives: cities. In a recent study published in the Proceedings of the National...
Intercomparison of Machine Learning Algorithms for Remote Sensing-based In-season Crop Mapping
arXiv:2606.05731v1 Announce Type: new Abstract: In-season crop type mapping is critical for food security in the face of increasingly extreme climate-related threats to crops. Currently, the USDA Cropland Data Layer provides crop type labels at 30m resolution and is available the February after harvest, but no product exists that maps crop types before harvest with satisfactory accuracy that would allow emergency managers to respond to crop threats in near real time.
Reconstructing Multi-Decadal Forest Disturbances: A Spatio-Temporal Transformer Approach
Announce Type: new Abstract: Accurate monitoring of forest disturbances is essential for understanding carbon dynamics and land management, yet traditional approaches typically rely on pixel-wise analysis of satellite time-series, ignoring spatial context. We present a deep learning framework that maps 38 years (1984-2022) of forest disturbance across the contiguous United States by modeling temporal trajectories and spatial neighborhoods simultaneously. By leveraging a vision transformer...