Monitoring Earth’s climate variables with satellite laser altimetry

Nature Reviews Earth & Environment, Journal Year: 2024, Volume and Issue: 5(2), P. 120 - 136

Published: Jan. 30, 2024

Language: Английский

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Sea ice melt pond bathymetry reconstructed from aerial photographs using photogrammetry: a new method applied to MOSAiC data

˜The œcryosphere, Journal Year: 2024, Volume and Issue: 18(7), P. 2991 - 3015

Published: July 2, 2024

Abstract. Melt ponds are a core component of the summer sea ice system in Arctic, increasing uptake solar energy and impacting ice-associated ecosystem. They were thus one key topics during 1-year drift campaign Multidisciplinary drifting Observatory for Study Arctic Climate (MOSAiC) Transpolar Drift 2019/2020. Pond depth is dominating factor describing surface meltwater volume; it necessary to estimate budgets used model parameterization simulate pond coverage evolution. However, observational data on spatially temporally strongly limited few situ measurements. bathymetry, which fully resolved, remains unexplored. Here, we present newly developed method derive bathymetry from aerial images. We determine photogrammetric multi-view reconstruction topography. Based images recorded dedicated grid flights facilitated assumptions, able obtain with mean deviation 3.5 cm compared manual observations. The independent color sky conditions, an advantage over recently radiometric airborne retrieval methods. It can furthermore be implemented any typical photogrammetry workflow. algorithm, including requirements recording survey planning, correction refraction at air–pond interface. In addition, show how retrieved topography synergizes initial image retrieve water level individual visually determined margins. use give profound overview MOSAiC floe, found unexpected steady volume. properties more than 1600 their size, volume, elevation above level, temporal scaling single measurements, discuss representativeness measurements importance such high-resolution new satellite retrievals, indications non-rigid bottoms. study points out great potential geometric emerging increasingly available visual uncrewed vehicles (UAVs) or aircraft, allowing integrated understanding improved formulation thermodynamic hydrological models.

Language: Английский

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Automated Detection and Depth Determination of Melt Ponds on Sea Ice in ICESat-2 ATLAS Data—The Density-Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-Bifurcate-Seaice)

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2023, Volume and Issue: 61, P. 1 - 22

Published: Jan. 1, 2023

As climate warms and the transition from a perennial to seasonal Arctic sea-ice cover is imminent, understanding melt ponding central changes in new Arctic. NASA's Ice, Cloud land Elevation Satellite (ICESat-2) has capacity provide measurements monitoring of onset on progression. Yet ponds are currently not identified ICESat-2 standard products, which only single surface determined. The objective this paper introduce mathematical algorithm that facilitates automated detection ATLAS data, retrieval two heights, pond bottom, depth width ponds. With Advanced Topographic Laser Altimeter System (ATLAS), carries first space-borne multi-beam micro-pulse photon-counting laser altimeter system, operating at 532 nm frequency. data recorded as clouds discrete photon points. Density-Dimension Algorithm for bifurcating reflectors (DDA-bifurcate-seaice) an auto-adaptive solves problem near 0.7 m nominal along-track spacing utilizing radial basis function calculation density field threshold automatically adapts background, apparent reflectance some instrument effects. DDA-bifurcate-seaice applied large sets 2019 2020 seasons multi-year region. Results evaluated by comparison those manually forced algorithm.

Language: Английский

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A framework for automated supraglacial lake detection and depth retrieval in ICESat-2 photon data across the Greenland and Antarctic ice sheets

˜The œcryosphere, Journal Year: 2024, Volume and Issue: 18(11), P. 5173 - 5206

Published: Nov. 15, 2024

Abstract. Water depths of supraglacial lakes on the ice sheets are difficult to monitor continuously due lakes' ephemeral nature and inaccessible locations. Supraglacial have been linked shelf collapse in Antarctica accelerated flow grounded Greenland. However, impact dynamics has not quantified accurately enough predict their contribution future mass loss sea level rise. This is largely because ice-sheet-wide assessments meltwater volumes rely models that poorly constrained a lack accurate depth measurements. Various recent case studies demonstrated lake can be obtained from NASA's Ice, Cloud land Elevation Satellite (ICESat-2) ATL03 photon-level data product. comprises hundreds terabytes unstructured point cloud data, which made it challenging use this bathymetric capability at scale. Here, we present two new algorithms – Flat Lake Underlying Ice Detection (FLUID) Surface Removal Robust Fit (SuRRF) together provide fully automated scalable method for detection along-track determination establish framework its large-scale implementation using distributed high-throughput computing. We report FLUID–SuRRF algorithm performance over regions known significant surface melt central West Greenland Amery Shelf catchment East during seasons. reveals total 1249 ICESat-2 segments up 25 m deep, with more water higher-melt years. In absence ground-truth manual annotation test suggests our reliably detects along ICESat-2's ground tracks whenever bed visible or partially estimates mean absolute error <0.27 m. These results imply proposed potential generate comprehensive product across both sheets.

Language: Английский

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Enhanced sea ice classification for ICESat-2 using combined unsupervised and supervised machine learning

Remote Sensing of Environment, Journal Year: 2025, Volume and Issue: 318, P. 114607 - 114607

Published: Jan. 25, 2025

Language: Английский

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Predicting Melt Pond Coverage on Arctic Sea Ice From Pre‐Melt Surface Topography

Geophysical Research Letters, Journal Year: 2025, Volume and Issue: 52(9)

Published: May 8, 2025

Abstract Sea‐ice melt ponds form in the depressions of pre‐melt surface topography, a process widely accepted yet lacking larger‐scale evaluation through explicit comparisons. During MOSAiC, we collected multi‐dimensional aerial data to examine relationship between topography and pond evolution across an entire Arctic ice floe. Using hydrological models, analyze correlation potential meltwater accumulation areas identified winter spring topographies, available meltwater, observed coverage. Our findings demonstrate strong connection, revealing 72% accuracy matching low ponds, with 98% basins deeper than 0.5 m transforming into ponds. Incorporating assumptions regarding availability improve predictions fraction highlight key factors driving extensive lateral runoff networks on No significant differences are first‐ second‐year ice. This study provides valuable ground truth for future modeling measurements formation.

Language: Английский

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Comparing elevation and backscatter retrievals from CryoSat-2 and ICESat-2 over Arctic summer sea ice

˜The œcryosphere, Journal Year: 2023, Volume and Issue: 17(9), P. 4165 - 4178

Published: Sept. 27, 2023

Abstract. The CryoSat-2 radar altimeter and ICESat-2 laser can provide complementary measurements of the freeboard thickness Arctic sea ice. However, both sensors face significant challenges for accurately measuring ice when is melting in summer months. Here, we used crossover points between to compare elevation retrievals over 2018–2021. We focused on electromagnetic (EM) bias documented measurements, associated with surface melt ponds which cause underestimate elevation. not susceptible this but has other biases ponds. So, compared difference reflectance statistics two satellites. found that underestimated by a median 2.4 cm absolute deviation 5.3 cm, while differences individual beams ranged 1–3.5 cm. Spatial temporal patterns were roughness information derived from data, photon rate (surface reflectivity), backscatter, pond fraction Sentinel-3 Ocean Land Color Instrument (OLCI) data. good agreement theoretical predictions EM our new observations; however, at typical <0.1 m experimentally measured was larger (5–10 cm) resulting simulations (0–5 cm). This intercomparison will be valuable interpreting improving altimeters.

Language: Английский

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Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(11), P. 1854 - 1854

Published: May 23, 2024

The objectives of this paper are to investigate the trade-offs between a physically constrained neural network and deep, convolutional design combined ML approach (“VarioCNN”). Our solution is provided in framework cyberinfrastructure that includes newly designed software, GEOCLASS-image (v1.0), modern high-resolution satellite image data sets (Maxar WorldView data), instructions/descriptions may facilitate solving similar spatial classification problems. Combining advantages physically-driven connectionist-geostatistical method with those an efficient CNN, VarioCNN provides means for rapid extraction complex geophysical information from submeter resolution imagery. A retraining loop overcomes difficulties creating labeled training set. Computational analyses developments centered on specific, but generalizable, problem: crevasse types form during surge glacier system. glacial catastrophe, acceleration typically 100–200 times its normal velocity. applied study current (2016-2024) Negribreen Glacier System, Svalbard. result description structural evolution expansion surge, based capture ice deformation six simplified classes.

Language: Английский

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Joint mapping of melt pond bathymetry and water volume on sea ice using optical remote sensing images and physical reflectance models

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 318, P. 114571 - 114571

Published: Dec. 20, 2024

Language: Английский

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