Lightnews — Scholar-powered news

Reposted by ML Earth Sciences

Santiago Gassó @sangasso.bsky.social · 16d

🧪🛰️🌍
A great use of satellite platforms: ML applied to #geostationary native radiance observations (GOES and TEMPO) with High res model outputs to derive surface PM2.5

pubs.acs.org/doi/10.1021/...

@aerosolwatch.bsky.social @mparrington.bsky.social

Hour by Hour PM2.5 Mapping Using Geostationary Satellites

This study estimates ground-level fine particulate matter (PM2.5) concentrations using geostationary satellites-derived Aerosol Optical Depth (AOD) and radiance measurements and meteorological paramet...

pubs.acs.org

3 11

Reposted by ML Earth Sciences

KinarNicholas.bsky.social @kinarnicholas.bsky.social · 17d

Hydrology Paper of the Day @mlearthsciences.bsky.social on linkages between rainfall and landslides as predicted by machine learning: investigating how ANNs demonstrate a geography of risk; comparisons with known areas of landslides; and application to the Serra Geral region of southern Brazil.

ML Earth Sciences @mlearthsciences.bsky.social · 18d

Spatially distributed antecedent rainfall thresholds for landslide occurrence: a multitask machine learning modelling approach

doi.org/10.1080/0262...
#machinelearning #landslide

Spatially distributed antecedent rainfall thresholds for landslide occurrence: a multitask machine learning modelling approach

Landslide susceptibility and the amount of antecedent rainfall required to trigger landslides are conceptually tightly linked but usually modelled separately. We propose an approach for modelling b...

doi.org

1 5

ML Earth Sciences @mlearthsciences.bsky.social · 18d

Spatially distributed antecedent rainfall thresholds for landslide occurrence: a multitask machine learning modelling approach

doi.org/10.1080/0262...
#machinelearning #landslide

Spatially distributed antecedent rainfall thresholds for landslide occurrence: a multitask machine learning modelling approach

Landslide susceptibility and the amount of antecedent rainfall required to trigger landslides are conceptually tightly linked but usually modelled separately. We propose an approach for modelling b...

doi.org

5

Reposted by ML Earth Sciences

KinarNicholas.bsky.social @kinarnicholas.bsky.social · 24d

Hydrology Paper of the Day @mlearthsciences.bsky.social on the use of frequency-domain neural networks for soil moisture imputation: weather station observations and model application by sliding windows and spatial convolution; a comparison of approaches; and rainfall magnitudes and sensitivities.

ML Earth Sciences @mlearthsciences.bsky.social · 25d

Worried about missing value?

A novel imputation framework based on Fourier neural operators (FNO) for soil moisture. The FNO model outperforms traditional approaches, and incorporating temporal lag reduces error by up to 15% in the diverse climates in India and Zambia.
doi.org/10.1029/2025...

Leveraging Neural Operator and Sliding Window Technique for Enhanced Subsurface Soil Moisture Imputation Under Diverse Precipitation Scenarios

Developed a novel Fourier Neural Operator (FNO) to enhance subsurface soil moisture imputation by employing a sliding window concept that seamlessly integrates rainfall, soil temperature, and norm...

doi.org

2 7

ML Earth Sciences @mlearthsciences.bsky.social · 25d

Worried about missing value?

A novel imputation framework based on Fourier neural operators (FNO) for soil moisture. The FNO model outperforms traditional approaches, and incorporating temporal lag reduces error by up to 15% in the diverse climates in India and Zambia.
doi.org/10.1029/2025...

Leveraging Neural Operator and Sliding Window Technique for Enhanced Subsurface Soil Moisture Imputation Under Diverse Precipitation Scenarios

Developed a novel Fourier Neural Operator (FNO) to enhance subsurface soil moisture imputation by employing a sliding window concept that seamlessly integrates rainfall, soil temperature, and norm...

doi.org

3

Reposted by ML Earth Sciences

IOPP Machine Learning and AI @iopp-mlresearch.bsky.social · Sep 5

Recently published in #MachineLearningEngineering 🥳

Read the #openaccess article: 'Understanding interpretable patterns of Shapley behaviours in materials data'
👉 ow.ly/bmmN50WHE2X
From Tommy Liu and Amanda S Barnard, (Australian National University)
#DataAnalysis #ExplainableArtificialIntelligence

1 3

Reposted by ML Earth Sciences

AMS Committee on Hydrology @amshydrology.bsky.social · Aug 26

We are excited to announce openings for SIX new members to join the @ametsoc.org Committee on Hydrology! We are looking for 2 undergrad/grad student members and 4 regular members.

Applications are due September 16 (CV and letter of interest required): forms.gle/qKdsLTTK1k9e...

5 5

Reposted by ML Earth Sciences

KinarNicholas.bsky.social @kinarnicholas.bsky.social · Aug 21

Hydrology Paper of the Day @pnas.org on how climate change, increases in stream temperature, and latitudinal gradients affect freshwater fish populations: the RivFishTime database paired with the Global Biodiversity Information Facility database for spatial analyses, and identifying decadal trends.

Proceedings of the National Academy of Sciences @pnas.org · Dec 11

A study of 632 freshwater fish species from 9,989 locations finds climate warming affects fish abundances, with increases at the poleward edges of species’ distributions and declines at the equatorward edges of species’ distributions. In PNAS: www.pnas.org/doi/10.1073/...

1 3 8

Reposted by ML Earth Sciences

Critical Zone News @cznews.bsky.social · Jul 17

#CriticalZone at #AGU25

Louise Kreissig-Arnal, PhD (she/elle) @arnallouise.bsky.social · Jul 16

Excited to share our #AGU25 session: New Developments & Future Directions in Community Water Resources Modeling

Topics include: 💻Open software, cloud computing 🌐Process representation, uncertainty, model coupling 🧠ML 👥Social hydrology

🔗Submit an abstract by July 30: agu.confex.com/agu/agu25/pr...

2 2

Reposted by ML Earth Sciences

Ben Bond-Lamberty @benbondlamberty.bsky.social · Jul 21

BO11 - Advancing Biogeochemical Cycle Modeling with Artificial Intelligence (Al): Bridging Data-Driven Methods and Process-Based Approaches #AGU25

1 3

Reposted by ML Earth Sciences

AGU Catchment Hydrology Technical Committee @agucatchhydro.bsky.social · Jul 23

🌎 Are you attending #AGU25?
Consider submitting your abstract to our session on Challenges and Solutions for Hydrologic Scaling Across Multiple Processes and Scales.
More information below!!!

Antonio Meira @ntoniomeiraneto.bsky.social · Jul 22

Inviting submissions to our session!
💧🌎
SESSION H048 - Challenges and Solutions for Hydrologic Scaling Across Multiple Processes and Scales

See the link below:
agu.confex.com/agu/agu25/pr...

Shout out to:
@agucatchhydro.bsky.social @agu-h3s.bsky.social @agu.org

2 3

Reposted by ML Earth Sciences

Fernando Pérez @fernandoperez.org · Jul 24

Open source, open science for earth, climate and geospatial science? Coming to #AGU25? Build tools in #Python @jupyter.org?

Submit an abstract for this session and come meet us and like minded scientists!

Wilson Sauthoff @wilsonsauthoff.bsky.social · Jul 22

📣 Call for submissions: AGU25 IN029 "Open Source Geospatial Workflows in the Cloud" 🌍

Please submit your work using cutting-edge practices and tools that are shaping the future of geospatial science.

More info: events.geojupyter.org/conferences/...

#opensource #geospatial #jupyter #AGU25

AGU session advertisement with session title and invited speakers.

4 9

Reposted by ML Earth Sciences

Abhilash Singh @abhilashsingh.bsky.social · Jul 22

First post !!!

Happy to share our new paper is out in Engineering Applications of AI!
"Overcoming Data Scarcity" uses transfer learning + satellite fusion to predict soil moisture with 55% less in-situ data
🔗 doi.org/10.1016/j.enga…
�� abhilashsingh.net/codes.ht#RemoteSensinge#MLn#AIML #AI

1 4 5

Reposted by ML Earth Sciences

IOPP Machine Learning and AI @iopp-mlresearch.bsky.social · Jul 22

Publish in AI for Science for:

🔬 Broad scope for AI-driven scientific breakthroughs
🌍 Open access publishing at no cost to you
✅ Review by top international experts
⚡ Fast publication
📝 Formatting your way and we’ll handle the rest

🔗 Submit now: iopscience.iop.org/journal/3050...

#AIResearch

1 2

Reposted by ML Earth Sciences

ML_Hydroclimate @mlhydroclimate.bsky.social · Jul 22

A new paper is out in Engineering Applications of AI!
"Overcoming Data Scarcity" uses transfer learning + satellite fusion to predict soil moisture with 55% less in-situ data
🔗 doi.org/10.1016/j.enga…

https://doi.org/10.1016/j.enga…

1 2

Reposted by ML Earth Sciences

esaclimate @esaclimate.bsky.social · Jun 20

✊ From observations to actionable information: Take a look at our mission in our digital flyer!

It gives an overview of 40+ years of satellite data from a growing number of Essential Climate Variables to strengthen climate understanding and inform effective decision-making:

t1p.de/lbgyl #LPS25

1 3

Reposted by ML Earth Sciences

Margaret Zimmer @margaretzimmer.bsky.social · Jun 6

Wrapping up your PhD? I’m planning to hire a postdoc in the next few months (start date flexible). Will start formally advertising soon, but you heard it here first!! Possible research topics include - critical zone hydrology, agricultural water quality, SW-GW interactions. Reach out if interested!

1 23 26

Reposted by ML Earth Sciences

KinarNicholas.bsky.social @kinarnicholas.bsky.social · Jun 7

Hydrology Paper of the Day @rarakihydro.bsky.social on a model of soil moisture loss that utilizes a nonlinear function: model fitting to SMAP remote sensing data; an examination of global-scale patterns; how aridity, sand fraction and landcover affects outputs; and the role of evapotranspiration.

Ryoko Araki @rarakihydro.bsky.social · Jun 3

My 2nd PhD chapter is out! Soil drying speeds encode the signature of evapotranspiration. Using SMAP data, we demonstrated that, introducing nonlinearity in a traditional soil model help capture aggressive vs conservative vegetation water consumption.

doi.org/10.1029/2024...

4 10

Reposted by ML Earth Sciences

petersalamon.bsky.social @petersalamon.bsky.social · Jun 7

⏳ Time is running out!

Apply by 15 June to join the JRC as a hydrologic modeller and help shape the future of early warnings in Europe and beyond.

Make an impact with #CEMS, #EFAS, #GloFAS.
🔗 recruitment.jrc.ec.europa.eu/vacancy/1885

#Floods #Drought #Hydrology

2 2

ML Earth Sciences @mlearthsciences.bsky.social · May 26

PC: Yifang Ban, KTH

1

Reposted by ML Earth Sciences

KinarNicholas.bsky.social @kinarnicholas.bsky.social · May 24

Hydrology Paper of the Day @bioclimatology.bsky.social on how more than six years of data from a beech forest in Germany indicates changes in Critical Zone hydrology due to climate change: rainfall trends and canopy-scale partitioning; seasonal changes; and identifying storage and drivers.

Bioclimatology Göttingen @bioclimatology.bsky.social · May 12

Paper alert! Drollinger et al. show that shorter, more intense rain events in European beech forests shrink throughfall and amplify moisture patchiness, gradually loosening hydrological links and altering Critical Zone function. Link: iopscience.iop.org/article/10.1... #Hydrology #Climate

3 6

Reposted by ML Earth Sciences

Manousos Valyrakis @hydromechanic.bsky.social · May 22

Calling all river scientists & engineers! #RiverFlow2026 is now accepting abstracts. Share your latest research in fluvial hydraulics and join us in Thessaloniki, Greece! Submit your abstract by August 15, 2025. 🔗 riverflow2026.web.auth.gr  #Hydraulics #RiverScience

1 2 3

Reposted by ML Earth Sciences

Hydrology Next @hydrologynext.bsky.social · May 22

Interesting readings very recently published on 𝘿𝙞𝙜𝙞𝙩𝙖𝙡 𝙏𝙬𝙞𝙣𝙨 𝙤𝙛 𝙩𝙝𝙚 𝙀𝙖𝙧𝙩𝙝

Digital Twins of the Earth Between Vision and Fiction
lnkd.in/dYQTs5wM

and also:
Bring digital twins back to Earth
lnkd.in/dih-JvGT

1 3

Reposted by ML Earth Sciences

KinarNicholas.bsky.social @kinarnicholas.bsky.social · May 4

Hydrology Paper of the Day @ianabrown.bsky.social on how landscape affects drone-based photogrammetry: comparisons with LiDAR DEMs and CHMs for the Kronängen area of boreal forests and meadows; differences in topography; and accuracy comparisons with respect to weather, location and vegetation.

Ian Brown @ianabrown.bsky.social · Jul 23

New paper alert! 🚨📢

Weather impacts on #drone #photogrammetry:
- seasonal and weather effects on accuracy
- canopy height model and DEMs evaluated
- reference #lidar dataset with #dGPS control points
with @linegreis.bsky.social

#UAV #forest #remotesensing
dx.doi.org/10.1111/avsc...

Seasonal optimisation of drone‐based photogrammetry in a heterogeneous boreal landscape

In this study we show how weather and flight parameters affect photogrammetric surveys from drones. Using a commercial (off-the-shelf) drone we surveyed a mixed boreal forest site in summer, autumn a...

dx.doi.org

2 12

Reposted by ML Earth Sciences

KinarNicholas.bsky.social @kinarnicholas.bsky.social · Apr 28

Hydrology Paper of the Day @okke-batelaan.bsky.social on determining ET and temperature beneath the canopy of a forest by drone and airplane thermal imaging, LiDAR scanning and modelling: two catchments in Australia; satellite optical sensing; upscaling; validation; and quantifying uncertainties.

Okke Batelaan @okke-batelaan.bsky.social · Mar 28

New open access paper on innovative approach to understorey evapotranspiration mapping
doi.org/10.1016/j.jh...

Redirecting

doi.org

3 8