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#earthobservation — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #earthobservation, aggregated by home.social.

  1. Some of Earth's most important places, rainforests and flood zones, hide under cloud most of the year. Radar (SAR) makes its own signal at a wavelength clouds ignore, mapping the ground day, night, and through storms. Why radar quietly underpins Earth observation.

    #RemoteSensing #SAR #EarthObservation

  2. A Scalable Cloud-Native Framework for Satellite Rainfall-Driven Landslide Early Warning in Data-Scarce Regions
    --
    doi.org/10.5194/egusphere-2026 <-- shared technical article
    --
    ee-yunuscool.projects.eartheng <-- shared web map based ALERT, Automated Landslide Early Risk Tracker
    --
    doi.org/10.1186/s40677-022-002 <-- shared paper
    --
    H/T @ Yunus Ali Pulpadan | Assistant Professor at Indian Institute of Science Education and Research (IISER), Mohali
    “[The authors] are excited to share ALERT — the Automated Landslide Early Risk Tracker, a cloud-native framework and web application developed to support near-real-time, impact-based assessment of rainfall-triggered landslide hazards.
    ALERT integrates multiple components within a single scalable framework:
    🛰️ Satellite-based rainfall observations
    🌦️ Operational weather forecasts
    🗺️ Terrain susceptibility information
    📈 Rainfall intensity–duration thresholds
    🏔️ Debris-flow runout modelling
    🏘️ Building exposure
    The goal is to move beyond simply identifying intense rainfall and towards understanding where hazardous conditions may trigger landslides and what may lie in the potential path of downstream impacts. A particular motivation behind ALERT is the challenge of developing landslide early-warning capabilities in data-scarce mountainous regions, where dense rain-gauge networks and operational monitoring infrastructure are often limited. The framework incorporates a catalogue of rainfall thresholds while also allowing users to integrate their own thresholds and susceptibility information, making it adaptable across different climatic and geomorphological settings…”
    #GIS #spatial #mapping #AI #deeplearning #massmovement #landslide #engineeringgeology #water #precipitation #rainfall #earlywarning #remotesensing #earthobservation #global #webmap #dataportal #risk #hazard #infrastructure #building #weather #forecasting #imagery #debrisflow #model #modeling #ALERT #opendata #climate #geology hydrogeomorphology geomorphology public safety global #regional

  3. A Scalable Cloud-Native Framework for Satellite Rainfall-Driven Landslide Early Warning in Data-Scarce Regions
    --
    doi.org/10.5194/egusphere-2026 <-- shared technical article
    --
    ee-yunuscool.projects.eartheng <-- shared web map based ALERT, Automated Landslide Early Risk Tracker
    --
    doi.org/10.1186/s40677-022-002 <-- shared paper
    --
    H/T @ Yunus Ali Pulpadan | Assistant Professor at Indian Institute of Science Education and Research (IISER), Mohali
    “[The authors] are excited to share ALERT — the Automated Landslide Early Risk Tracker, a cloud-native framework and web application developed to support near-real-time, impact-based assessment of rainfall-triggered landslide hazards.
    ALERT integrates multiple components within a single scalable framework:
    🛰️ Satellite-based rainfall observations
    🌦️ Operational weather forecasts
    🗺️ Terrain susceptibility information
    📈 Rainfall intensity–duration thresholds
    🏔️ Debris-flow runout modelling
    🏘️ Building exposure
    The goal is to move beyond simply identifying intense rainfall and towards understanding where hazardous conditions may trigger landslides and what may lie in the potential path of downstream impacts. A particular motivation behind ALERT is the challenge of developing landslide early-warning capabilities in data-scarce mountainous regions, where dense rain-gauge networks and operational monitoring infrastructure are often limited. The framework incorporates a catalogue of rainfall thresholds while also allowing users to integrate their own thresholds and susceptibility information, making it adaptable across different climatic and geomorphological settings…”
    #GIS #spatial #mapping #AI #deeplearning #massmovement #landslide #engineeringgeology #water #precipitation #rainfall #earlywarning #remotesensing #earthobservation #global #webmap #dataportal #risk #hazard #infrastructure #building #weather #forecasting #imagery #debrisflow #model #modeling #ALERT #opendata #climate #geology hydrogeomorphology geomorphology public safety global #regional

  4. A Scalable Cloud-Native Framework for Satellite Rainfall-Driven Landslide Early Warning in Data-Scarce Regions
    --
    doi.org/10.5194/egusphere-2026 <-- shared technical article
    --
    ee-yunuscool.projects.eartheng <-- shared web map based ALERT, Automated Landslide Early Risk Tracker
    --
    doi.org/10.1186/s40677-022-002 <-- shared paper
    --
    H/T @ Yunus Ali Pulpadan | Assistant Professor at Indian Institute of Science Education and Research (IISER), Mohali
    “[The authors] are excited to share ALERT — the Automated Landslide Early Risk Tracker, a cloud-native framework and web application developed to support near-real-time, impact-based assessment of rainfall-triggered landslide hazards.
    ALERT integrates multiple components within a single scalable framework:
    🛰️ Satellite-based rainfall observations
    🌦️ Operational weather forecasts
    🗺️ Terrain susceptibility information
    📈 Rainfall intensity–duration thresholds
    🏔️ Debris-flow runout modelling
    🏘️ Building exposure
    The goal is to move beyond simply identifying intense rainfall and towards understanding where hazardous conditions may trigger landslides and what may lie in the potential path of downstream impacts. A particular motivation behind ALERT is the challenge of developing landslide early-warning capabilities in data-scarce mountainous regions, where dense rain-gauge networks and operational monitoring infrastructure are often limited. The framework incorporates a catalogue of rainfall thresholds while also allowing users to integrate their own thresholds and susceptibility information, making it adaptable across different climatic and geomorphological settings…”
    #GIS #spatial #mapping #AI #deeplearning #massmovement #landslide #engineeringgeology #water #precipitation #rainfall #earlywarning #remotesensing #earthobservation #global #webmap #dataportal #risk #hazard #infrastructure #building #weather #forecasting #imagery #debrisflow #model #modeling #ALERT #opendata #climate #geology hydrogeomorphology geomorphology public safety global #regional

  5. A Scalable Cloud-Native Framework for Satellite Rainfall-Driven Landslide Early Warning in Data-Scarce Regions
    --
    doi.org/10.5194/egusphere-2026 <-- shared technical article
    --
    ee-yunuscool.projects.eartheng <-- shared web map based ALERT, Automated Landslide Early Risk Tracker
    --
    doi.org/10.1186/s40677-022-002 <-- shared paper
    --
    H/T @ Yunus Ali Pulpadan | Assistant Professor at Indian Institute of Science Education and Research (IISER), Mohali
    “[The authors] are excited to share ALERT — the Automated Landslide Early Risk Tracker, a cloud-native framework and web application developed to support near-real-time, impact-based assessment of rainfall-triggered landslide hazards.
    ALERT integrates multiple components within a single scalable framework:
    🛰️ Satellite-based rainfall observations
    🌦️ Operational weather forecasts
    🗺️ Terrain susceptibility information
    📈 Rainfall intensity–duration thresholds
    🏔️ Debris-flow runout modelling
    🏘️ Building exposure
    The goal is to move beyond simply identifying intense rainfall and towards understanding where hazardous conditions may trigger landslides and what may lie in the potential path of downstream impacts. A particular motivation behind ALERT is the challenge of developing landslide early-warning capabilities in data-scarce mountainous regions, where dense rain-gauge networks and operational monitoring infrastructure are often limited. The framework incorporates a catalogue of rainfall thresholds while also allowing users to integrate their own thresholds and susceptibility information, making it adaptable across different climatic and geomorphological settings…”
    #GIS #spatial #mapping #AI #deeplearning #massmovement #landslide #engineeringgeology #water #precipitation #rainfall #earlywarning #remotesensing #earthobservation #global #webmap #dataportal #risk #hazard #infrastructure #building #weather #forecasting #imagery #debrisflow #model #modeling #ALERT #opendata #climate #geology hydrogeomorphology geomorphology public safety global #regional

  6. A Scalable Cloud-Native Framework for Satellite Rainfall-Driven Landslide Early Warning in Data-Scarce Regions
    --
    doi.org/10.5194/egusphere-2026 <-- shared technical article
    --
    ee-yunuscool.projects.eartheng <-- shared web map based ALERT, Automated Landslide Early Risk Tracker
    --
    doi.org/10.1186/s40677-022-002 <-- shared paper
    --
    H/T @ Yunus Ali Pulpadan | Assistant Professor at Indian Institute of Science Education and Research (IISER), Mohali
    “[The authors] are excited to share ALERT — the Automated Landslide Early Risk Tracker, a cloud-native framework and web application developed to support near-real-time, impact-based assessment of rainfall-triggered landslide hazards.
    ALERT integrates multiple components within a single scalable framework:
    🛰️ Satellite-based rainfall observations
    🌦️ Operational weather forecasts
    🗺️ Terrain susceptibility information
    📈 Rainfall intensity–duration thresholds
    🏔️ Debris-flow runout modelling
    🏘️ Building exposure
    The goal is to move beyond simply identifying intense rainfall and towards understanding where hazardous conditions may trigger landslides and what may lie in the potential path of downstream impacts. A particular motivation behind ALERT is the challenge of developing landslide early-warning capabilities in data-scarce mountainous regions, where dense rain-gauge networks and operational monitoring infrastructure are often limited. The framework incorporates a catalogue of rainfall thresholds while also allowing users to integrate their own thresholds and susceptibility information, making it adaptable across different climatic and geomorphological settings…”
    hydrogeomorphology geomorphology public safety global

  7. Sub-Seasonal Forecasting Of Cropland Productivity Anomalies Using Satellite Soil Moisture In Water-Limited Environments
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    H/T @Adebowale Daniel Adebayo | Doctoral Candidate | Geospatial Data Scientist
    “When rainfall fails and evaporative demand climbs, root-zone soil moisture is where the deficit registers first and where it carries forward, weeks before crop condition reflects it. In this study [link above], [the authors] used SMAP root-zone soil moisture to forecast crop productivity anomalies across drought-prone croplands of Eastern and Southern Africa, and [they] quantified how much predictive value soil moisture actually carries, over what lead times, and under which hydroclimatic conditions. The contribution of soil moisture was negligible at short leads but grew steadily out to 40 days, and it concentrated in water-limited croplands where soil moisture and vegetation are most tightly coupled. During the 2024 southern African El Niño drought, the soil moisture informed model resolved the spatial pattern of productivity anomalies roughly a month in advance.
    Beyond the results themselves, this work is a pointer to how much predictive information soil moisture holds. Leveraging the temporal record of SMAP-related products together with the 100–200 metre resolution NISAR will deliver gives us, [believes the H/T], a clear path to attempt field-scale drought forecasting in the smallholder landscapes where early warning matters most…”
    #agriculture #crops #foodsecurity #cropland #productivity #forecasting #rootzone #soilmoisture #SMAP #NIRV #subseasonal #prediction #EasternAfrica #SouthernAfrica #africa #vegetation #anomaly #precipitation #ET #drought #extremeweather #water #hydrology #hydroclimate #productivity #arid #waterlimited #spatialanalysis #spatiotemporal #model #modeling #vegetation #Africa #ElNino #ElNiño #mitigation #prediction #forecast #smallholders #earlywarning #RZSM #SoilMoistureActivePassive #NearInfraredReflectance #NIR #remotesensing #earthobservation

  8. Sub-Seasonal Forecasting Of Cropland Productivity Anomalies Using Satellite Soil Moisture In Water-Limited Environments
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    H/T @Adebowale Daniel Adebayo | Doctoral Candidate | Geospatial Data Scientist
    “When rainfall fails and evaporative demand climbs, root-zone soil moisture is where the deficit registers first and where it carries forward, weeks before crop condition reflects it. In this study [link above], [the authors] used SMAP root-zone soil moisture to forecast crop productivity anomalies across drought-prone croplands of Eastern and Southern Africa, and [they] quantified how much predictive value soil moisture actually carries, over what lead times, and under which hydroclimatic conditions. The contribution of soil moisture was negligible at short leads but grew steadily out to 40 days, and it concentrated in water-limited croplands where soil moisture and vegetation are most tightly coupled. During the 2024 southern African El Niño drought, the soil moisture informed model resolved the spatial pattern of productivity anomalies roughly a month in advance.
    Beyond the results themselves, this work is a pointer to how much predictive information soil moisture holds. Leveraging the temporal record of SMAP-related products together with the 100–200 metre resolution NISAR will deliver gives us, [believes the H/T], a clear path to attempt field-scale drought forecasting in the smallholder landscapes where early warning matters most…”
    #agriculture #crops #foodsecurity #cropland #productivity #forecasting #rootzone #soilmoisture #SMAP #NIRV #subseasonal #prediction #EasternAfrica #SouthernAfrica #africa #vegetation #anomaly #precipitation #ET #drought #extremeweather #water #hydrology #hydroclimate #productivity #arid #waterlimited #spatialanalysis #spatiotemporal #model #modeling #vegetation #Africa #ElNino #ElNiño #mitigation #prediction #forecast #smallholders #earlywarning #RZSM #SoilMoistureActivePassive #NearInfraredReflectance #NIR #remotesensing #earthobservation

  9. Sub-Seasonal Forecasting Of Cropland Productivity Anomalies Using Satellite Soil Moisture In Water-Limited Environments
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    H/T @Adebowale Daniel Adebayo | Doctoral Candidate | Geospatial Data Scientist
    “When rainfall fails and evaporative demand climbs, root-zone soil moisture is where the deficit registers first and where it carries forward, weeks before crop condition reflects it. In this study [link above], [the authors] used SMAP root-zone soil moisture to forecast crop productivity anomalies across drought-prone croplands of Eastern and Southern Africa, and [they] quantified how much predictive value soil moisture actually carries, over what lead times, and under which hydroclimatic conditions. The contribution of soil moisture was negligible at short leads but grew steadily out to 40 days, and it concentrated in water-limited croplands where soil moisture and vegetation are most tightly coupled. During the 2024 southern African El Niño drought, the soil moisture informed model resolved the spatial pattern of productivity anomalies roughly a month in advance.
    Beyond the results themselves, this work is a pointer to how much predictive information soil moisture holds. Leveraging the temporal record of SMAP-related products together with the 100–200 metre resolution NISAR will deliver gives us, [believes the H/T], a clear path to attempt field-scale drought forecasting in the smallholder landscapes where early warning matters most…”
    #agriculture #crops #foodsecurity #cropland #productivity #forecasting #rootzone #soilmoisture #SMAP #NIRV #subseasonal #prediction #EasternAfrica #SouthernAfrica #africa #vegetation #anomaly #precipitation #ET #drought #extremeweather #water #hydrology #hydroclimate #productivity #arid #waterlimited #spatialanalysis #spatiotemporal #model #modeling #vegetation #Africa #ElNino #ElNiño #mitigation #prediction #forecast #smallholders #earlywarning #RZSM #SoilMoistureActivePassive #NearInfraredReflectance #NIR #remotesensing #earthobservation

  10. Sub-Seasonal Forecasting Of Cropland Productivity Anomalies Using Satellite Soil Moisture In Water-Limited Environments
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    H/T @Adebowale Daniel Adebayo | Doctoral Candidate | Geospatial Data Scientist
    “When rainfall fails and evaporative demand climbs, root-zone soil moisture is where the deficit registers first and where it carries forward, weeks before crop condition reflects it. In this study [link above], [the authors] used SMAP root-zone soil moisture to forecast crop productivity anomalies across drought-prone croplands of Eastern and Southern Africa, and [they] quantified how much predictive value soil moisture actually carries, over what lead times, and under which hydroclimatic conditions. The contribution of soil moisture was negligible at short leads but grew steadily out to 40 days, and it concentrated in water-limited croplands where soil moisture and vegetation are most tightly coupled. During the 2024 southern African El Niño drought, the soil moisture informed model resolved the spatial pattern of productivity anomalies roughly a month in advance.
    Beyond the results themselves, this work is a pointer to how much predictive information soil moisture holds. Leveraging the temporal record of SMAP-related products together with the 100–200 metre resolution NISAR will deliver gives us, [believes the H/T], a clear path to attempt field-scale drought forecasting in the smallholder landscapes where early warning matters most…”
    #agriculture #crops #foodsecurity #cropland #productivity #forecasting #rootzone #soilmoisture #SMAP #NIRV #subseasonal #prediction #EasternAfrica #SouthernAfrica #africa #vegetation #anomaly #precipitation #ET #drought #extremeweather #water #hydrology #hydroclimate #productivity #arid #waterlimited #spatialanalysis #spatiotemporal #model #modeling #vegetation #Africa #ElNino #ElNiño #mitigation #prediction #forecast #smallholders #earlywarning #RZSM #SoilMoistureActivePassive #NearInfraredReflectance #NIR #remotesensing #earthobservation

  11. Sub-Seasonal Forecasting Of Cropland Productivity Anomalies Using Satellite Soil Moisture In Water-Limited Environments
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    H/T @Adebowale Daniel Adebayo | Doctoral Candidate | Geospatial Data Scientist
    “When rainfall fails and evaporative demand climbs, root-zone soil moisture is where the deficit registers first and where it carries forward, weeks before crop condition reflects it. In this study [link above], [the authors] used SMAP root-zone soil moisture to forecast crop productivity anomalies across drought-prone croplands of Eastern and Southern Africa, and [they] quantified how much predictive value soil moisture actually carries, over what lead times, and under which hydroclimatic conditions. The contribution of soil moisture was negligible at short leads but grew steadily out to 40 days, and it concentrated in water-limited croplands where soil moisture and vegetation are most tightly coupled. During the 2024 southern African El Niño drought, the soil moisture informed model resolved the spatial pattern of productivity anomalies roughly a month in advance.
    Beyond the results themselves, this work is a pointer to how much predictive information soil moisture holds. Leveraging the temporal record of SMAP-related products together with the 100–200 metre resolution NISAR will deliver gives us, [believes the H/T], a clear path to attempt field-scale drought forecasting in the smallholder landscapes where early warning matters most…”

  12. Sentinel-2 cloud masking: SCL band, s2cloudless, or Fmask? I've been burned by each in different scenes. What's your default in 2026, and why?
    #RemoteSensing #Sentinel2 #EarthObservation #GIS

  13. Sentinel-2 cloud masking: SCL band, s2cloudless, or Fmask? I've been burned by each in different scenes. What's your default in 2026, and why?
    #RemoteSensing #Sentinel2 #EarthObservation #GIS

  14. Sentinel-2 cloud masking: SCL band, s2cloudless, or Fmask? I've been burned by each in different scenes. What's your default in 2026, and why?
    #RemoteSensing #Sentinel2 #EarthObservation #GIS

  15. Sentinel-2 cloud masking: SCL band, s2cloudless, or Fmask? I've been burned by each in different scenes. What's your default in 2026, and why?
    #RemoteSensing #Sentinel2 #EarthObservation #GIS

  16. Sentinel-2 cloud masking: SCL band, s2cloudless, or Fmask? I've been burned by each in different scenes. What's your default in 2026, and why?
    #RemoteSensing #Sentinel2 #EarthObservation #GIS

  17. Interferometric Synthetic Aperture Radar (InSAR) For Monitoring Seasonal Snow
    --
    doi.org/10.1029/2025WR042866 <-- shared paper
    --
    eos.org/features/satellite-rad <-- shared technical article
    --
    H/T @Jack Tarricone, PhD | Assistant Research Scientist @ NASA GSFC/UMD ESSIC | Remote Sensing and Snow Hydrology
    “[The authors] review[ed] 25 years of progress in using InSAR to measure changes in snow water equivalent (SWE) and snow depth and discuss[ed] what’s needed to extend these methods to basin-scale snow monitoring with NISAR. [They] hope it’s a useful resource for people interested in snow, SAR/InSAR, remote sensing, and hydrology in general…”
    #GIS #spatial #mapping #remotesensing #earthobservation #InterferometricSyntheticApertureRadar #InSAR #literaturereview #research #history #monitoring #spatialanalysis #spatiotemporal #seasonal #snow #water #hydrology #waterresources #snowpack #snowmelt #ablation #melt #runoff #snowwaterequivalent #SWE #NISAR #snowdepth #basin #snowphase #estimation #change #spatial #GIS #mapping #temporal #model #modeling #algorithm #ecosystems #environment #habitat #agriculture #farming #snowmass #satellite

  18. Interferometric Synthetic Aperture Radar (InSAR) For Monitoring Seasonal Snow
    --
    doi.org/10.1029/2025WR042866 <-- shared paper
    --
    eos.org/features/satellite-rad <-- shared technical article
    --
    H/T @Jack Tarricone, PhD | Assistant Research Scientist @ NASA GSFC/UMD ESSIC | Remote Sensing and Snow Hydrology
    “[The authors] review[ed] 25 years of progress in using InSAR to measure changes in snow water equivalent (SWE) and snow depth and discuss[ed] what’s needed to extend these methods to basin-scale snow monitoring with NISAR. [They] hope it’s a useful resource for people interested in snow, SAR/InSAR, remote sensing, and hydrology in general…”
    #GIS #spatial #mapping #remotesensing #earthobservation #InterferometricSyntheticApertureRadar #InSAR #literaturereview #research #history #monitoring #spatialanalysis #spatiotemporal #seasonal #snow #water #hydrology #waterresources #snowpack #snowmelt #ablation #melt #runoff #snowwaterequivalent #SWE #NISAR #snowdepth #basin #snowphase #estimation #change #spatial #GIS #mapping #temporal #model #modeling #algorithm #ecosystems #environment #habitat #agriculture #farming #snowmass #satellite

  19. Interferometric Synthetic Aperture Radar (InSAR) For Monitoring Seasonal Snow
    --
    doi.org/10.1029/2025WR042866 <-- shared paper
    --
    eos.org/features/satellite-rad <-- shared technical article
    --
    H/T @Jack Tarricone, PhD | Assistant Research Scientist @ NASA GSFC/UMD ESSIC | Remote Sensing and Snow Hydrology
    “[The authors] review[ed] 25 years of progress in using InSAR to measure changes in snow water equivalent (SWE) and snow depth and discuss[ed] what’s needed to extend these methods to basin-scale snow monitoring with NISAR. [They] hope it’s a useful resource for people interested in snow, SAR/InSAR, remote sensing, and hydrology in general…”
    #GIS #spatial #mapping #remotesensing #earthobservation #InterferometricSyntheticApertureRadar #InSAR #literaturereview #research #history #monitoring #spatialanalysis #spatiotemporal #seasonal #snow #water #hydrology #waterresources #snowpack #snowmelt #ablation #melt #runoff #snowwaterequivalent #SWE #NISAR #snowdepth #basin #snowphase #estimation #change #spatial #GIS #mapping #temporal #model #modeling #algorithm #ecosystems #environment #habitat #agriculture #farming #snowmass #satellite

  20. Interferometric Synthetic Aperture Radar (InSAR) For Monitoring Seasonal Snow
    --
    doi.org/10.1029/2025WR042866 <-- shared paper
    --
    eos.org/features/satellite-rad <-- shared technical article
    --
    H/T @Jack Tarricone, PhD | Assistant Research Scientist @ NASA GSFC/UMD ESSIC | Remote Sensing and Snow Hydrology
    “[The authors] review[ed] 25 years of progress in using InSAR to measure changes in snow water equivalent (SWE) and snow depth and discuss[ed] what’s needed to extend these methods to basin-scale snow monitoring with NISAR. [They] hope it’s a useful resource for people interested in snow, SAR/InSAR, remote sensing, and hydrology in general…”

  21. Interferometric Synthetic Aperture Radar (InSAR) For Monitoring Seasonal Snow
    --
    doi.org/10.1029/2025WR042866 <-- shared paper
    --
    eos.org/features/satellite-rad <-- shared technical article
    --
    H/T @Jack Tarricone, PhD | Assistant Research Scientist @ NASA GSFC/UMD ESSIC | Remote Sensing and Snow Hydrology
    “[The authors] review[ed] 25 years of progress in using InSAR to measure changes in snow water equivalent (SWE) and snow depth and discuss[ed] what’s needed to extend these methods to basin-scale snow monitoring with NISAR. [They] hope it’s a useful resource for people interested in snow, SAR/InSAR, remote sensing, and hydrology in general…”
    #GIS #spatial #mapping #remotesensing #earthobservation #InterferometricSyntheticApertureRadar #InSAR #literaturereview #research #history #monitoring #spatialanalysis #spatiotemporal #seasonal #snow #water #hydrology #waterresources #snowpack #snowmelt #ablation #melt #runoff #snowwaterequivalent #SWE #NISAR #snowdepth #basin #snowphase #estimation #change #spatial #GIS #mapping #temporal #model #modeling #algorithm #ecosystems #environment #habitat #agriculture #farming #snowmass #satellite

  22. Who will be at #InterGeo next week? I'll be there representing a Hereon spin-off that I now work for.
    If you're involved in labelling #EarthObservation data, you might be interested.
    Drop me a line if you like to meet or speak to me at the conference when you see mee.

  23. Who will be at #InterGeo next week? I'll be there representing a Hereon spin-off that I now work for.
    If you're involved in labelling #EarthObservation data, you might be interested.
    Drop me a line if you like to meet or speak to me at the conference when you see mee.

  24. Cities In Great Britain Most Vulnerable To Extreme Heat Revealed
    (OS index examines which ‘urban heat islands’ suffer the most – and which cope the best with rising temperatures)
    --
    theguardian.com/environment/20 <-- shared media article
    --
    ordnancesurvey.co.uk/news/new- <-- shared technical article, OS Heat Vulnerability Index
    --
    H/T @joe Clarkson | Campaign Manager - Diffusion PR
    “[The H/T] work[ed] … closely with Ordnance Survey (OS) on new research into which of Britain's cities are most vulnerable to retaining extreme heat, and how that vulnerability shifts between now and the turn of the century as record temperatures continue to climb…
    The newly commissioned OS Heat Vulnerability Index scored 71 cities across Britain using #4EI satellite temperature readings, @Met Office climate modelling projections from 2040 to 2100, and Ordnance Survey data on the makeup of natural and made environments in our cities. Put together, it measures how global warming will impact future heat vulnerability, and how urban heat islands form; a process in which hard surfaces absorb solar radiation through the day to keep centres warm overnight, while rural environments cool.
    Anyone who has been unfortunate enough to stand on a Tube platform or walk through central London this summer, or tried to sleep through a heatwave in a flat that hasn't cooled in months already knows what retained heat feels like. But it's more than an uncomfortable feeling. Extreme heat is claiming an increasing number of lives each year, and location-based intelligence like this is essential to support decisions on how we effectively mitigate and protect against climate change to save lives and protect critical infrastructure.
    What this analysis does is put a number on extreme heat, and shows where it's heading. As Britain's climate continues to warm, the capacity of our cities to cool themselves naturally will only matter more…”
    #ClimateChange #Heat #UrbanHeat #Heatwave #Climate #Warming #Infrastructure #London #Portsmouth #Resilience #OrdnanceSurvey #Geospatial #Data #extremeweather #extremeweather #publichealth #publicsafety #deaths #UK #GreatBritain #OS #HeatVulnerabilityIndex #vulnerability #spatialanalysis #mapping #model #modeling #analysis #spatiotemporal #climate #climatemodeling #spatial #mapping #remotesensing #earthobservation #solarradiation #natural #manmade #retainedheat #mitigation #planning #policy #infrastructure #concrete #asphalt #buildings #hardsurfaces #globalwarming #urbanheatislands #cities #urban
    @Ordnance Survey

  25. Cities In Great Britain Most Vulnerable To Extreme Heat Revealed
    (OS index examines which ‘urban heat islands’ suffer the most – and which cope the best with rising temperatures)
    --
    theguardian.com/environment/20 <-- shared media article
    --
    ordnancesurvey.co.uk/news/new- <-- shared technical article, OS Heat Vulnerability Index
    --
    H/T @joe Clarkson | Campaign Manager - Diffusion PR
    “[The H/T] work[ed] … closely with Ordnance Survey (OS) on new research into which of Britain's cities are most vulnerable to retaining extreme heat, and how that vulnerability shifts between now and the turn of the century as record temperatures continue to climb…
    The newly commissioned OS Heat Vulnerability Index scored 71 cities across Britain using #4EI satellite temperature readings, @Met Office climate modelling projections from 2040 to 2100, and Ordnance Survey data on the makeup of natural and made environments in our cities. Put together, it measures how global warming will impact future heat vulnerability, and how urban heat islands form; a process in which hard surfaces absorb solar radiation through the day to keep centres warm overnight, while rural environments cool.
    Anyone who has been unfortunate enough to stand on a Tube platform or walk through central London this summer, or tried to sleep through a heatwave in a flat that hasn't cooled in months already knows what retained heat feels like. But it's more than an uncomfortable feeling. Extreme heat is claiming an increasing number of lives each year, and location-based intelligence like this is essential to support decisions on how we effectively mitigate and protect against climate change to save lives and protect critical infrastructure.
    What this analysis does is put a number on extreme heat, and shows where it's heading. As Britain's climate continues to warm, the capacity of our cities to cool themselves naturally will only matter more…”
    #ClimateChange #Heat #UrbanHeat #Heatwave #Climate #Warming #Infrastructure #London #Portsmouth #Resilience #OrdnanceSurvey #Geospatial #Data #extremeweather #extremeweather #publichealth #publicsafety #deaths #UK #GreatBritain #OS #HeatVulnerabilityIndex #vulnerability #spatialanalysis #mapping #model #modeling #analysis #spatiotemporal #climate #climatemodeling #spatial #mapping #remotesensing #earthobservation #solarradiation #natural #manmade #retainedheat #mitigation #planning #policy #infrastructure #concrete #asphalt #buildings #hardsurfaces #globalwarming #urbanheatislands #cities #urban
    @Ordnance Survey

  26. Cities In Great Britain Most Vulnerable To Extreme Heat Revealed
    (OS index examines which ‘urban heat islands’ suffer the most – and which cope the best with rising temperatures)
    --
    theguardian.com/environment/20 <-- shared media article
    --
    ordnancesurvey.co.uk/news/new- <-- shared technical article, OS Heat Vulnerability Index
    --
    H/T @joe Clarkson | Campaign Manager - Diffusion PR
    “[The H/T] work[ed] … closely with Ordnance Survey (OS) on new research into which of Britain's cities are most vulnerable to retaining extreme heat, and how that vulnerability shifts between now and the turn of the century as record temperatures continue to climb…
    The newly commissioned OS Heat Vulnerability Index scored 71 cities across Britain using #4EI satellite temperature readings, @Met Office climate modelling projections from 2040 to 2100, and Ordnance Survey data on the makeup of natural and made environments in our cities. Put together, it measures how global warming will impact future heat vulnerability, and how urban heat islands form; a process in which hard surfaces absorb solar radiation through the day to keep centres warm overnight, while rural environments cool.
    Anyone who has been unfortunate enough to stand on a Tube platform or walk through central London this summer, or tried to sleep through a heatwave in a flat that hasn't cooled in months already knows what retained heat feels like. But it's more than an uncomfortable feeling. Extreme heat is claiming an increasing number of lives each year, and location-based intelligence like this is essential to support decisions on how we effectively mitigate and protect against climate change to save lives and protect critical infrastructure.
    What this analysis does is put a number on extreme heat, and shows where it's heading. As Britain's climate continues to warm, the capacity of our cities to cool themselves naturally will only matter more…”
    #ClimateChange #Heat #UrbanHeat #Heatwave #Climate #Warming #Infrastructure #London #Portsmouth #Resilience #OrdnanceSurvey #Geospatial #Data #extremeweather #extremeweather #publichealth #publicsafety #deaths #UK #GreatBritain #OS #HeatVulnerabilityIndex #vulnerability #spatialanalysis #mapping #model #modeling #analysis #spatiotemporal #climate #climatemodeling #spatial #mapping #remotesensing #earthobservation #solarradiation #natural #manmade #retainedheat #mitigation #planning #policy #infrastructure #concrete #asphalt #buildings #hardsurfaces #globalwarming #urbanheatislands #cities #urban
    @Ordnance Survey

  27. Cities In Great Britain Most Vulnerable To Extreme Heat Revealed
    (OS index examines which ‘urban heat islands’ suffer the most – and which cope the best with rising temperatures)
    --
    theguardian.com/environment/20 <-- shared media article
    --
    ordnancesurvey.co.uk/news/new- <-- shared technical article, OS Heat Vulnerability Index
    --
    H/T @joe Clarkson | Campaign Manager - Diffusion PR
    “[The H/T] work[ed] … closely with Ordnance Survey (OS) on new research into which of Britain's cities are most vulnerable to retaining extreme heat, and how that vulnerability shifts between now and the turn of the century as record temperatures continue to climb…
    The newly commissioned OS Heat Vulnerability Index scored 71 cities across Britain using #4EI satellite temperature readings, @Met Office climate modelling projections from 2040 to 2100, and Ordnance Survey data on the makeup of natural and made environments in our cities. Put together, it measures how global warming will impact future heat vulnerability, and how urban heat islands form; a process in which hard surfaces absorb solar radiation through the day to keep centres warm overnight, while rural environments cool.
    Anyone who has been unfortunate enough to stand on a Tube platform or walk through central London this summer, or tried to sleep through a heatwave in a flat that hasn't cooled in months already knows what retained heat feels like. But it's more than an uncomfortable feeling. Extreme heat is claiming an increasing number of lives each year, and location-based intelligence like this is essential to support decisions on how we effectively mitigate and protect against climate change to save lives and protect critical infrastructure.
    What this analysis does is put a number on extreme heat, and shows where it's heading. As Britain's climate continues to warm, the capacity of our cities to cool themselves naturally will only matter more…”
    #ClimateChange #Heat #UrbanHeat #Heatwave #Climate #Warming #Infrastructure #London #Portsmouth #Resilience #OrdnanceSurvey #Geospatial #Data #extremeweather #extremeweather #publichealth #publicsafety #deaths #UK #GreatBritain #OS #HeatVulnerabilityIndex #vulnerability #spatialanalysis #mapping #model #modeling #analysis #spatiotemporal #climate #climatemodeling #spatial #mapping #remotesensing #earthobservation #solarradiation #natural #manmade #retainedheat #mitigation #planning #policy #infrastructure #concrete #asphalt #buildings #hardsurfaces #globalwarming #urbanheatislands #cities #urban
    @Ordnance Survey

  28. Cities In Great Britain Most Vulnerable To Extreme Heat Revealed
    (OS index examines which ‘urban heat islands’ suffer the most – and which cope the best with rising temperatures)
    --
    theguardian.com/environment/20 <-- shared media article
    --
    ordnancesurvey.co.uk/news/new- <-- shared technical article, OS Heat Vulnerability Index
    --
    H/T @joe Clarkson | Campaign Manager - Diffusion PR
    “[The H/T] work[ed] … closely with Ordnance Survey (OS) on new research into which of Britain's cities are most vulnerable to retaining extreme heat, and how that vulnerability shifts between now and the turn of the century as record temperatures continue to climb…
    The newly commissioned OS Heat Vulnerability Index scored 71 cities across Britain using satellite temperature readings, @Met Office climate modelling projections from 2040 to 2100, and Ordnance Survey data on the makeup of natural and made environments in our cities. Put together, it measures how global warming will impact future heat vulnerability, and how urban heat islands form; a process in which hard surfaces absorb solar radiation through the day to keep centres warm overnight, while rural environments cool.
    Anyone who has been unfortunate enough to stand on a Tube platform or walk through central London this summer, or tried to sleep through a heatwave in a flat that hasn't cooled in months already knows what retained heat feels like. But it's more than an uncomfortable feeling. Extreme heat is claiming an increasing number of lives each year, and location-based intelligence like this is essential to support decisions on how we effectively mitigate and protect against climate change to save lives and protect critical infrastructure.
    What this analysis does is put a number on extreme heat, and shows where it's heading. As Britain's climate continues to warm, the capacity of our cities to cool themselves naturally will only matter more…”

    @Ordnance Survey

  29. An MTG-I2 satellite launches on Ariane 6, completing Europe’s new weather satellite constellation.

    The launch of MTG-I2 from French Guiana completes the first trio of Meteosat Third Generation satellites, delivering high-resolution data and images every 2.5 minutes to transform weather forecasting and early warnings of extreme events.

    mediafaro.org/article/20260828

    #Satellite #Weather #ESA #Space #Science #Tech #Aerospace #Ariane6 #EarthObservation

  30. We reported earlier that Europe is getting better at detecting wildfires. The Forest Stewardship Council reached out and made clear to us that meanwhile we’re losing some of the forestry workers needed to prevent risks from accumulating on the ground.

    Satellites can identify the danger. They cannot clear vegetation, negotiate with landowners or apply ecological judgement on a steep hillside.

    movetheneedle.news/brands/euro

    #forestry #technology #FSC #wildfires #earthobservation #labor #Europe #EU

  31. Enhancements To The USGS Landsat Level 2 Surface Temperature And Emissivity Product For Collection3 Reprocessing
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    H/T @terry Sohl | USGS EROS Science Branch Chief
    “HIGHLIGHTS:
    • C3 advances Landsat TIR atmospheric correction, emissivity correction, and uncertainty estimates.
    • C3 enables global surface temperature products, including polar regions.
    • C3 retrievals improve accuracy and consistency across validation sites.
    • Split window and single channel methods diverge at extreme temperature conditions.
    • C3 and Landsat 10 support multi-decadal climate monitoring.
    ABSTRACT: The Landsat program provides the longest continuous global record of thermal infrared observations of the Earth's surface, underpinning critical applications in climate monitoring, water resources, ecosystem dynamics, urban heat analysis, and natural hazard assessment. The release of a global inventory of Landsat Collection 2 Level 2 surface temperature products by the U.S. Geological Survey (USGS) marked a major milestone in operational provision of Landsat thermal infrared analysis-ready data. Ongoing validations and community uses of Collection 2 have identified opportunities to further improve accuracy, uncertainty characterization, and emissivity correction across diverse atmospheric and surface conditions. In preparation for the planned Landsat Collection 3 reprocessing of the Landsat data record in the late 2020s, the USGS is implementing a coordinated set of enhancements to the Level 2 surface temperature products. These include revised emissivity estimation that leverages external datasets, improved atmospheric characterization and uncertainty propagation, expanded dynamic range for high temperature targets, consideration of split window atmospheric correction algorithm for Landsat 8 and 9, and decoupling of thermal infrared processing from visible to shortwave infrared constraints to enable surface temperature retrievals under low or no solar illumination conditions. These changes are designed to improve product quality and consistency across the Landsat record. Beyond near-term performance gains, the Collection 3 design establishes a scalable processing architecture to accommodate the expanded spectral and radiometric measurement capabilities of the forthcoming Landsat 10 mission. By preserving continuity across the Landsat 4–9 record while enabling future algorithm evolution, Landsat Collection 3 will provide a foundation for long-term, multi-decadal Earth system thermal infrared observations…”
    #GIS #spatial #mapping #satellite #remotesening #earthobservation #Landsat #thermalinfrared #TIR #surface #temperature #emissivity #thermal #infrared #analysisreadydata #Collection2 #Collection3 #opendata #atmosphericcorrection #global #climate #monitoring #waterresources #ecosystems #dynamics #urbanheat #spatialanalysis #spatiotemporal #naturalhazards #updates #EROS #USGS
    @USGS EROS | @USGS

  32. Global Atlas Will Track Human And Climate Impact On River Systems
    --
    news.cornell.edu/stories/2026/
    --
    “Rivers are critical resources that affect everything from watersheds to agriculture to energy. But rivers, in turn, have been impacted by humans, often in the form of hydraulic infrastructure such as dams and wells.
    A new [Cornell] project… will create a global record that shows how river systems around the world have changed under human influence over the last 75 years…”
    #GIS #spatial #mapping #research #spatialanalysis #spatiotemporal #water #waterresources #river #remotesensing #earthobservation #climatechange #climate #change #resources #humanimpacts #survey #monitoring #hydraulic #infrastructure #dams #wells #engineered #canals #global #spatialdata #opendata #history #anthropocene #freshwater #DARE #sediment #discharge #transport #temperature #fish #biodiversity #atlas #ecology #ecosystems #riverine #delta #model #modeling #machinelearning #AI
    #CornellUniversity | #CornellDuffieldCollegeofEngineering

  33. Project Sunroof [solar panel appraisal tool]
    --
    sunroof.withgoogle.com/ <-- shared (Google) project page
    --
    insights.sustainability.google/ <-- shared (Google) Environmental Insights Explorer (EIE) homepage
    --
    [this should NOT be considered an endorsement of a specific product or company, rather a post about a specific spatial/remote sensing use case]
    “Your own personalized solar savings estimator, powered by Google Earth imagery…”
    "Should I get solar?" → Gemini [AI] powered roof analysis, payback calc…
    #GIS #spatial #mapping #ProjectSunroof #solar #solarpower #alternateenergy #derived #solarenergy #solarpanels #exposure #orientation #sunlight #energy #residential #costeffective #remotesensing #earthobservation #AI #cost #economics #ReturnOnInvestment #ROI #solarsavings #estimator #spatialanalysis #spatiotemporal #USA #imagery #GoogleEarth #usecase #payback
    #Google

  34. Improving our Coasts with High-Resolution Land Cover Data [#NOAA]
    --
    coast.noaa.gov/states/stories/ <-- shared technical article
    --
    coast.noaa.gov/ccapatlas/ <-- on-demand, online NOAA CCAP Landcover Atlas
    --
    coast.noaa.gov/digitalcoast/da <-- #opendata C-CAP High-Resolution Land Cover
    --
    Use Case Examples:
    • Flood Inundation Modeling and Risk Assessment
    • Stormwater Management and Water Quality Protection
    • Heat Risk and Urban Forestry
    • Wetland Monitoring, Conservation, or Restoration Planning
    • Other – e.g., Discovering Gaps in Broadband Access
    --
    #GIS #spatial #mapping #NOAA #DigitalCoast #LandCover #CoastalManagement #GeospatialData #EnvironmentalData #ResilientCommunities #landcover #usecase #economics #remotesensing #earthobservation #opendata #floodinnundation #waterquality #water #hydrology #risk #hazard #spatialanalysis #spatiotemporal #stormwater #management #heatrisk #urbanforestry #wetland #monitoring #conservation #planning #resortation
    @noaa #NOAAOfficeForCoastalManagement