#earthobservation — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #earthobservation, aggregated by home.social.
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Satellite view of Ohio — 2026-09-10
Imagery: MODIS_Terra_CorrectedReflectance_TrueColor
https://worldview.earthdata.nasa.gov/?v=-84.82,38.4,-80.52,42.32&l=MODIS_Terra_CorrectedReflectance_TrueColor&t=2026-09-10Imagery: NASA GIBS #satellite #NASA #EarthObservation
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Satellite view of Ohio — 2026-09-10
Imagery: MODIS_Terra_CorrectedReflectance_TrueColor
https://worldview.earthdata.nasa.gov/?v=-84.82,38.4,-80.52,42.32&l=MODIS_Terra_CorrectedReflectance_TrueColor&t=2026-09-10Imagery: NASA GIBS #satellite #NASA #EarthObservation
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Satellite view of Ohio — 2026-09-10
Imagery: MODIS_Terra_CorrectedReflectance_TrueColor
https://worldview.earthdata.nasa.gov/?v=-84.82,38.4,-80.52,42.32&l=MODIS_Terra_CorrectedReflectance_TrueColor&t=2026-09-10Imagery: NASA GIBS #satellite #NASA #EarthObservation
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Satellite view of Ohio — 2026-09-10
Imagery: MODIS_Terra_CorrectedReflectance_TrueColor
https://worldview.earthdata.nasa.gov/?v=-84.82,38.4,-80.52,42.32&l=MODIS_Terra_CorrectedReflectance_TrueColor&t=2026-09-10Imagery: NASA GIBS #satellite #NASA #EarthObservation
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Satellite view of Ohio — 2026-09-10
Imagery: MODIS_Terra_CorrectedReflectance_TrueColor
https://worldview.earthdata.nasa.gov/?v=-84.82,38.4,-80.52,42.32&l=MODIS_Terra_CorrectedReflectance_TrueColor&t=2026-09-10Imagery: NASA GIBS #satellite #NASA #EarthObservation
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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.
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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.
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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.
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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.
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A Scalable Cloud-Native Framework for Satellite Rainfall-Driven Landslide Early Warning in Data-Scarce Regions
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https://doi.org/10.5194/egusphere-2026-4588 <-- shared technical article
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https://ee-yunuscool.projects.earthengine.app/view/alert-app <-- shared web map based ALERT, Automated Landslide Early Risk Tracker
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https://doi.org/10.1186/s40677-022-00218-1 <-- shared paper
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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 -
A Scalable Cloud-Native Framework for Satellite Rainfall-Driven Landslide Early Warning in Data-Scarce Regions
--
https://doi.org/10.5194/egusphere-2026-4588 <-- shared technical article
--
https://ee-yunuscool.projects.earthengine.app/view/alert-app <-- shared web map based ALERT, Automated Landslide Early Risk Tracker
--
https://doi.org/10.1186/s40677-022-00218-1 <-- 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 -
A Scalable Cloud-Native Framework for Satellite Rainfall-Driven Landslide Early Warning in Data-Scarce Regions
--
https://doi.org/10.5194/egusphere-2026-4588 <-- shared technical article
--
https://ee-yunuscool.projects.earthengine.app/view/alert-app <-- shared web map based ALERT, Automated Landslide Early Risk Tracker
--
https://doi.org/10.1186/s40677-022-00218-1 <-- 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 -
A Scalable Cloud-Native Framework for Satellite Rainfall-Driven Landslide Early Warning in Data-Scarce Regions
--
https://doi.org/10.5194/egusphere-2026-4588 <-- shared technical article
--
https://ee-yunuscool.projects.earthengine.app/view/alert-app <-- shared web map based ALERT, Automated Landslide Early Risk Tracker
--
https://doi.org/10.1186/s40677-022-00218-1 <-- 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 -
A Scalable Cloud-Native Framework for Satellite Rainfall-Driven Landslide Early Warning in Data-Scarce Regions
--
https://doi.org/10.5194/egusphere-2026-4588 <-- shared technical article
--
https://ee-yunuscool.projects.earthengine.app/view/alert-app <-- shared web map based ALERT, Automated Landslide Early Risk Tracker
--
https://doi.org/10.1186/s40677-022-00218-1 <-- 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 -
Sub-Seasonal Forecasting Of Cropland Productivity Anomalies Using Satellite Soil Moisture In Water-Limited Environments
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https://doi.org/10.1016/j.rse.2026.115645 <-- shared paper
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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 -
Sub-Seasonal Forecasting Of Cropland Productivity Anomalies Using Satellite Soil Moisture In Water-Limited Environments
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https://doi.org/10.1016/j.rse.2026.115645 <-- shared paper
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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 -
Sub-Seasonal Forecasting Of Cropland Productivity Anomalies Using Satellite Soil Moisture In Water-Limited Environments
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https://doi.org/10.1016/j.rse.2026.115645 <-- 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 -
Sub-Seasonal Forecasting Of Cropland Productivity Anomalies Using Satellite Soil Moisture In Water-Limited Environments
--
https://doi.org/10.1016/j.rse.2026.115645 <-- 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 -
Sub-Seasonal Forecasting Of Cropland Productivity Anomalies Using Satellite Soil Moisture In Water-Limited Environments
--
https://doi.org/10.1016/j.rse.2026.115645 <-- 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 -
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 -
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 -
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 -
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 -
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 -
Interferometric Synthetic Aperture Radar (InSAR) For Monitoring Seasonal Snow
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https://doi.org/10.1029/2025WR042866 <-- shared paper
--
https://eos.org/features/satellite-radar-advances-could-transform-global-snow-monitoring <-- 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 -
Interferometric Synthetic Aperture Radar (InSAR) For Monitoring Seasonal Snow
--
https://doi.org/10.1029/2025WR042866 <-- shared paper
--
https://eos.org/features/satellite-radar-advances-could-transform-global-snow-monitoring <-- 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 -
Interferometric Synthetic Aperture Radar (InSAR) For Monitoring Seasonal Snow
--
https://doi.org/10.1029/2025WR042866 <-- shared paper
--
https://eos.org/features/satellite-radar-advances-could-transform-global-snow-monitoring <-- 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 -
Interferometric Synthetic Aperture Radar (InSAR) For Monitoring Seasonal Snow
--
https://doi.org/10.1029/2025WR042866 <-- shared paper
--
https://eos.org/features/satellite-radar-advances-could-transform-global-snow-monitoring <-- 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 -
Interferometric Synthetic Aperture Radar (InSAR) For Monitoring Seasonal Snow
--
https://doi.org/10.1029/2025WR042866 <-- shared paper
--
https://eos.org/features/satellite-radar-advances-could-transform-global-snow-monitoring <-- 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 -
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. -
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)
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https://www.theguardian.com/environment/2026/sep/08/cities-great-britain-most-vulnerable-extreme-heat-revealed <-- shared media article
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https://www.ordnancesurvey.co.uk/news/new-urban-heat-islands-analysis <-- shared technical article, OS Heat Vulnerability Index
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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 -
Satellite view of Ohio — 2026-09-07
Imagery: MODIS_Terra_CorrectedReflectance_TrueColor
https://worldview.earthdata.nasa.gov/?v=-84.82,38.4,-80.52,42.32&l=MODIS_Terra_CorrectedReflectance_TrueColor&t=2026-09-07Imagery: NASA GIBS #satellite #NASA #EarthObservation
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Python is use useful for reducing manual work in geospatial analysis. Get info from:
QGIS & Claude Code (MCP) Integration:
https://rsandgis.me/articles/qgis-claude-code-mcp-integration.htmlhttps://Cloud-Optimized GeoTIFF Python Tutorial
https://rsandgis.me/articles/cloud-optimized-geotiff-python-tutorial.htmlPoint Cloud Classification via PDAL & Python
https://rsandgis.me/articles/deep-learning-point-cloud-pdal.html#Python #Geospatial #DataScience #QGIS #GIS #SpatialAnalysis #PyQGIS #EarthObservation #OpenSource
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Isnt it harder to find in-depth, information on geospatial tech, which is why I want to share a fantastic independent resource:
Run by a geospatial expert, its an blog currently focused heavily on RS and GIS, machine learning and modern spatial data workflows. It completely bridges the gap between Earth sciences and modern programming.
#GIS #RemoteSensing #GeoAI #EarthObservation #Python #QGIS #SpatialAnalysis #DataScience #LiDAR #OpenScience
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India (ISRO) has successfully launched its first geostationary Earth observation imager.
Operating from a geostationary orbit 36,000 kms above the equator, the new Earth observation satellite delivers continuous, real-time multispectral imaging of the Indian subcontinent and Indian Ocean region:
This satellite is capable of capturing high-resolution optical, near-infrared, and thermal infrared channels to monitor storm formation, sea surface temperatures, and land vegetation indices. https://satnews.com/2026/08/31/isro-deploys-first-geostationary-earth-imager-as-navic-constellation-operates-below-baseline-threshold/ #Space #India #Satellite #EarthObservation #Imaging #SatelliteImaging #OpticalImaging #Infrared #ThermalImaging #GeostationaryOrbit #GSO #IndianSpaceResearchOrganisation #ISRO
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Great opportunity for #arctic researchers interested in #earthobservation
https://events.coastal.hub.copernicus.eu/e/the-copernicus-arctic-hub -
At night, the Earth tells a different story. 🌃 From orbit, city lights trace where we live and build, a glowing map of human civilization and energy. Remote sensing turns those lights into data on population and growth. 🛰️
#RemoteSensing #NightLights #Geography #Satellites #EarthObservation #Cities
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Satellite view of Ohio — 2026-09-04
Imagery: MODIS_Terra_CorrectedReflectance_TrueColor
https://worldview.earthdata.nasa.gov/?v=-84.82,38.4,-80.52,42.32&l=MODIS_Terra_CorrectedReflectance_TrueColor&t=2026-09-04Imagery: NASA GIBS #satellite #NASA #EarthObservation
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In 2025, an area equivalent to **eight Norways** burned across the planet.
That sounds like a catastrophe.
- It isn't quite that simple.Not every fire is a disaster. Some are natural. Some are deliberate. Some are tiny. Some are enormous.
So what actually happened?
Our latest feature digs into the satellite data behind **265 million hectares of recorded burned area** — and asks a more interesting question than simply *how many fires were there?*
**What the hell was actually going on?**
🔥 **EIGHT NORWAYS BURNED IN ONE YEAR**
A new feature from RABAGAS Magazine.
https://rabagas.ghost.io/eight-norways-burned-in-one-year/#Rabagasmagazine #Wildfires #ClimateScience #EarthObservation #ClimateCrisis #SatelliteData #DataJournalism #Climate
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To Predict Tree Death, Scientists Tapped Gamma Rays To Peer Underground
(Airborne radiation sensors could help forecast and prevent drought-driven tree mortality_
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https://www.science.org/content/article/predict-tree-death-scientists-tapped-gamma-rays-peer-underground <-- shared technical article
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https://doi.org/10.1029/2026GL122182 <-- shared paper
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H/T @hannah Richter
“Over an 18-month period starting in 2023, the dense forests of Western Australia [WA] experienced a record-setting drought. Jarrah trees towering 35 metres high died off in patchy brown splotches, turning 400 square kilometres - 3% of the forest - into brittle, fire-prone stands. The event led researchers to wonder whether there was a better way to predict where such die-offs might occur both there and in other forests, a problem that has long been tricky to solve because important factors such as soil depth are hidden underground…
Now, those same researchers have unveiled a surprising new tool for predicting tree mortality: gamma rays [link above.] Resulting from the natural decay of the potassium-40 isotope from granite-rich bedrock, the radiation acts as a proxy for soil depth, which in turn signals how much water a tree can access during drought. The new method could be applied to other highly weathered soils, which cover one-third of Earth’s ice-free land...”
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"... PLAIN LANGUAGE SUMMARY: During a record-breaking drought and heat event in 2023–2024, forests in southwestern Australia experienced widespread, patchy die-off. While we know that extreme weather triggers these events, it is often a hidden factor, the thickness of soil and the depth to underlying bedrock, that determines which trees live or die. Trees growing in shallow soil over solid rock are highly vulnerable due to limited water storage. Here, [they] show how to map these hidden zones from the air using gamma rays that are naturally emitted by potassium in the ground. Like southwestern Australia, many parts of the world have highly weathered soils where potassium has been washed out of the upper layers of soil. However, [they] showed that higher potassium areas signal that potassium-rich bedrock is closer to the surface and this is sensitive for tens of meters. By comparing gamma ray maps with ground-based geophysical surveys and satellite data, [they] showed that these potassium hotspots accurately predict where forests are most likely to experience die-off during a drought. These types of soils cover about one-third of the Earth's land, so the method provides a powerful new tool for managers to identify and protect vulnerable forests from future, hotter droughts…”
#GIS #spatial #mapping #spatialanalysis #spatiotemporal #Australia #WesternAustralia #WA #forests #vegetation #bush #jarrah #karri #drought #heat #extremedrought #extremeweather #climatechange #water #waterresources #dieoff #soil #weathering #erosion #moisture #nutrients #airborne #gammarays #GRS #granite #gneiss #bedrock #geology #potassium40 #potassium #K #remotesensing #earthobservation #groundwater #interstitial #subsurface #waterstorage #electricalresistivitytomography -
Nepal glacier collapse and flood (after)
#china #earthexplorer #earthfromspace #earthobservation #esa #europeanspaceagency #glaciercollapse #landsat9
▶️ 1 new picture from ESA (Flickr) https://commons.wikimedia.org/wiki/File:Nepal_glacier_collapse_and_flood_%28after%29_%2855504451912%29.jpg
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On-Demand Global Landsat Evapotranspiration Product - Development, Evaluation, And Dissemination
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https://doi.org/10.1016/j.rse.2026.115633 <-- shared paper
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https://espa.cr.usgs.gov <-- shared (open data) USGS EROS Science Processing Architecture (ESPA) platform
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https://etdata.org/ <-- OpenET SSEBop platform implementation (water management)
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https://www.usgs.gov/landsat-missions/landsat-collection-2-provisional-actual-evapotranspiration-science-product <-- shared USGS Landsat Collection 2 Provisional Actual Evapotranspiration Science Product
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H/T @mac Friedrichs | Remote Sensing Scientist, KBR | USGS EROS
“This paper summarizes new achievements in developing and distributing the Global Landsat Level-3 Evapotranspiration (ET) product. It is freely available through the USGS EROS Science Processing Architecture (ESPA) platform (2nd link above). 🛰️ …
Since the product launch in June 2020, there have been over 1.2 million Landsat-based ET orders around the world. This indicates increasing awareness and application of the ET data to help understand and manage the relationships among food, energy, and water resources. 🌽💧 …
It features the ESPA workflow and evaluation of the upgraded SSEBop model using a variety of observational datasets and hydrologic regions, and examination against OpenET SSEBop platform implementation (3rd link above.)…”
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“HIGHLIGHTS
• ESPA platform allows access to on-demand, global, Landsat-based, ET products.
• SSEBop model has been used to create ET data since 1982 through ESPA.
• A quick estimation of field-scale crop consumptive water use can be achieved.
• Numerous orders reflect worldwide extensive interest and utilization of the data.
• Method, workflow, and performance of the actual ET data are presented in the study..."
#EROSScienceProcessingArchitecture #climate #global #Evapotranspiration #ET #GIS #spatial #mapping #remotesensing #earthobservation #water #hydrology #opendata #Landsat #OpenET #SSEBop #WaterManagement #Agriculture #USGS #EROS #datadelivery #food #foodsecurity #energy #water #watersecurity #model #modeling #ESPA #farming #cropland #wateruse #waterresources #workflow
@USGS @EROS -
Satellite view of Ohio — 2026-09-01
Imagery: MODIS_Terra_CorrectedReflectance_TrueColor
https://worldview.earthdata.nasa.gov/?v=-84.82,38.4,-80.52,42.32&l=MODIS_Terra_CorrectedReflectance_TrueColor&t=2026-09-01Imagery: NASA GIBS #satellite #NASA #EarthObservation
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Satellite view of Ohio — 2026-08-29
Imagery: MODIS_Terra_CorrectedReflectance_TrueColor
https://worldview.earthdata.nasa.gov/?v=-84.82,38.4,-80.52,42.32&l=MODIS_Terra_CorrectedReflectance_TrueColor&t=2026-08-29Imagery: NASA GIBS #satellite #NASA #EarthObservation
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Inside Kourou: how a satellite is readied to watch Europe’s skies and prevent natural disasters.
Engineers from ArianeGroup, Arianespace, CNES, Eumetsat, ESA and Thales Alenia Space describe, step by step, how an Ariane 6 rocket and a 3.8-tonne weather satellite were brought together to the launch pad at Europe’s Spaceport.
#Aerospace #ESA #Tech #Weather #EarthObservation #Satellite #Science #ArianeGroup #Arianespace #CNES #Eumetsat #Thales
