#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 -
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 -
Mapping Flood Agents In The Northern Pantanal Wetland Using Multiple Spatio-Temporal GIS And Remote Sensing Techniques
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https://doi.org/10.1007/s13157-026-02082-3 <-- shared paper
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H/T @renato Gatto de Morais | Geógrafo | Mestre em Recursos Hídricos | Doutorando em Geografia
“Combining MODIS data, TRMM, river discharge, a digital terrain model, and multinomial logistic regression, [the authors] identified three predominant classes of flood origin: local rainfall, bank overflow, and mixed areas. The model achieved a Nagelkerke Pseudo-R² of 0.63 and a classification accuracy of up to 81.2% (10-fold cross-validation). To the best of [their] knowledge, this is the first explicit approach to mapping flood agents for this wetland, a component that has historically remained unmapped despite its recognized influence on habitats and biodiversity…”
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“The mapping of functional hydrologic units is crucial for enhancing our understanding of flooding and hydroecological processes in large wetlands. These units are typically defined by flooding frequency, duration, and magnitude, but another important hydrologic characteristic is the origin of flooding, known as the flood agent. This study presents an empirical framework utilizing remote sensing and GIS procedures for modeling flood agents in the northern Pantanal wetland. Eleven spatial data layers were derived from multi-year MODIS flood maps, daily rainfall estimates from the TRMM 3B42 grid, in situ discharge data, the BEST Digital Terrain Model, and a hydrographic network layer. These layers were tested for their predictive power in a multinomial logistic regression mapping model. Model performance metrics, along with qualitative validation of mapping outcomes using in situ flooding measurements, and vegetation and soil data from field test sites, support the plausibility of the proposed mapping scheme. However, they also highlight the challenges of flood agent mapping in large tropical wetlands…”
#GIS #spatial #mapping #MODIS #TRMM #riverdischarge #digitalterrainmodel #multinomiallogisticregression #geostatistics #Pantanal #Cuiaba #Brazil #water #hydrology #spatialanalysis #spatiotemporal #remotesensing #earthobservation #flood #flooding #source #type #floodagent #tropical #wetland #habitat #biodiversity #ecosystem #hydrologicunit #hydroecology #model #modeling #rainfall #precipitation #weather #climate #discharge #network #metrics -
Details on the #flood catastrophe in #Nepal -
you may also read this as a story of why #technology fails, and will always fail to prevent suffering like this.
Countries that don't coordinate,
commercial #satellite #earthObservation companies that have the #data but do not act,
#sensor stations that get washed away.you may hope that #technology is the solution .. maybe ... (?)
1/2
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Challenges In The Use Of Local Data For Regional Scale Mapping Of C And N Stocks In The Continuous Permafrost Zone At The Yukon Coastal Plain | Heatwave Risks To Tipping Point Of Permafrost
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https://doi.org/10.5194/soil-12-113-2026 <-- shared paper
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https://doi.org/10.1038/s41558-026-02603-2 <-- shared paper
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https://www.theguardian.com/environment/2026/aug/20/tipping-points-heatwaves-wildfires-permafrost-climate-crisis <-- shared media article
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https://www.cbc.ca/news/canada/north/permafrost-slumps-herschel-island-qikiqtaruk-yukon-9.7168780 <-- shared media article
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[putting together two different ‘sorts’/focuses of research/reporting, but…]
H/T @gustaf Hugelius | Professor at Stockholm University
--
“Permafrost soils are particularly vulnerable to climate change. To assess and improve estimations of carbon (C) and nitrogen (N) budgets it is necessary to accurately map soil carbon and nitrogen in the permafrost region. In particular, soil organic carbon (SOC) stocks have been predicted and mapped by many studies from local to pan-Arctic scales. Several studies have been carried out at the Canadian Beaufort Sea coast, though no regional maps of terrestrial carbon stocks based on spatial modelling has been conducted yet. This study combines available field data from the Canadian Yukon coastal plain and uses it to map regional SOC and N stocks using the machine learning algorithm random forest and environmental variables based on remote sensing data. [The authors] developed models using the data for the entire region and separate models for the coastal mainland area and Qikiqtaruk Herschel Island. Each model was used to map SOC and N stocks for its respective area. [They] assessed the performance of the different random forest models by using crossvalidation. [They] further assessed model results using the Area of Applicability (AOA) method and the quantile regression forest approach, comparing the results and discussing their implications within the context of both methods. [They] explore[d] local differences in soil properties and how soil data distribution across the region affects the accuracy of the predictions of SOC and N stocks..."
#permafrost #soils #geology #climatechange #temperature #thawing #melting #emissions #CO2 #methane #carbon #nitrogen #GIS #spatial #mapping #Qikiqtaruk #HerschelIsland #Yukon #Canada #soilorganiccarbon #SOC #arctic #cryosphere #BeaufortSea #coast #coastal #machinelearning #model #modeling #remotesensing #earthobservation #carbonstocks #island #mainland #spatialanalysis #scale -
Comparing DESIS Hyperspectral and Landsat 10 Simulated Superspectral Data for Crop Type Classification in California’s Central Valley
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https://doi.org/10.3390/rs18142282 <-- shared paper
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https://www.usgs.gov/publications/comparing-desis-hyperspectral-and-landsat-10-simulated-superspectral-data-crop-type <-- shared USGs publication page
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H/T @USGS
“How can we get better at classifying crops from space? 🛰️🌽
Some types of satellite imagery are better at distinguishing crops than others. A USGS study compared two approaches and found one produced more accurate crop maps.
Here's what the researchers found:
• Hyperspectral imagery achieved 86% accuracy, compared to 75% for simulated superspectral imagery.
• Using just 14 carefully selected hyperspectral bands produced nearly the same results as using all 60 DLR Earth Sensing Imaging Spectrometer (DESIS) bands, showing those wavelengths contain much of the information needed to distinguish crop types.
• Using superspectral imagery on the upcoming Landsat 10 mission will allow for routine tracking of crops and enhance finer crop mapping.
• The findings help scientists identify which wavelengths provide the most useful information for crop mapping and future remote sensing applications.
Better crop maps will help governments and scientists track global agriculture, monitor the current crop season, and study agricultural trends…”
--
“HIGHLIGHTS:
• What are the main findings?
- The 14 DESIS hyperspectral narrowbands (10 nm) aligned with the Landsat 10 (formerly Landsat Next) spectral dataset produced similar accuracy results to the full 60-band DESIS hyperspectral dataset for classifying crop types. These 14 DESIS narrowbands resulted in higher accuracy than the 14 simulated Landsat 10 superspectral broadbands.
- When using DESIS narrowbands, Support Vector Machine (SVM) resulted in higher accuracy than Random Forest (RF).
• What are the implications of the main findings?
- A carefully selected set of 14 DESIS hyperspectral narrowbands (10 nm) can achieve classification accuracy comparable to those obtained using all 60 DESIS narrowbands across the 400–1000 nm range. These 14 strategically positioned narrowbands classified crop types with higher classification accuracy than the corresponding 14 Landsat 10 superspectral broadbands within the same spectral range.
- This study underscores the importance of multi-temporal imagery across the full crop-growing season for achieving more detailed and accurate crop type classifications. Such temporal coverage is more feasible with the planned Landsat 10 routine acquisition of broadband imagery than with task-based hyperspectral collections…”
#hyperspectral #superspectral #optimalbands #randomforest #supportvectormachine #agriculture #crops #croptype #classifaction #croplands #California #CentralValley #GIS #spatial #mapping #remotesensing #earthobservation #imagery #DESIS #Landsat #Landsat10 #satellite #spatialanalysis #spatiotemporal #global #AI #machinelearning #model #modeling #SupportVectorMachine #SVM #RandomForest #RF #GoogleEarthEngine
@USGS -
Enhancements To The USGS Landsat Level 2 Surface Temperature And Emissivity Product For Collection3 Reprocessing
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https://doi.org/10.1016/j.rse.2026.115563 <-- shared paper
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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 -
Satellites, HAPS, and aircraft are quietly becoming something more than relays. They're turning into compute nodes.
The Datacom Industry Association Aerospace #WorkingGroup just released a whitepaper on this shift: "Aerospace-enabled Services — The Edge-Cloud Continuum Beyond the Atmosphere."
📄 Read the whitepaper now: https://datacom-ia.eu/2026/07/24/aerospace-wg-releases-whitepaper-on-edge-cloud-continuum-beyond-the-atmosphere/
#Aerospace #EdgeComputing #NTN #EarthObservation #HAPS #Satellite #DigitalInfrastructure #DIA
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EU delays release of Copernicus imagery over Gulf of Oman
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Refined Modeling of Arctic Circumpolar Building Stock Increases Estimated Mid-Century Permafrost Degradation Damages
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https://doi.org/10.1029/2026EF008578 <-- shared paper
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https://www.thearcticinstitute.org/climate-change-geopolitics-monitoring-thawing-permafrost/ | https://www.thearcticinstitute.org/dwindling-arctic-sea-ice-impacts-permafrost-health/ <-- shared technical articles
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https://www.theguardian.com/cities/2016/oct/14/thawing-permafrost-destroying-arctic-cities-norilsk-russia <-- shared media article
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https://news.grida.no/new-map-shows-extent-of-permafrost-in-northern-hemisphere <-- shared technical article
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H/T @elias Manos
“Why the increase?
Our understanding of climate risk is only as good as our understanding of our exposure to hazards. The better we can account for what is at risk, the better we can measure risk in a changing world.
In this new study [link above], [they] investigate[d] how damage to the building stock across the Arctic, a key impact of permafrost degradation, is underestimated because of underdeveloped exposure information. With National Science Foundation (NSF) supercomputers and 400 TB of Vantor satellite imagery, [they] detected building footprints across the Arctic and classified their use types using deep learning models. Then, using Polar Geospatial Center's ArcticDEM digital surface model, [they] estimated the total floor space of each residential building. This move from 2D to 3D representation of the building stock was the largest contributor to increased building damage.
Properly estimating this consequence is necessary for understanding the near future of the Arctic economy. Knowing the magnitude of damages is critical for sustaining the communities and livelihoods of more than 5 million people that call the Arctic home. There are also much broader implications. With the Arctic continuing to emerge as a strategic centerpiece in global affairs and the global economy, accurately quantifying the physical shocks to its built environment will allow researchers to more effectively represent the Arctic in global climate economic models. More precise international policymaking will also be enabled by these improvements.
Ultimately, this research highlights a similar challenge in completely different regions of the world (e.g., Southeast Asia, Sub-Saharan Africa) where exposure is constantly evolving alongside rapid population growth and urbanization. Building stock information can quickly become outdated as these changes occur; satellite remote sensing and AI are key players in keeping up with these changes and supporting data-driven disaster risk management…”
#arctic #circumpolar #permafrost #model #modeling #spatialanalysis #spatiotemporal #GIS #spatial #mapping #melting #degradation #damage #cost #economics #risk #hazard #climaterisk #climatechange #remotesensing #HPC #earthobservation #ArcticDEM #buildingfootprint #LLM #AI #machinelearning #engineering #economy #buildingstock #community #policy #planning #geopolitics #risk #management @UConn Research -
Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
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https://doi.org/10.1038/s41598-026-52915-8 <-- shared paper
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https://doi.org/10.1007/s11600-022-00943-z <-- shared paper
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H/T @Kuldeep Dutta | Geology-Earth Science
“… In hilly regions transitioning rapidly to low gradient alluvial plains, localized hydrometeorological triggers can instantly scale into devastating basin wide disasters. This study dissects the September 2020 cascading hazard in parts of the Arunachal Pradesh-Assam corridor to quantify the rapid coupling between upstream hillslopes and downstream floodplains.
Check out the [attached graphical abstract figure] for an integrated visual workflow of the entire disaster continuum from hillslope failure to floodplain transformation...”
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“Extreme precipitation in the Eastern Himalaya is increasingly associated with coupled hillslope-floodplain hazards. This study examines the 17th-18th September 2020 rainfall event in Arunachal Pradesh initiating landslides and its downstream impacts in Assam, India, using multi-sensor satellite data and long-term rainfall records. Sentinel-2 imagery was used to map landslides and debris flows, Sentinel-1 SAR data to delineate flood extent, and IMD gridded rainfall (1996–2020) to analyse rainfall spell characteristics. The event triggered widespread slope failures, localized landslide damming, and a subsequent breach, generating sediment-laden flows that inundated ~ 100 km² of the Dhemaji floodplain. A backscatter-derived Relative Flood Volume Index (RFVI) indicates spatial variability in inundation intensity, although it does not represent absolute flood volume. Rainfall analysis suggests that antecedent wetness from preceding spells preconditioned slopes, while peak daily rainfall (> 170 mm day−1) initiated landsliding. Power-law scaling shows negligible dependence of intensity on duration (R2 ≈ 0.0004), whereas cumulative rainfall exhibits a stronger relationship with duration (R2 ≈ 0.54). These results indicate distinct roles of rainfall intensity and accumulation in controlling landslide initiation and downstream flooding, respectively, highlighting the importance of compound rainfall forcing in rapid hydrogeomorphic cascades…”
#EarthScience #RemoteSensing #Himalayas #NaturalHazards #ClimateChange #ScientificReports #GeospatialAnalysis #DisasterMitigation #Landslide #trigger #Flooding #massmovement #extremeweather #engineeringgeology #floodplain #innundation #hillslope #fluvial #pluvial #alluvial #sediment #sedimentation #hydrometeorology #ArunachalPradesh #Assam #India #Brahmaputra #risk #hazard #geology #engineeringgeology #remotesensing #earthobservation #spatialanalysis #spatiotemporal #disaster #hydrogeomorphology #workflow -
Comparative Hydro-Climatic Datasets For Catchment-Wise Linked Water Fluxes And Storage Changes Across South America
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https://doi.org/10.3389/fenvs.2026.1764771 <-- shared paper
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https://doi.org/10.1038/s43247-026-03661-2 <-- shared paper
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https://doi.org/10.1002/joc.6443 <-- shared paper
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https://www.pik-potsdam.de/en/news/latest-news/from-droughts-to-floods-climate-change-and-migration-in-peru | https://publications.iom.int/books/evaluacion-de-la-evidencia-cambio-climatico-y-migracion-en-el-peru <-- shared 2021 Peru hydroclimate technical article | report
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https://youtu.be/Ngbm0gsmYAw?si=haqV7t15pGkEmJB8 <-- shared overview video
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#water #GIS #spatial #mappping #spatialanalysis #spatiotemporal #remotesensing #earthobservation #Hydrology #Hydroclimatology #ClimateChange #extremeweather #WaterResources #WaterSecurity uncertainity #SouthAmerica #ClimateData #PeerReview #OpenScience #Hydrometeorology #opendata #datasets #rainfall #precipitation #fluvial #heatwave #temperature #changing #consistency #flood #flooding #drought #riskmanagement #risk #hazard #earthsystems #resilience #waterquality #waterpollution #model #modeling #monitoring #records #hydroclimate #hydrogeomorphology #review #SouthAmerica #planning #policy #sustainability #evapotranspiration #runoff #waterstorage #SAHCD -
Remote Sensing And The New Global River Science
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https://doi.org/10.1038/s44221-026-00665-2 <-- shared paper
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“Rivers impact the well-being of humans and the environment. As they increasingly face planetary-scale stressors, it is critically important to monitor and understand rivers at the global scale. As the only synoptic resource for global primary data on rivers, satellite remote sensing has recently begun to provide unprecedented opportunities for the monitoring, understanding, and prediction of global river behaviour. Despite these advances, the role of satellite remote sensing in global river science has still not been fully explored. New satellite systems and algorithms will enable substantial improvements in river measurements, provide new answers to long-standing or newly emerging scientific questions, and eventually update basic knowledge of rivers to advance global river science. In this [paper they] explore how remote sensing has been used to study the world’s rivers, examine challenges and opportunities for further advancing our understanding of rivers using existing and upcoming sensors, and identify possible solutions and future research directions…”
#GIS #spatial #mapping #water #hydrology #satellite #remotsesensing #earthobservation #hydrography #spatialanalysis #spatiotemporal #physicalgeography #change #river #global #model #modeling #research #hydrogeomorphology #geomorphometry #riverine #humanimpacts #waterquality #waterresources #watermanagement #infrastructure #lake #reservoir #dam #impoundment #canals #avulsion #overbank #flood #flooding #erosion #sedimentation #morphology #network #downstream -
Remote Sensing, Real Impact - Technology-Driven Conservation At The Jane Goodall Institute
(How the Jane Goodall Institute [JGI] is using satellite imagery and spatial technology to help people and animals thrive.)
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https://www.geoweeknews.com/news/conservation-at-the-jane-goodall-institute <-- shared technical article
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https://www.iceye.com/blog/government/solutions/how-the-jane-goodall-institute-uses-iceye-sar-to-protect-ecosystems <-- shared technical article
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[this post should not be seen as endorsement of a particular provider and/or approach]
“Dr. Jane Goodall’s groundbreaking research at Gombe National Park in Tanzania began in 1960, and transformed our understanding of chimpanzees and redefined the relationship between humans and animals. Dr. Goodall dedicated her life to conservation and community empowerment. Her recent passing marks the end of an extraordinary career in ethology and conservation science. Yet her legacy lives on powerfully through the Jane Goodall Institute [JGI] and the countless individuals and communities she inspired worldwide. Her pioneering work revealed that chimpanzees make and use tools, have complex social structures, and possess individual personalities; discoveries that fundamentally challenged our understanding of what it means to be human, for the better…
The institute's current work embodies Dr. Goodall's profound philosophy: that humanity can only reach its full potential "when our clever brains and our compassionate hearts are connected."
This principle guides every aspect of JGI's conservation science strategy, from analyzing satellite data to providing local communities with the tools they need to strengthen their environment. Dr. Goodall's vision that conservation must be rooted in compassion, scientific rigor, and respect for local communities continues to shape the institute's innovative approach to using technology for conservation action.
As time has gone on, geospatial technologies have become increasingly indispensable to this mission, enabling JGI to monitor forest health in real-time, map critical chimpanzee habitats and migration corridors, and measure the tangible impact of community-led conservation initiatives. But perhaps most importantly, these technologies help the communities understand that the lives of people, animals and their shared environment are all interconnected…”
#ConservationTechnology #illegal #Deforestation #hunting #mining #GombeNationalPark #remotesensing #radar #earthobservation #chimpanzee #conservation #communityempowerment #JaneGoodallInstitute #JGI #satellite #spatialanalysis #spatiotemporal #GIS #spatial #mapping #habitat #destruction #migration #corridors #monitoring #conservationscience #strategy #resource #cloudcover #alerts #foresthealth #protection #SAR #vegetation #ecosystems #rainforest #greatape #disease #foodsecurity #DRC #Tanzania #CongoBasin #nationalpark #ranger #parkranger #security #safety #alerts #wildlifetrafficking
@JaneGoodallInstitute @iceeye -
Decoupling Of Surface Water Storage From Precipitation In Global Drylands Due To Anthropogenic Activity
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https://doi.org/10.1038/s44221-024-00367-7 <-- shared paper
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“The availability of surface water in global drylands is essential for both human society and ecosystems. However, the long-term drivers of change in surface water storage, particularly those related to anthropogenic activities, remain unclear. Here [they] use[d] multi-mission remote sensing data to construct monthly time series of water storage changes from 1985 to 2020 for 105,400 lakes and reservoirs in global drylands. An increase of 2.20 km³ per year in surface water storage is found primarily due to the construction of new reservoirs. For lakes and old reservoirs (constructed before 1983), conversely, the trend in storage is minor when aggregated globally, but they dominate surface water storage trends in 91% of individual global dryland basins. Further analysis reveals that long-term storage changes in these water bodies are primarily linked to anthropogenic factors - including human-induced warming and water-management practices - rather than to precipitation changes, as previously thought. These findings reveal a decoupling of surface water storage from precipitation in global drylands, raising concerns about societal and ecosystem sustainability…”
#water #hydrology #hydrography #waterstorage #waterresources #surfacewater #global #drylands #precipitation #rainfall #watersecurity #ecosystems #habitat #publichealth #anthropogenic #GIS #spatial #mapping #remotesensing #earthobservation #spatiotemporal #spatialanalysis #monitoring #geostatistics #engineering #reservoirs #infrastructure #lakes #waterbodies #globalwarming #climatechange #sustainability #planning #baseline -
Multidecadal Reconstruction Of Terrestrial Water Storage Changes By Combining Pre-GRACE Satellite Observations And Climate Data
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https://doi.org/10.5194/essd-18-1747-2026 <-- shared paper
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https://doi.org/10.5281/zenodo.15827789 <-- reconstructed fields and corresponding uncertainty datasets
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https://doi.org/10.5281/zenodo.16643628 <-- corresponding time series datasets
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#GIS #spatial #mapping #remotesensing #earthobservation #GRACE #GRACEFO #water #hydrology #hydrography #waterresources #waterstorage #planning #monitoring #spatialanalysis #spatiotemporal #climatemodel #climatechange #extremeweather #regression #AI #machinelearning #rainfall #precipitation #remperatures #parameters #gravity #geodetic #satellite #orbitography #geomorphometry #DORIS #global #reconstruction #limitations #usecase -
Active Faults Greece - A Comprehensive Geomorphology-Based 1:25,000 Fault Database
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https://doi.org/10.1038/s41597-025-06283-z <-- shared paper
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https://activefaults.eagme.gr/en/ <-- shared Hellenic DataBase of Active Faults (HeDBAF) access
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#GIS #spatial #mapping #Greece #Europe #geology #fault #faulting #remotesensing #earthobservation #seismology #seismic #earthquake #paleoseismology #activefault #risk #hazard #naturaldisasters #DEM #elevation #ActiveFaultsGreece #AFG #database #faulttraces #engineeringeology #spatialanalysis #geomorphology #landsurface #planning #hazardassessment -
Remote Sensing And GIS-Supported Framework Of Pre-Monsoon Drought Assessment In Bangladesh (2000–2022) Using CHIRPS-Based SPI-3 And MODIS-Derived Vegetation And Temperature Indices
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https://doi.org/10.1007/s12665-026-12848-x <-- shared paper
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H/T MD. ABDULLAH AL MAMUNM | Studying PhD in Rural and Environmental Sciences
“১ বছর ২০ দিন লেগে গেল! প্রথম ৪ জন রিভিউয়ারের প্রায় ৫০+ কমেন্টের পর মনে হয়েছিল আর এগোব না। তবে আমার সুপারভাইজার বলেছিলেন, “রিজেকশনের চেয়ে কমেন্ট ফেস করা ভালো।”
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#GIS #spatial #mapping #remotesensing #Bangladesh #earthobservation #water #hydrology #premoonsoon #moonsoon #drought #CHIRPS #MODIS #SPI #vegetation #temperature #indices #parameters #SPI #NDVI #VCI #TCI #VHI #monitoring #droughts #agriculture #farming #crop #cultivation #yield #foodsecurity #weather #rainfall #precipitation #Pearsoncorrelation #geostatistics #irrigation #watersecurity #foodsecurity #policy #planning -
Detecting Land Use And Land Cover Changes And Quantifying Soil Erosion And Sediment Export Using GIS And Remote Sensing In The GERD Catchment, Ethiopia
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https://doi.org/10.1016/j.iswcr.2026.100657 <-- shared paper
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https://infonile.org/en/2023/05/battling-for-survival-along-the-warming-source-of-the-blue-nile/ <-- shared technical media article
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https://doi.org/10.1007/978-3-031-65241-7_4 <-- shared technical book chapter
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https://doi.org/10.3390/rs8121020 <-- shared 2016 paper
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#landuse #landcover #RUSLE #model #soil #erosion #sedimentation #GERD #catchment #NorthAfrica #Ethiopia #AbbayBasin #sustainability #landmanagement #agriculture #farmland #foodsecurity #water #hydrogology #reservior #watermanagement #watersecurity #waterresources #catchment #GrandEthiopianRenaissanceDam #BlueNile #hydrography #impoundment #GIS #spatial #mapping #remotesensing #earthobservation #thematic #spatialanalysis #spatiotemporal #landsat #elevation #DEM #CHIRPS #rainfall #precipitation #LULC #AI #forest #grazing #grassland #field #crops #cropland #waterbodies #urban #biophysical #risk #hazard #mitigation #soilquality -
Remote Sensing And Process Attribution Uncertainties In The Dharali Event [Himalayas, India]
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https://doi.org/10.1038/s44304-026-00211-w <-- shared paper review
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https://doi.org/10.1038/s44304-026-00191-x <-- shared paper that was reviewed
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https://doi.org/10.1016/j.nhres.2025.11.001 <-- related shared paper
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#Dharali #disaster #BhagirathiBasin #India #NorthernIndia #Himalayas #GIS #spatial #mapping #remotesensing #satellite #imagery #earthobservation #trigger #risk #hazard #damage #infrastructure #mountain #geomorphology #glacier #glacial #debrisflow #publicsafety #landuse #paraglacial #rainfall #precipitation #extremeweather #massmovement #landslide #spatialanalysis #spatiotemporal #Bhagirathi #River #water #hydrology #icepatchcollapse -
Scale Dependence In Remotely Sensed Biodiversity: Leveraging Continental-Scale Imaging Spectroscopy From The National Ecological Observatory Network [spatial analysis]
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https://doi.org/10.1002/rse2.70068 <-- shared paper
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https://doi.org/10.1038/s41559-022-01702-5 <-- shared paper
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#spectraldiversity #spectroscopy #spatialscale #US #NEON #NationalEcologicalObservatoryNetwork #species #ecology #humanimpacts #remotesensing #biodiversity #earthobservation #GIS #spatial #mapping #scale #scale #diversity #metrics #ecosystems #spectral #richness #scaledependency #principalcompoenent #divergence #spatialanalysis #raster #topography #climate #geomorphology #regression #geostatistics #vegetation #plant #area #region #largescale #continent #forest #tree -
A Review Of Evolving Remote Sensing And Automated Techniques In Rock Glacier Mapping
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https://doi.org/10.1016/j.earscirev.2026.105473 <-- shared paper
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#GIS #spatial #mapping #rockglacier #glaciers #permafrost #remotesensing #interferometry #earthobservation #spatialanalysis #machinelearning #AI #machinelearning #ML #deeplearning #CNN #metrics #inventory #earthobservation #GeoAI #geostatistics #InSAR #LiDAR #radar #satellite #review #literaturereview #geomorphology #geomorphometry #hydrology #geohazard #risk #hazard #engineeringgeology #biodiversity #permafrost #cryosphere #ice #snow #geology #assessment #survey #research
@Geospatial Research Institute Toi Hangarau | @University of Canterbury