#earthobservation — Public Fediverse posts

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=2026-09-04

Imagery: NASA GIBS #satellite #NASA #EarthObservation

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=2026-09-04

Imagery: NASA GIBS #satellite #NASA #EarthObservation

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=2026-09-04

Imagery: NASA GIBS #satellite #NASA #EarthObservation

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=2026-09-04

Imagery: NASA GIBS #satellite #NASA #EarthObservation

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=2026-09-04

Imagery: NASA GIBS #satellite #NASA #EarthObservation

Setúbal, Portugal, increases its resilience to floods | EU Space Support Office https://www.byteseu.com/2338239/ #EarthObservation #Portugal

https://www.europesays.com/dk/159668/ Tallinn, European Green Capital 2023 #EarthObservation #Estonia #ForestryAndBiodiversity #Tallinn

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

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

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

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

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

https://www.europesays.com/ie/?p=672272 Satellogic to provide Merlin maritime intelligence exclusively through SynMax platform #EarthObservation #Éire #IE #Ireland #maritime #Satellogic #Science #SN #Space #SynMax

To Predict Tree Death, Scientists Tapped Gamma Rays To Peer Underground
(Airborne radiation sensors could help forecast and prevent drought-driven tree mortality_
--
https://www.science.org/content/article/predict-tree-death-scientists-tapped-gamma-rays-peer-underground <-- shared technical article
--
https://doi.org/10.1029/2026GL122182 <-- shared paper
--
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...”
--
"... 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

On-Demand Global Landsat Evapotranspiration Product - Development, Evaluation, And Dissemination
--
https://doi.org/10.1016/j.rse.2026.115633 <-- shared paper
--
https://espa.cr.usgs.gov <-- shared (open data) USGS EROS Science Processing Architecture (ESPA) platform
--
https://etdata.org/ <-- OpenET SSEBop platform implementation (water management)
--
https://www.usgs.gov/landsat-missions/landsat-collection-2-provisional-actual-evapotranspiration-science-product <-- shared USGS Landsat Collection 2 Provisional Actual Evapotranspiration Science Product
--
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.)…”
--
“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

Mapping Flood Agents In The Northern Pantanal Wetland Using Multiple Spatio-Temporal GIS And Remote Sensing Techniques
--
https://doi.org/10.1007/s13157-026-02082-3 <-- shared paper
--
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…”
--
“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

Essential Earth Observation Variables For High-Level Multi-Scale Indicators And Policies
--
https://doi.org/10.1016/j.envsci.2021.12.024 <-- shared paper
--
https://earthobservations.org/about-us/news/remembering-paolo-mazzetti-a-legacy-of-science-kindness-and-collaboration <-- shared author memorial
--
H/T @Paolo Mazzetti | Senior Researcher at the Institute of Technologies and Environmental Intelligence (ITIAm) of CNR
“HIGHLIGHTS:
• Defining Essential Variables (EVs) to describe the global socio-ecological system.
• Setting the objectives of a new Group on Earth Observation (GEO) community activity on EVs.
• Defining criteria for selecting EVs.
• Demonstrating a fully functional workflow on land degradation.
• Presenting a new vision for the mainstreaming of EVs in science-policy interfaces..."
#Earthobservations #Essentialvariables #Indicators #Workflows #Sustainabledevelopmentgoals #Policy #earthobservation #essentialvariables #Indicators #Workflow #sustainabledevelopmentgoals #SDGs #policy #spatial #mapping #remotesensing #EVs #GroupOnEarthObservation #GEO #socioecological #sustainability #earthsystems #Drivers #Pressures #States #Impacts #Responses #sustainability #monitoring #observations #organisation #GlobalEarthObservationSystemofSystems #overview #review #literaturereview #environmental #Essentiality #Evolvability #Unambiguity #Feasibility #ecologicalfootprinting #viability

Essential Earth Observation Variables For High-Level Multi-Scale Indicators And Policies
--
https://doi.org/10.1016/j.envsci.2021.12.024 <-- shared paper
--
https://earthobservations.org/about-us/news/remembering-paolo-mazzetti-a-legacy-of-science-kindness-and-collaboration <-- shared author memorial
--
H/T @Paolo Mazzetti | Senior Researcher at the Institute of Technologies and Environmental Intelligence (ITIAm) of CNR
“HIGHLIGHTS:
• Defining Essential Variables (EVs) to describe the global socio-ecological system.
• Setting the objectives of a new Group on Earth Observation (GEO) community activity on EVs.
• Defining criteria for selecting EVs.
• Demonstrating a fully functional workflow on land degradation.
• Presenting a new vision for the mainstreaming of EVs in science-policy interfaces..."
#Earthobservations #Essentialvariables #Indicators #Workflows #Sustainabledevelopmentgoals #Policy #earthobservation #essentialvariables #Indicators #Workflow #sustainabledevelopmentgoals #SDGs #policy #spatial #mapping #remotesensing #EVs #GroupOnEarthObservation #GEO #socioecological #sustainability #earthsystems #Drivers #Pressures #States #Impacts #Responses #sustainability #monitoring #observations #organisation #GlobalEarthObservationSystemofSystems #overview #review #literaturereview #environmental #Essentiality #Evolvability #Unambiguity #Feasibility #ecologicalfootprinting #viability

Essential Earth Observation Variables For High-Level Multi-Scale Indicators And Policies
--
https://doi.org/10.1016/j.envsci.2021.12.024 <-- shared paper
--
https://earthobservations.org/about-us/news/remembering-paolo-mazzetti-a-legacy-of-science-kindness-and-collaboration <-- shared author memorial
--
H/T @Paolo Mazzetti | Senior Researcher at the Institute of Technologies and Environmental Intelligence (ITIAm) of CNR
“HIGHLIGHTS:
• Defining Essential Variables (EVs) to describe the global socio-ecological system.
• Setting the objectives of a new Group on Earth Observation (GEO) community activity on EVs.
• Defining criteria for selecting EVs.
• Demonstrating a fully functional workflow on land degradation.
• Presenting a new vision for the mainstreaming of EVs in science-policy interfaces..."
#Earthobservations #Essentialvariables #Indicators #Workflows #Sustainabledevelopmentgoals #Policy #earthobservation #essentialvariables #Indicators #Workflow #sustainabledevelopmentgoals #SDGs #policy #spatial #mapping #remotesensing #EVs #GroupOnEarthObservation #GEO #socioecological #sustainability #earthsystems #Drivers #Pressures #States #Impacts #Responses #sustainability #monitoring #observations #organisation #GlobalEarthObservationSystemofSystems #overview #review #literaturereview #environmental #Essentiality #Evolvability #Unambiguity #Feasibility #ecologicalfootprinting #viability

Essential Earth Observation Variables For High-Level Multi-Scale Indicators And Policies
--
https://doi.org/10.1016/j.envsci.2021.12.024 <-- shared paper
--
https://earthobservations.org/about-us/news/remembering-paolo-mazzetti-a-legacy-of-science-kindness-and-collaboration <-- shared author memorial
--
H/T @Paolo Mazzetti | Senior Researcher at the Institute of Technologies and Environmental Intelligence (ITIAm) of CNR
“HIGHLIGHTS:
• Defining Essential Variables (EVs) to describe the global socio-ecological system.
• Setting the objectives of a new Group on Earth Observation (GEO) community activity on EVs.
• Defining criteria for selecting EVs.
• Demonstrating a fully functional workflow on land degradation.
• Presenting a new vision for the mainstreaming of EVs in science-policy interfaces..."
#Earthobservations #Essentialvariables #Indicators #Workflows #Sustainabledevelopmentgoals #Policy #earthobservation #essentialvariables #Indicators #Workflow #sustainabledevelopmentgoals #SDGs #policy #spatial #mapping #remotesensing #EVs #GroupOnEarthObservation #GEO #socioecological #sustainability #earthsystems #Drivers #Pressures #States #Impacts #Responses #sustainability #monitoring #observations #organisation #GlobalEarthObservationSystemofSystems #overview #review #literaturereview #environmental #Essentiality #Evolvability #Unambiguity #Feasibility #ecologicalfootprinting #viability

Essential Earth Observation Variables For High-Level Multi-Scale Indicators And Policies
--
https://doi.org/10.1016/j.envsci.2021.12.024 <-- shared paper
--
https://earthobservations.org/about-us/news/remembering-paolo-mazzetti-a-legacy-of-science-kindness-and-collaboration <-- shared author memorial
--
H/T @Paolo Mazzetti | Senior Researcher at the Institute of Technologies and Environmental Intelligence (ITIAm) of CNR
“HIGHLIGHTS:
• Defining Essential Variables (EVs) to describe the global socio-ecological system.
• Setting the objectives of a new Group on Earth Observation (GEO) community activity on EVs.
• Defining criteria for selecting EVs.
• Demonstrating a fully functional workflow on land degradation.
• Presenting a new vision for the mainstreaming of EVs in science-policy interfaces..."
#Earthobservations #Essentialvariables #Indicators #Workflows #Sustainabledevelopmentgoals #Policy #earthobservation #essentialvariables #Indicators #Workflow #sustainabledevelopmentgoals #SDGs #policy #spatial #mapping #remotesensing #EVs #GroupOnEarthObservation #GEO #socioecological #sustainability #earthsystems #Drivers #Pressures #States #Impacts #Responses #sustainability #monitoring #observations #organisation #GlobalEarthObservationSystemofSystems #overview #review #literaturereview #environmental #Essentiality #Evolvability #Unambiguity #Feasibility #ecologicalfootprinting #viability

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
--
https://doi.org/10.5194/soil-12-113-2026 <-- shared paper
--
https://doi.org/10.1038/s41558-026-02603-2 <-- shared paper
--
https://www.theguardian.com/environment/2026/aug/20/tipping-points-heatwaves-wildfires-permafrost-climate-crisis <-- shared media article
--
https://www.cbc.ca/news/canada/north/permafrost-slumps-herschel-island-qikiqtaruk-yukon-9.7168780 <-- shared media article
--
[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
--
https://doi.org/10.3390/rs18142282 <-- shared paper
--
https://www.usgs.gov/publications/comparing-desis-hyperspectral-and-landsat-10-simulated-superspectral-data-crop-type <-- shared USGs publication page
--
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

Mapping Snow On Northern Winter Roads - A Dual-Frequency Polarimetric Radar Approach For Snow Characterization Over Land, Lake And Sea Ice
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https://doi.org/10.5194/tc-20-4367-2026 <-- shared paper
--
H/T @Monojit Saha | Geospatial Analysis | Remote Sensing | Satellite Altimetry | Cryosphere
“Winter roads are essential transportation links for many remote northern communities, but their safety and reliability depend strongly on snow conditions and ice growth. In this study [link above], [the authors] evaluated a fully polarimetric, dual-frequency Ku- and Ka-band radar approach for retrieving snow depth across landfast sea ice, lake ice, and tundra.
Using field measurements near Churchill, Manitoba, and Resolute Bay, Nunavut [Canada], [they] found that the approach produced snow-depth retrieval bias and error within 3 cm over landfast ice, with encouraging Ku-band performance over frozen ground as well. [They] also developed an interface-detection approach for lake ice that can retrieve both snow depth and ice thickness - a promising direction for characterizing conditions relevant to winter-road planning and safety…”
--
“Winter roads are lifelines for remote northern communities. Built over land, lakes, rivers, and sea ice, these travel routes are increasingly vulnerable to warming temperatures and variable precipitation. To ensure safety and adapt to these changes, operators require high-resolution monitoring of snow depth across these diverse surfaces, as natural snow accumulation dictates ice growth rates, route viability and road stability. This study extends our polarimetric radar method, previously demonstrated on pack ice, to landfast sea ice, tundra, and frozen lakes and assesses how well we can retrieve snow depth over these surfaces. Results indicate consistency with earlier sea ice analyses, maintaining a mean snow depth retrieval bias and error within 3 cm over the landfast ice. Promising performance is also found over frozen ground using Ku-band (mean biases less than 6 cm). To address the specific challenge of lake ice, which includes strong returns from the ice/water interface, we present a new interface-detection technique that simultaneously retrieves snow depth and ice thickness. While current validation focuses on undisturbed snow, this approach could provide a path forward for characterizing the cryospheric environment in a way that can directly support the optimization of winter roads…”
#Cryosphere #RemoteSensing #Snow #SeaIce #LakeIce #WinterRoads #characterisation #ArcticResearch #EarthObservation #PolarScience #maintainence #ploughing #winter #roads #transportation #northern #communities #mines #FirstNation #canada #remotesensing #polarimetric #radar #snowdepth #ice #landfastice #iceroad #tundra #Churchill #Manitoba #ResoluteBay #Nunavut #monitoring #planning #safety #trucking #freight

Impact Of Urbanization Driven Land Use And Land Cover Change On Ecological Environmental Quality In Rupandehi Nepal Assessed Using The Remote Sensing Ecological Index
--
https://doi.org/10.1007/s44288-026-00650-y <-- shared paper
--
https://kathmandupost.com/money/2026/02/18/rupandehi-s-continued-urban-sprawl-comes-at-a-cost-for-agriculture-in-the-periphery <-- shared media article
--
H/T@ Gaurav Parajulim
“[The authors] studied how the ecological quality of Nepal's Rupandehi District has changed over three decades (1993–2023), using satellite imagery and the Remote Sensing Ecological Index (RSEI) to track the health of the landscape year by year and to understand how urbanization-driven land use change has reshaped it.
What [they] found tells a nuanced story: as Butwal and Bhairahawa grew and built-up land expanded, ecological quality shifted in ways that a single number can't capture, some areas recovered, others declined, and the patterns rarely moved in a straight line…”
--
“Rapid urbanization and population growth are major drivers of land use and land cover (LULC) change and can substantially alter ecological environmental quality (EEQ). This study assessed the spatiotemporal dynamics of LULC and their effect on EEQ in Rupandehi District, Nepal, over a 30-year period (1993–2023). Four ecological indicators representing greenness, wetness, dryness, and heat were derived from Landsat imagery in Google Earth Engine (GEE), and LULC was classified using a Support Vector Machine (SVM). The Remote Sensing Ecological Index (RSEI) was then constructed from these indicators using Principal Component Analysis (PCA) in ArcGIS Pro, and its spatial structure was examined using global and local spatial autocorrelation. The mean RSEI followed a non-linear trajectory, rising from 0.59 in 1993 to 0.635 in 2004, declining to 0.55 in 2013, and recovering to 0.67 in 2023, indicating an overall improvement in EEQ with a temporary mid-period decline. Over the same period, built-up areas expanded substantially and agricultural land declined, whereas forest cover fluctuated but showed a slight net increase by 2023, and barren land decreased markedly. Higher EEQ was concentrated in the forested northern hills, while lower values occurred in the urban centers of Butwal and Bhairahawa, closely matching the spatial pattern of LULC change. The results indicate that ecological quality reflects the combined influence of all land cover classes rather than any single class. This study provides a transferable and reproducible workflow for long-term ecological assessment based on openly available Landsat data, with the analysis code shared in a public repository, offering practical guidance for sustainable land management and environmentally responsible urban development...”
#GIS #spatial #mapping #RemoteSensing #GIS #RSEI #EnvironmentalMonitoring #Nepal #Research #GoogleEarthEngine #ArcGIS #EcologicalQuality #spatialautocorrelation #ecology #environment #earthobservation #RemoteSensingEcologicalIndex #landscape #urbanisation #urban #development #landuse #change #spatialanalysis #spatiotemporal

The Latest Data Confirms - Forest Fires Are Getting Worse
--
https://www.wri.org/insights/global-trends-forest-fires <-- shared technical article
--
http://alturl.com/efp6m <-- shared (focused) #GlobalNatureWatch web map
--
https://science.nasa.gov/earth/explore/wildfires-and-climate-change/ <-- shared NASA technical article, ‘Wildfires and Climate Change’
--
https://doi.org/10.3389/frsen.2022.825190 <-- shared paper
--
https://doi.org/10.1073/pnas.2505418122 <-- shared paper
--
https://doi.org/10.1088/1748-9326/add606 <-- shared paper
--
https://globalnaturewatch.org/dashboards/global/ <-- shared Global Nature Watch dashboard
--
https://youtu.be/-0-pv1Bqm-U?si=IHcZJNiVphosbeVt <-- shared overview video
--
https://grist.org/wildfires/the-us-has-lost-a-quarter-of-its-forest-cover-to-fire-since-2001/ <-- shared technical article, ‘Fire is responsible for a quarter of US forest loss since 2021’
--
https://www.nytimes.com/2026/04/29/climate/wri-report-forest-loss.html <-- shared media article
--
H/T @ World Resources Institute
[‘topical’ - Europe, North America, indeed globally, more & more…]
“New data shows that forest fires are getting worse, burning more than twice as much tree cover today as they did 20 years ago, largely due to climate change…
The latest data [2nd link above] confirms [that] forest fires are becoming more widespread and destructive around the globe. Updated data from researchers [3rd link above] shows that between 2001 and 2025 forest fires now burn over twice as much tree cover each year as they did two decades ago, and more than three times as much in the tropics.
This increased fire activity has been starkly visible in recent years. Record-setting blazes are becoming the norm, with four of the five worst years for global forest fires occurring since 2021. As fires worsen - including in historically low-risk areas, like rainforests - they are becoming an increasingly prevalent driver of global forest loss…”
#GlobalForestWatch #GlobalNatureWatch #deforestation #fire #wildfire #forest #vegetation #climatechange #risk #hazard #loss #ecosystems #GIS #spatial #mapping #remotesensing #earthobservation #spatialanalysis #spatiotemporal #global #worldwide #forestfire #damage #destruction #fireactivity #forestLOSS
@WRI | @Global Nature Watch

Flood And Landslide Susceptibility Assessment And Multi Hazard Interaction Mapping Using Machine Learning And GIS For Sustainable Settlement Planning In Nepal
--
https://doi.org/10.1007/s44288-026-00670-8 <-- shared paper
--
H/T @Narayan Thapa | Earth Data Modeling
“Nepal lies within an active seismic zone and is influenced by most dynamic climatic systems in the world. It faces compounding floods and landslide threats. Impacts are worst where multi-hazard interactions create spatially linked corridors. Despite frequent co-occurrence, national-scale assessments remain limited. This study presents machine learning and GIS-based approach to map nationwide susceptibility to floods, landslides, and identify their potential interaction zones, and delineate critical multi-hazard flow zones through spatial adjacency analysis. Using Google Earth Engine, the Random Forest model integrates topographic, climatic, environmental, and hydrological datasets to overcome subjective expert-driven methods. The model achieved strong predictive accuracy (AUC: 0.84 for floods, 0.85 for landslides). The results showed 19% of Nepal’s lowlands are medium to very highly susceptible to inundation, threatening approximately 900,000 people and over 3.4 million buildings; whilst in the hilly terrains, 40% is susceptible to slope-failure endangering 200,000 people and about 0.6 million buildings. K-means clustering followed by spatial adjacency analysis identified four spatial zonation: 81% of national area as low-hazard zone, 9% as flood-only zone, 5% as landslide-only zone, and 5% as interaction zones. Critical multi-hazard flow zone covering 7,588 km² represents spatially connected corridors linking interaction zones to downstream flood-prone populated areas, affecting 88 km² built-up land and 1,722 km² cropland. These zones represent susceptibility-based spatial connectivity rather than physically simulated cascading processes. These findings support recommendations for risk-informed land-use planning, resilient infrastructure development and climate adaptation aligned to sustainable development and investment risk screening…”
#GIS #spatial #mapping #GoogleEarthEngine #MachineLearning #RemoteSensing #GeospatialAI #DisasterRiskReduction #MultiHazard #ClimateAdaptation #climatechange #extremeweather #LandUsePlanning #SustainableDevelopment #InfrastructurePlanning #RiskAssessment #NaturalHazards #Nepal #EarthObservation #HinduKushHimalaya #HKH #HinduKush #Himalayas #risk #hazard #assessment #national #regional #spatialanalysis #spatiotemporal #massmovement #landslide #assessment #mitigation #water #hydrology #flood #flooding #sustainability

Enhancements To The USGS Landsat Level 2 Surface Temperature And Emissivity Product For Collection3 Reprocessing
--
https://doi.org/10.1016/j.rse.2026.115563 <-- shared paper
--
H/T @terry Sohl | USGS EROS Science Branch Chief
“HIGHLIGHTS:
• C3 advances Landsat TIR atmospheric correction, emissivity correction, and uncertainty estimates.
• C3 enables global surface temperature products, including polar regions.
• C3 retrievals improve accuracy and consistency across validation sites.
• Split window and single channel methods diverge at extreme temperature conditions.
• C3 and Landsat 10 support multi-decadal climate monitoring.
ABSTRACT: The Landsat program provides the longest continuous global record of thermal infrared observations of the Earth's surface, underpinning critical applications in climate monitoring, water resources, ecosystem dynamics, urban heat analysis, and natural hazard assessment. The release of a global inventory of Landsat Collection 2 Level 2 surface temperature products by the U.S. Geological Survey (USGS) marked a major milestone in operational provision of Landsat thermal infrared analysis-ready data. Ongoing validations and community uses of Collection 2 have identified opportunities to further improve accuracy, uncertainty characterization, and emissivity correction across diverse atmospheric and surface conditions. In preparation for the planned Landsat Collection 3 reprocessing of the Landsat data record in the late 2020s, the USGS is implementing a coordinated set of enhancements to the Level 2 surface temperature products. These include revised emissivity estimation that leverages external datasets, improved atmospheric characterization and uncertainty propagation, expanded dynamic range for high temperature targets, consideration of split window atmospheric correction algorithm for Landsat 8 and 9, and decoupling of thermal infrared processing from visible to shortwave infrared constraints to enable surface temperature retrievals under low or no solar illumination conditions. These changes are designed to improve product quality and consistency across the Landsat record. Beyond near-term performance gains, the Collection 3 design establishes a scalable processing architecture to accommodate the expanded spectral and radiometric measurement capabilities of the forthcoming Landsat 10 mission. By preserving continuity across the Landsat 4–9 record while enabling future algorithm evolution, Landsat Collection 3 will provide a foundation for long-term, multi-decadal Earth system thermal infrared observations…”
#GIS #spatial #mapping #satellite #remotesening #earthobservation #Landsat #thermalinfrared #TIR #surface #temperature #emissivity #thermal #infrared #analysisreadydata #Collection2 #Collection3 #opendata #atmosphericcorrection #global #climate #monitoring #waterresources #ecosystems #dynamics #urbanheat #spatialanalysis #spatiotemporal #naturalhazards #updates #EROS #USGS
@USGS EROS | @USGS

Coupled Hydrological And Public Health Risks From Urban Flooding - Integrated Remote Sensing, Machine Learning, And Hydrodynamic–Ecological Modelling
--
https://doi.org/10.1016/j.jhydrol.2026.135999 <-- shared paper
--
https://youtu.be/VHzYLvSYR7k?si=5oGGPeH6T0dFusfY <-- recent overview video created about the research
--
https://doi.org/10.1016/j.wroa.2025.100396 <-- share (earlier) paper
--
H/T @RAHUL DEOPA | Research Scholar (IIT Roorkee)
“… [U]rban floods are not merely hydraulic events; they also transport sewage, pathogens, and other contaminants across streets and communities, leading to significant public health risks…
How do we quantify microbial contamination in near real time during a flood event, when emergency conditions make field sampling unsafe, sparse, or even impossible?...
[The authors] explored whether Earth observation data, combined with machine learning, could bridge this critical monitoring gap. By combining Landsat-derived water surface temperature, machine learning, a coupled MIKE+ Flood–ECO Lab hydrodynamic–ecological model, and Quantitative Microbial Risk Assessment (QMRA), [they] estimated microbial concentrations (𝘌. 𝘤𝘰𝘭𝘪), simulated their fate and transport during floods, and quantified the associated human health risks.
The takeaway: predicting flood risk isn't just about where the water goes; it's about what it's carrying and who it puts in harm's way. Earth observation and machine learning can help close that gap when it matters most, during the emergency, not weeks after…”
#publichealth #risk #hazard #watersecurity #Floodrisk #Humanhealthrisk #Urbanflooding #Hydrodynamicmodelling #waterquality #model #modeling #SupportVectorRegression #flood #flooding #urban #city #sewage #pathogens #contaminant #disease #streets #community #quantification #remotesensing #GIS #spatial #mapping #earthobservation #spatialanalysis #water #hydrology #climatechange #extremeweather #spatiotemporal #AI #machineleraning #fateandtransport #hydrodynamic #microbial #rainfall #drainage #streamflow #topography #hydrogeomorphology #Delhi #India #floodplain

EU delays release of Copernicus imagery over Gulf of Oman

https://fed.brid.gy/r/https://spacenews.com/eu-delays-release-of-copernicus-imagery-over-gulf-of-oman/

Refined Modeling of Arctic Circumpolar Building Stock Increases Estimated Mid-Century Permafrost Degradation Damages
--
https://doi.org/10.1029/2026EF008578 <-- shared paper
--
https://www.thearcticinstitute.org/climate-change-geopolitics-monitoring-thawing-permafrost/ | https://www.thearcticinstitute.org/dwindling-arctic-sea-ice-impacts-permafrost-health/ <-- shared technical articles
--
https://www.theguardian.com/cities/2016/oct/14/thawing-permafrost-destroying-arctic-cities-norilsk-russia <-- shared media article
--
https://news.grida.no/new-map-shows-extent-of-permafrost-in-northern-hemisphere <-- shared technical article
--
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
--
https://doi.org/10.1038/s41598-026-52915-8 <-- shared paper
--
https://doi.org/10.1007/s11600-022-00943-z <-- shared paper
--
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...”
--
“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

A Scale-Invariance-Based Algorithm Application For Land Surface Temperature Downscaling In Denmark
--
https://doi.org/10.3390/rs18132263 <-- shared paper
--
https://zenodo.org/records/20863040 <-- shared open data for downscaled LST dataset for Copenhagen
--
H/T @CLIM4cities
#urbanclimate #downscaling #landsurfacetemperature #LST #AI #machinelearning #scaleinvariance #residualcorrection #Sentinel #Landsat #satellite #remotesensing #earthobservation #CLIM4cities #UrbanClimate #ClimateServices #MachineLearning #ClimateAdaptation #heatwave #temperature #ontheground #Copenhagen #Denmark #impervioussurface #asphalt #roof #concrete #albedo #heatabsorption #mitigation #urban #urbancentre #treecover #vegetation #urbanheatisland #planning #design #hotspots #monitoring #spatialanalysis #spatiotemporal #model #modeling #usecase #operational #climatechange #extremeweather #evidencebased #adapation #sustainability #urbanplanning #climateresilience #EssentialClimateVariable #ECV
@+ATLANTIC | @Danish Meteorological Institute | @ESA Φ-lab Collaborative Innovation Network | @CLIM4cities

Geospatial Data As Bioethical Evidence
--
https://doi.org/10.4401/jgsg-111 <-- shared paper
--
https://theconversation.com/gaza-we-analysed-a-year-of-satellite-images-to-map-the-scale-of-agricultural-destruction-248796 <-- shared technical article
--
https://www.scientificamerican.com/article/inside-the-satellite-tech-revealing-gazas-destruction/ <-- shared 2023 technical article
--
“Satellite imagery now documents systematic patterns of infrastructure destruction at spatial resolutions and temporal cadences that were unavailable during the atrocities of the twentieth century. Whether and how such data may enter bioethical deliberation, however, remains under-theorized. Quantitative remote sensing produces damage percentages, not normative claims, and bridging the two without committing an is-ought fallacy requires an explicit epistemological procedure. The contribution developed here is normative and epistemological rather than empirical. A six-component admissibility framework integrates, for the first time, geospatial evidence, population-level bioethical principlism, and the coherentist verification epistemology of political fact-checking into a single reproducible procedure for the bioethical use of satellite imagery. The first component specifies evidence admissibility criteria tailored to bioethical rather than strictly legal use. The second requires coherentist triangulation across methodologically independent remote sensing studies. The third operates as a bioethical relevance filter mapping infrastructure categories onto population-level social determinants of health. The fourth operationalizes principlism by translating health justice, accountability, solidarity, and sustainability into measurable geospatial observables. The fifth establishes ethical representation safeguards against voyeuristic or dehumanizing uses of destruction imagery. The sixth demands explicit epistemic humility regarding uncertainty, data missingness, and attribution limits. The Gaza conflict provides the case in point. Two independently produced geospatial studies, one based on SAR coherent change detection and one on very-high-resolution optical analysis, converge on extensive damage to civilian healthcare, water, sanitation, and educational infrastructure, and thereby satisfy the coherentist triangulation requirement of the framework. The resulting inference licenses bioethical claims of systematic survival infrastructure degradation while preserving transparent boundaries between what satellite evidence can and cannot establish about genocidal intent…”
#geoethics #GIS #spatial #mapping #remotesensing #earthobservation #imagery #satellite #opendata #conflict #war #military #destruction #infrastructure #spatiotemporal #bioethical #evidence #damagepercentages #spatialanalysis #change #quantitative #metrics #normative #epistemological #admissibility #framework #factchecking #controlled #publicsafety #publichealth #healthjustice #geospatialobservables #observation #uncertainty #controls #boundaries #changedetection #coherentisttriangulation #example #gaza

[G]lobal Decline In Endorheic Basin Water Storages
--
https://doi.org/10.1038/s41561-018-0265-7 <-- shared paper
--
https://en.wikipedia.org/wiki/Endorheic_basin <-- shared Wikipedia page
--
“Endorheic (hydrologically landlocked) basins spatially concur with arid/semi-arid climates. Given limited precipitation but high potential evaporation, their water storage is vulnerable to subtle flux perturbations, which are exacerbated by global warming and human activities. Increasing regional evidence suggests a probably recent net decline in endorheic water storage, but this remains unquantified at a global scale. By integrating satellite observations and hydrological modelling, [they] reveal[ed] that during 2002–2016 the global endorheic system experienced a widespread water loss of about 106.3 Gt/yr, attributed to comparable losses in surface water, soil moisture and groundwater. This decadal decline, disparate from water storage fluctuations in exorheic basins, appears less sensitive to El Niño–Southern Oscillation-driven climate variability, which implies a possible response to longer-term climate conditions and human water management. In the mass-conserved hydrosphere, such an endorheic water loss not only exacerbates local water stress, but also imposes excess water on exorheic basins, leading to a potential sea level rise that matches the contribution of nearly half of the land glacier retreat (excluding Greenland and Antarctica). Given these dual ramifications, [they] suggest the necessity for long-term monitoring of water storage variation in the global endorheic system and the inclusion of its net contribution to future sea level budgeting…”
#water #hydrology #hydrography #global #waterresources #waterstorage #Endorheic #Basin #watersecurity #arid #semiarid #rainfall #precipitation #spatialanalysis #spatiotemporal #globalwarming #climatechange #humanimpacts #anthropogenic #regional #remotesensing #GIS #spatial #mapping #earthobservation #surfacewater #groundwater #soilmoisture #exorheic #watermanagement #hydrosphere #waterstress #SLR #sealevelrise #monitoring #waterbudgets

#Mexico City is sinking #earthobservation #subsidence #radar #SAR #NISAR https://www.nasa.gov/missions/nisar/us-indian-space-mission-maps-extreme-subsidence-in-mexico-city/

Sea Levels Rising Dramatically In Some Areas Due To Land Subsidence [global]
--
https://phys.org/news/2026-05-sea-areas-due-subsidence.html <-- shared technical article
--
https://doi.org/10.1038/s41467-026-72293-z <-- shared paper
--
[#VLM = vertical land motion; #ASL = absolute sea-level; #RSL = relative sea-level; #GIA = (global) Glacial Isostatic Adjustment; #inSAR = Interferometric Synthetic Aperture Radar; #GNSS = Global Navigation Satellite System (~GPS); #OE24 = paper, https://doi.org/10.1038/s41561-023-01357-2, interpolated VLM reconstruction based on the joint analysis of GNSS, tide gauges (TGs), and satellite altimetry]
#GIS #spatial #mapping #remotesensing #earthobservation #sealevel #verticallandmotion #absolutesealevel #relativesealevel #GlacialIsostaticAdjustment #geomorphometry #SLR #sealevelrise #coast #coastal #flood #flooding #subsidence #landmass #landsubsidence #global #globalsealevelrise #climatechange #city #urban #farmlands #population #demographics #cities #planning #community #elevation #monitoring #spatialanalysis #spatiotemporal #altimetry

Why Earth observation data is getting stuck in orbit

https://fed.brid.gy/r/https://spacenews.com/why-earth-observation-data-is-getting-stuck-in-orbit/

I was really honored to give a talk (remotely) at NASA Goddard today! Thanks for the invitation, and I hope we'll stay in touch!

#RemoteSensing #NighttimeLights #NightLightRemoteSensing #VIIRS_DNB #SDGSat1 #ESA #EarthExplorer #EarthObservation #LightPollution

I was really honored to give a talk (remotely) at NASA Goddard today! Thanks for the invitation, and I hope we'll stay in touch!

#RemoteSensing #NighttimeLights #NightLightRemoteSensing #VIIRS_DNB #SDGSat1 #ESA #EarthExplorer #EarthObservation #LightPollution

I was really honored to give a talk (remotely) at NASA Goddard today! Thanks for the invitation, and I hope we'll stay in touch!

#RemoteSensing #NighttimeLights #NightLightRemoteSensing #VIIRS_DNB #SDGSat1 #ESA #EarthExplorer #EarthObservation #LightPollution

I was really honored to give a talk (remotely) at NASA Goddard today! Thanks for the invitation, and I hope we'll stay in touch!

#RemoteSensing #NighttimeLights #NightLightRemoteSensing #VIIRS_DNB #SDGSat1 #ESA #EarthExplorer #EarthObservation #LightPollution

I was really honored to give a talk (remotely) at NASA Goddard today! Thanks for the invitation, and I hope we'll stay in touch!

#RemoteSensing #NighttimeLights #NightLightRemoteSensing #VIIRS_DNB #SDGSat1 #ESA #EarthExplorer #EarthObservation #LightPollution

Characterizing Wildfire Behavior With ECOSTRESS Land Surface Temperature Across Four California Case Studies
--
https://doi.org/10.1016/j.ecoinf.2026.103777 <-- shared paper
--
#GIS #spatial #mapping #ECOSTRESS #remotesensing #earthobservation #wildfire #increased #frequency #intensity #forestfire #monitoring #hotspot #detection #Rateofspread #HotspotBurn #severity #spatialanalysis #spatiotemporal #VIIRS #fireradiativepower #FRP #landsurface #temperature #California #casestudy #burn #severity #dNBR #landsat #satellite #WesternUSA #extremeweather #climatechange #water #hydrology #hydrometeorology #drought #humanactivity #humanimpacts #ECOsystemSpaceborneThermalRadiometerExperimentonSpaceStation #ISS #spacestation #LST #rateofspread
@nasa

Satellite Imagery Reveals Increasing Volatility In Human Night-Time Activity
--
https://doi.org/10.1038/s41586-026-10260-w <-- shared paper
--
https://ee-downloading.projects.earthengine.app/view/alan-change <-- shared associated web map
--
https://www.theguardian.com/us-news/2026/apr/18/earth-brightness-study <-- shared technical media article
--
https://svs.gsfc.nasa.gov/5634/ <-- shared NASA technical article with videos showing global change over time
--
#GIS #spatial #mapping #global #spatialanalysis #spationtemporal #NightTime #lights #ALAN #artificiallightatnight #humanimpacts #remotesensing #earthobservation #change #lightradiance #dynamic #NASAEarthObservatory #NASA #monitoring #abrupt #gradual #volatility #urban #policy #planning #ecology
@nasa | @nasa Earth Observatory

Active Faults Greece - A Comprehensive Geomorphology-Based 1:25,000 Fault Database
--
https://doi.org/10.1038/s41597-025-06283-z <-- shared paper
--
https://activefaults.eagme.gr/en/ <-- shared Hellenic DataBase of Active Faults (HeDBAF) access
--
#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

Detecting Land Use And Land Cover Changes And Quantifying Soil Erosion And Sediment Export Using GIS And Remote Sensing In The GERD Catchment, Ethiopia
--
https://doi.org/10.1016/j.iswcr.2026.100657 <-- shared paper
--
https://infonile.org/en/2023/05/battling-for-survival-along-the-warming-source-of-the-blue-nile/ <-- shared technical media article
--
https://doi.org/10.1007/978-3-031-65241-7_4 <-- shared technical book chapter
--
https://doi.org/10.3390/rs8121020 <-- shared 2016 paper
--
#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]
--
https://doi.org/10.1038/s44304-026-00211-w <-- shared paper review
--
https://doi.org/10.1038/s44304-026-00191-x <-- shared paper that was reviewed
--
https://doi.org/10.1016/j.nhres.2025.11.001 <-- related shared paper
--
#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