#landsat — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #landsat, aggregated by home.social.
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Michigan’s Les Cheneaux Islands – NASA Science
With one glance at a particular 12-mile stretch of Lake Huron’s shoreline, it’s clear there’s a pattern. Small…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Space #EarthObservatory #HumanDimensions #Landsat-9 #Science #topography
https://www.newsbeep.com/us/825093/ -
Michigan’s Les Cheneaux Islands – NASA Science
With one glance at a particular 12-mile stretch of Lake Huron’s shoreline, it’s clear there’s a pattern. Small…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Space #EarthObservatory #HumanDimensions #Landsat-9 #Science #topography
https://www.newsbeep.com/us/825093/ -
Die Erde von oben: Das @DLR macht mit Hilfe der TUM und des LRZ sein Archiv mit #Landsat -Daten zugänglich – ein #Archiv für #umweltdaten Diese liefern unter anderem Bilder der Oberflächentemperaturen, etwa von #munchen wie unten. Bislang stellt die @nasa Landsat-Daten zur Verfügung. Das macht die Wissenschaft abhängig von politischen Entscheidungen.
Zudem werden Infrastrukturen zur Speicherung dieser Daten aufgebaut: https://www.ls.tum.de/ls/presse/aktuelles/nachricht-detail/article/tum-und-dlr-sichern-landsat-archiv/ -
Die Erde von oben: Das @DLR macht mit Hilfe der TUM und des LRZ sein Archiv mit #Landsat -Daten zugänglich – ein #Archiv für #umweltdaten Diese liefern unter anderem Bilder der Oberflächentemperaturen, etwa von #munchen wie unten. Bislang stellt die @nasa Landsat-Daten zur Verfügung. Das macht die Wissenschaft abhängig von politischen Entscheidungen.
Zudem werden Infrastrukturen zur Speicherung dieser Daten aufgebaut: https://www.ls.tum.de/ls/presse/aktuelles/nachricht-detail/article/tum-und-dlr-sichern-landsat-archiv/ -
Die Erde von oben: Das @DLR macht mit Hilfe der TUM und des LRZ sein Archiv mit #Landsat -Daten zugänglich – ein #Archiv für #umweltdaten Diese liefern unter anderem Bilder der Oberflächentemperaturen, etwa von #munchen wie unten. Bislang stellt die @nasa Landsat-Daten zur Verfügung. Das macht die Wissenschaft abhängig von politischen Entscheidungen.
Zudem werden Infrastrukturen zur Speicherung dieser Daten aufgebaut: https://www.ls.tum.de/ls/presse/aktuelles/nachricht-detail/article/tum-und-dlr-sichern-landsat-archiv/ -
Die Erde von oben: Das @DLR macht mit Hilfe der TUM und des LRZ sein Archiv mit #Landsat -Daten zugänglich – ein #Archiv für #umweltdaten Diese liefern unter anderem Bilder der Oberflächentemperaturen, etwa von #munchen wie unten. Bislang stellt die @nasa Landsat-Daten zur Verfügung. Das macht die Wissenschaft abhängig von politischen Entscheidungen.
Zudem werden Infrastrukturen zur Speicherung dieser Daten aufgebaut: https://www.ls.tum.de/ls/presse/aktuelles/nachricht-detail/article/tum-und-dlr-sichern-landsat-archiv/ -
Die Erde von oben: Das @DLR macht mit Hilfe der TUM und des LRZ sein Archiv mit #Landsat -Daten zugänglich – ein #Archiv für #umweltdaten Diese liefern unter anderem Bilder der Oberflächentemperaturen, etwa von #munchen wie unten. Bislang stellt die @nasa Landsat-Daten zur Verfügung. Das macht die Wissenschaft abhängig von politischen Entscheidungen.
Zudem werden Infrastrukturen zur Speicherung dieser Daten aufgebaut: https://www.ls.tum.de/ls/presse/aktuelles/nachricht-detail/article/tum-und-dlr-sichern-landsat-archiv/ -
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
--
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 -
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 -
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 -
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 -
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 -
🚀 Thinking about building Landsat‑10 in your garage?
Better hurry — NASA’s RFP is out and the clock’s ticking. Early delivery even gets you a bonus.
Details: https://www.usgs.gov/landsat-missions/news/nasa-releases-landsat-10-spacecraft-request-proposal
#EarthObservation #Landsat -
🚀 Thinking about building Landsat‑10 in your garage?
Better hurry — NASA’s RFP is out and the clock’s ticking. Early delivery even gets you a bonus.
Details: https://www.usgs.gov/landsat-missions/news/nasa-releases-landsat-10-spacecraft-request-proposal
#EarthObservation #Landsat -
This #SOTMUS2026 session demonstrates a #Python program used to identify #urbanheatislands by utilizing #LANDSAT thermal band imagery and #OSM ways exported through #Overpass. The program calculates the land surface temperature exposure for each way.
🦩
🔗 https://youtu.be/En2dNw2K3SQ?si=MoryVd0_7pRw_yVn
🦩
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This #SOTMUS2026 session demonstrates a #Python program used to identify #urbanheatislands by utilizing #LANDSAT thermal band imagery and #OSM ways exported through #Overpass. The program calculates the land surface temperature exposure for each way.
🦩
🔗 https://youtu.be/En2dNw2K3SQ?si=MoryVd0_7pRw_yVn
🦩
-
This #SOTMUS2026 session demonstrates a #Python program used to identify #urbanheatislands by utilizing #LANDSAT thermal band imagery and #OSM ways exported through #Overpass. The program calculates the land surface temperature exposure for each way.
🦩
🔗 https://youtu.be/En2dNw2K3SQ?si=MoryVd0_7pRw_yVn
🦩
-
This #SOTMUS2026 session demonstrates a #Python program used to identify #urbanheatislands by utilizing #LANDSAT thermal band imagery and #OSM ways exported through #Overpass. The program calculates the land surface temperature exposure for each way.
🦩
🔗 https://youtu.be/En2dNw2K3SQ?si=MoryVd0_7pRw_yVn
🦩
-
This #SOTMUS2026 session demonstrates a #Python program used to identify #urbanheatislands by utilizing #LANDSAT thermal band imagery and #OSM ways exported through #Overpass. The program calculates the land surface temperature exposure for each way.
🦩
🔗 https://youtu.be/En2dNw2K3SQ?si=MoryVd0_7pRw_yVn
🦩
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Vine by Vine - Conserving Water with Landsat
#Agriculture #Biosphere #Landsat
⏩ 1 new picture and 1 new video from NASA (SVS) https://commons.wikimedia.org/w/index.php?search=incategory%3A%22NASA+SVS+files+uploaded+by+OptimusPrimeBot%22&sort=create_timestamp_desc&ns6=1
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Vine by Vine - Conserving Water with Landsat
#Agriculture #Biosphere #Landsat
⏩ 1 new picture and 1 new video from NASA (SVS) https://commons.wikimedia.org/w/index.php?search=incategory%3A%22NASA+SVS+files+uploaded+by+OptimusPrimeBot%22&sort=create_timestamp_desc&ns6=1
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Vine by Vine - Conserving Water with Landsat
#Agriculture #Biosphere #Landsat
⏩ 1 new picture and 1 new video from NASA (SVS) https://commons.wikimedia.org/w/index.php?search=incategory%3A%22NASA+SVS+files+uploaded+by+OptimusPrimeBot%22&sort=create_timestamp_desc&ns6=1
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Vine by Vine - Conserving Water with Landsat
#Agriculture #Biosphere #Landsat
⏩ 1 new picture and 1 new video from NASA (SVS) https://commons.wikimedia.org/w/index.php?search=incategory%3A%22NASA+SVS+files+uploaded+by+OptimusPrimeBot%22&sort=create_timestamp_desc&ns6=1
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#ESA:
"
Europas Wälder verlieren seit 2018 deutlich mehr Biomasse
"
"Europas Wälder verlieren mehr Biomasse als bisher angenommen. Faktoren wie Dürre und Schädlingsbefall verursachen zunehmend große Verluste in einigen der ältesten Wälder Europas. Seit 2018 hat sich diese Situation deutlich verschlechtert."5.8.2026
#Abholzung #Biomasse #Dürre #EO #Europa #Erdbeobachtung #Klimawandel #Landsat #Raumfahrt #Satelliten #SpaceFlight #Wald
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#ESA:
"
Europas Wälder verlieren seit 2018 deutlich mehr Biomasse
"
"Europas Wälder verlieren mehr Biomasse als bisher angenommen. Faktoren wie Dürre und Schädlingsbefall verursachen zunehmend große Verluste in einigen der ältesten Wälder Europas. Seit 2018 hat sich diese Situation deutlich verschlechtert."5.8.2026
#Abholzung #Biomasse #Dürre #EO #Europa #Erdbeobachtung #Klimawandel #Landsat #Raumfahrt #Satelliten #SpaceFlight #Wald
-
#ESA:
"
Europas Wälder verlieren seit 2018 deutlich mehr Biomasse
"
"Europas Wälder verlieren mehr Biomasse als bisher angenommen. Faktoren wie Dürre und Schädlingsbefall verursachen zunehmend große Verluste in einigen der ältesten Wälder Europas. Seit 2018 hat sich diese Situation deutlich verschlechtert."5.8.2026
#Abholzung #Biomasse #Dürre #EO #Europa #Erdbeobachtung #Klimawandel #Landsat #Raumfahrt #Satelliten #SpaceFlight #Wald
-
#ESA:
"
Europas Wälder verlieren seit 2018 deutlich mehr Biomasse
"
"Europas Wälder verlieren mehr Biomasse als bisher angenommen. Faktoren wie Dürre und Schädlingsbefall verursachen zunehmend große Verluste in einigen der ältesten Wälder Europas. Seit 2018 hat sich diese Situation deutlich verschlechtert."5.8.2026
#Abholzung #Biomasse #Dürre #EO #Europa #Erdbeobachtung #Klimawandel #Landsat #Raumfahrt #Satelliten #SpaceFlight #Wald
-
#ESA:
"
Europas Wälder verlieren seit 2018 deutlich mehr Biomasse
"
"Europas Wälder verlieren mehr Biomasse als bisher angenommen. Faktoren wie Dürre und Schädlingsbefall verursachen zunehmend große Verluste in einigen der ältesten Wälder Europas. Seit 2018 hat sich diese Situation deutlich verschlechtert."5.8.2026
#Abholzung #Biomasse #Dürre #EO #Europa #Erdbeobachtung #Klimawandel #Landsat #Raumfahrt #Satelliten #SpaceFlight #Wald
-
How the Tide Turns at the Mouth of the Elbe
One of the major rivers of Europe, the Elbe flows more than 1,000 kilometers (600 miles) across the…
#NewsBeep #News #Space #AU #Australia #EarthObservatory #HumanDimensions #Landsat-9 #oceans #Science #topography
https://www.newsbeep.com/au/845291/ -
How the Tide Turns at the Mouth of the Elbe
One of the major rivers of Europe, the Elbe flows more than 1,000 kilometers (600 miles) across the…
#NewsBeep #News #Space #AU #Australia #EarthObservatory #HumanDimensions #Landsat-9 #oceans #Science #topography
https://www.newsbeep.com/au/845291/ -
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 -
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 -
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 -
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 -
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 -
In Pursuit of Precision: Remembering John Barker
By Laura E.P. Rocchio John L. Barker, a seminal force behind Landsat’s science-grade data, died on Monday, July 6, 2026. He was…
#NewsBeep #News #Space #Landsat #Science #UK #UnitedKingdom
https://www.newsbeep.com/uk/723725/ -
Does urban greenery yield microclimatic cooling? Spatial analysis of Calgary (Summer 2025) shows a non-linear NDVI vs LST response.
🛠 Stack: Google Earth Engine (Landsat 8/9, Sentinel-2) + R (terra, tidyverse).
📊 Key Findings:
🔹 Cooling Deficit (NDVI < 0.34): LST stays trapped at 35–36°C. Heat stress overrides evapotranspiration; saplings & isolated lawns fail to cool.
🔹 Tipping Point (NDVI > 0.34): Cooling begins above 0.34. Dense canopy (NDVI > 0.70) suppresses LST below 28–30°C (6–8°C delta).💡 Takeaway: Urban forestry can't just count saplings. Without threshold canopy density, isolated greenery is decoration, not climate infrastructure.
🔗 Link to the research:
https://www.datastory.org.ua/calgarys-summer-heat-a-2025-satellite-perspective/#RemoteSensing #GIS #RStats #rspatial #terra #tidyverse #GoogleEarthEngine #UrbanForestry #Calgary #YYC #OpenData #GreennessOfCalgary #FOSSGIS #Landsat #Sentinel2
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Does urban greenery yield microclimatic cooling? Spatial analysis of Calgary (Summer 2025) shows a non-linear NDVI vs LST response.
🛠 Stack: Google Earth Engine (Landsat 8/9, Sentinel-2) + R (terra, tidyverse).
📊 Key Findings:
🔹 Cooling Deficit (NDVI < 0.34): LST stays trapped at 35–36°C. Heat stress overrides evapotranspiration; saplings & isolated lawns fail to cool.
🔹 Tipping Point (NDVI > 0.34): Cooling begins above 0.34. Dense canopy (NDVI > 0.70) suppresses LST below 28–30°C (6–8°C delta).💡 Takeaway: Urban forestry can't just count saplings. Without threshold canopy density, isolated greenery is decoration, not climate infrastructure.
🔗 Link to the research:
https://www.datastory.org.ua/calgarys-summer-heat-a-2025-satellite-perspective/#RemoteSensing #GIS #RStats #rspatial #terra #tidyverse #GoogleEarthEngine #UrbanForestry #Calgary #YYC #OpenData #GreennessOfCalgary #FOSSGIS #Landsat #Sentinel2
-
Does urban greenery yield microclimatic cooling? Spatial analysis of Calgary (Summer 2025) shows a non-linear NDVI vs LST response.
🛠 Stack: Google Earth Engine (Landsat 8/9, Sentinel-2) + R (terra, tidyverse).
📊 Key Findings:
🔹 Cooling Deficit (NDVI < 0.34): LST stays trapped at 35–36°C. Heat stress overrides evapotranspiration; saplings & isolated lawns fail to cool.
🔹 Tipping Point (NDVI > 0.34): Cooling begins above 0.34. Dense canopy (NDVI > 0.70) suppresses LST below 28–30°C (6–8°C delta).💡 Takeaway: Urban forestry can't just count saplings. Without threshold canopy density, isolated greenery is decoration, not climate infrastructure.
🔗 Link to the research:
https://www.datastory.org.ua/calgarys-summer-heat-a-2025-satellite-perspective/#RemoteSensing #GIS #RStats #rspatial #terra #tidyverse #GoogleEarthEngine #UrbanForestry #Calgary #YYC #OpenData #GreennessOfCalgary #FOSSGIS #Landsat #Sentinel2
-
Does urban greenery yield microclimatic cooling? Spatial analysis of Calgary (Summer 2025) shows a non-linear NDVI vs LST response.
🛠 Stack: Google Earth Engine (Landsat 8/9, Sentinel-2) + R (terra, tidyverse).
📊 Key Findings:
🔹 Cooling Deficit (NDVI < 0.34): LST stays trapped at 35–36°C. Heat stress overrides evapotranspiration; saplings & isolated lawns fail to cool.
🔹 Tipping Point (NDVI > 0.34): Cooling begins above 0.34. Dense canopy (NDVI > 0.70) suppresses LST below 28–30°C (6–8°C delta).💡 Takeaway: Urban forestry can't just count saplings. Without threshold canopy density, isolated greenery is decoration, not climate infrastructure.
🔗 Link to the research:
https://www.datastory.org.ua/calgarys-summer-heat-a-2025-satellite-perspective/#RemoteSensing #GIS #RStats #rspatial #terra #tidyverse #GoogleEarthEngine #UrbanForestry #Calgary #YYC #OpenData #GreennessOfCalgary #FOSSGIS #Landsat #Sentinel2
-
Does urban greenery yield microclimatic cooling? Spatial analysis of Calgary (Summer 2025) shows a non-linear NDVI vs LST response.
🛠 Stack: Google Earth Engine (Landsat 8/9, Sentinel-2) + R (terra, tidyverse).
📊 Key Findings:
🔹 Cooling Deficit (NDVI < 0.34): LST stays trapped at 35–36°C. Heat stress overrides evapotranspiration; saplings & isolated lawns fail to cool.
🔹 Tipping Point (NDVI > 0.34): Cooling begins above 0.34. Dense canopy (NDVI > 0.70) suppresses LST below 28–30°C (6–8°C delta).💡 Takeaway: Urban forestry can't just count saplings. Without threshold canopy density, isolated greenery is decoration, not climate infrastructure.
🔗 Link to the research:
https://www.datastory.org.ua/calgarys-summer-heat-a-2025-satellite-perspective/#RemoteSensing #GIS #RStats #rspatial #terra #tidyverse #GoogleEarthEngine #UrbanForestry #Calgary #YYC #OpenData #GreennessOfCalgary #FOSSGIS #Landsat #Sentinel2
-
We mapped urban heat island intensification in Minneapolis–St. Paul and Chicago with 32 years of Landsat (1984–2016). The pattern is stubborn: land-cover change writes itself into surface temperature, neighborhood by neighborhood, and it compounds.
Paper in Geocarto International: https://doi.org/10.1080/10106049.2019.1655802
#UrbanHeatIsland #Landsat #RemoteSensing #ClimateChange #Cities
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We mapped urban heat island intensification in Minneapolis–St. Paul and Chicago with 32 years of Landsat (1984–2016). The pattern is stubborn: land-cover change writes itself into surface temperature, neighborhood by neighborhood, and it compounds.
Paper in Geocarto International: https://doi.org/10.1080/10106049.2019.1655802
#UrbanHeatIsland #Landsat #RemoteSensing #ClimateChange #Cities
-
We mapped urban heat island intensification in Minneapolis–St. Paul and Chicago with 32 years of Landsat (1984–2016). The pattern is stubborn: land-cover change writes itself into surface temperature, neighborhood by neighborhood, and it compounds.
Paper in Geocarto International: https://doi.org/10.1080/10106049.2019.1655802
#UrbanHeatIsland #Landsat #RemoteSensing #ClimateChange #Cities
-
We mapped urban heat island intensification in Minneapolis–St. Paul and Chicago with 32 years of Landsat (1984–2016). The pattern is stubborn: land-cover change writes itself into surface temperature, neighborhood by neighborhood, and it compounds.
Paper in Geocarto International: https://doi.org/10.1080/10106049.2019.1655802
#UrbanHeatIsland #Landsat #RemoteSensing #ClimateChange #Cities
-
We mapped urban heat island intensification in Minneapolis–St. Paul and Chicago with 32 years of Landsat (1984–2016). The pattern is stubborn: land-cover change writes itself into surface temperature, neighborhood by neighborhood, and it compounds.
Paper in Geocarto International: https://doi.org/10.1080/10106049.2019.1655802
#UrbanHeatIsland #Landsat #RemoteSensing #ClimateChange #Cities
-
https://www.europesays.com/be-nl/104247/ Satellietbeelden van uitwerpselen van pinguïns onthullen de gevolgen van klimaatverandering voor deze diersoort #Aarde #aardobservatie #Adéliepinguïn #BE #België #Belgium #klimaat #Klimaatverandering #landsat #OpwarmingVanDeAarde #pinguïnkolonies #pinguïns #satellietbeelden #Science #ScienceAndTechnology #ScienceAndTechnology #Technologie #Technology #uitwerpselen #voedselketen #Wetenschap #WetenschapEnTechnologie #WetenschapTechnologie
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Fans of the Arctic – NASA Science
Editor’s Note: Today’s story is the answer to the July Puzzler. Call it an alluvial face-off. On the southern…
#NewsBeep #News #Space #AU #Australia #EarthObservatory #Ice&Glaciers #Landsat-9 #Science #surfacewater #topography
https://www.newsbeep.com/au/799110/ -
Fans of the Arctic – NASA Science
Editor’s Note: Today’s story is the answer to the July Puzzler. Call it an alluvial face-off. On the southern…
#NewsBeep #News #Space #AU #Australia #EarthObservatory #Ice&Glaciers #Landsat-9 #Science #surfacewater #topography
https://www.newsbeep.com/au/799110/ -
Fans of the Arctic – NASA Science
Editor’s Note: Today’s story is the answer to the July Puzzler. Call it an alluvial face-off. On the southern…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Space #EarthObservatory #Ice&Glaciers #Landsat-9 #Science #surfacewater #topography
https://www.newsbeep.com/us/761747/ -
Fans of the Arctic – NASA Science
Editor’s Note: Today’s story is the answer to the July Puzzler. Call it an alluvial face-off. On the southern…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Space #EarthObservatory #Ice&Glaciers #Landsat-9 #Science #surfacewater #topography
https://www.newsbeep.com/us/761747/ -
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 -
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 -
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 -
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 -
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 -
#Earthdata:
"
Little Water Left in San Carlos ReservoirOPERA's dynamic surface water extent layer and Landsat images show extremely low water levels in this Arizona reservoir in June 2026.
"
https://www.earthdata.nasa.gov/news/worldview-image-archive/little-water-left-san-carlos-reservoir29.6.2026
#Arizona #CoolidgeDam #EO #Erdbeobachtung #Klimakrise #Klimawandel #Landsat #LowWater #NASA #Niedrigwasser #reservoir #SanCarlosReservoir #Sentinel2 #Staudamm #USA #Wasser #Water
-
#Earthdata:
"
Little Water Left in San Carlos ReservoirOPERA's dynamic surface water extent layer and Landsat images show extremely low water levels in this Arizona reservoir in June 2026.
"
https://www.earthdata.nasa.gov/news/worldview-image-archive/little-water-left-san-carlos-reservoir29.6.2026
#Arizona #CoolidgeDam #EO #Erdbeobachtung #Klimakrise #Klimawandel #Landsat #LowWater #NASA #Niedrigwasser #reservoir #SanCarlosReservoir #Sentinel2 #Staudamm #USA #Wasser #Water
