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#modis — Public Fediverse posts

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

  1. Remote Sensing And GIS-Supported Framework Of Pre-Monsoon Drought Assessment In Bangladesh (2000–2022) Using CHIRPS-Based SPI-3 And MODIS-Derived Vegetation And Temperature Indices
    --
    doi.org/10.1007/s12665-026-128 <-- shared paper
    --
    H/T MD. ABDULLAH AL MAMUNM | Studying PhD in Rural and Environmental Sciences
    “১ বছর ২০ দিন লেগে গেল! প্রথম ৪ জন রিভিউয়ারের প্রায় ৫০+ কমেন্টের পর মনে হয়েছিল আর এগোব না। তবে আমার সুপারভাইজার বলেছিলেন, “রিজেকশনের চেয়ে কমেন্ট ফেস করা ভালো।”
    --
    #GIS #spatial #mapping #remotesensing #Bangladesh #earthobservation #water #hydrology #premoonsoon #moonsoon #drought #CHIRPS #MODIS #SPI #vegetation #temperature #indices #parameters #SPI #NDVI #VCI #TCI #VHI #monitoring #droughts #agriculture #farming #crop #cultivation #yield #foodsecurity #weather #rainfall #precipitation #Pearsoncorrelation #geostatistics #irrigation #watersecurity #foodsecurity #policy #planning

  2. Remote Sensing And GIS-Supported Framework Of Pre-Monsoon Drought Assessment In Bangladesh (2000–2022) Using CHIRPS-Based SPI-3 And MODIS-Derived Vegetation And Temperature Indices
    --
    doi.org/10.1007/s12665-026-128 <-- shared paper
    --
    H/T MD. ABDULLAH AL MAMUNM | Studying PhD in Rural and Environmental Sciences
    “১ বছর ২০ দিন লেগে গেল! প্রথম ৪ জন রিভিউয়ারের প্রায় ৫০+ কমেন্টের পর মনে হয়েছিল আর এগোব না। তবে আমার সুপারভাইজার বলেছিলেন, “রিজেকশনের চেয়ে কমেন্ট ফেস করা ভালো।”
    --
    #GIS #spatial #mapping #remotesensing #Bangladesh #earthobservation #water #hydrology #premoonsoon #moonsoon #drought #CHIRPS #MODIS #SPI #vegetation #temperature #indices #parameters #SPI #NDVI #VCI #TCI #VHI #monitoring #droughts #agriculture #farming #crop #cultivation #yield #foodsecurity #weather #rainfall #precipitation #Pearsoncorrelation #geostatistics #irrigation #watersecurity #foodsecurity #policy #planning

  3. Remote Sensing And GIS-Supported Framework Of Pre-Monsoon Drought Assessment In Bangladesh (2000–2022) Using CHIRPS-Based SPI-3 And MODIS-Derived Vegetation And Temperature Indices
    --
    doi.org/10.1007/s12665-026-128 <-- shared paper
    --
    H/T MD. ABDULLAH AL MAMUNM | Studying PhD in Rural and Environmental Sciences
    “১ বছর ২০ দিন লেগে গেল! প্রথম ৪ জন রিভিউয়ারের প্রায় ৫০+ কমেন্টের পর মনে হয়েছিল আর এগোব না। তবে আমার সুপারভাইজার বলেছিলেন, “রিজেকশনের চেয়ে কমেন্ট ফেস করা ভালো।”
    --
    #GIS #spatial #mapping #remotesensing #Bangladesh #earthobservation #water #hydrology #premoonsoon #moonsoon #drought #CHIRPS #MODIS #SPI #vegetation #temperature #indices #parameters #SPI #NDVI #VCI #TCI #VHI #monitoring #droughts #agriculture #farming #crop #cultivation #yield #foodsecurity #weather #rainfall #precipitation #Pearsoncorrelation #geostatistics #irrigation #watersecurity #foodsecurity #policy #planning

  4. Remote Sensing And GIS-Supported Framework Of Pre-Monsoon Drought Assessment In Bangladesh (2000–2022) Using CHIRPS-Based SPI-3 And MODIS-Derived Vegetation And Temperature Indices
    --
    doi.org/10.1007/s12665-026-128 <-- shared paper
    --
    H/T MD. ABDULLAH AL MAMUNM | Studying PhD in Rural and Environmental Sciences
    “১ বছর ২০ দিন লেগে গেল! প্রথম ৪ জন রিভিউয়ারের প্রায় ৫০+ কমেন্টের পর মনে হয়েছিল আর এগোব না। তবে আমার সুপারভাইজার বলেছিলেন, “রিজেকশনের চেয়ে কমেন্ট ফেস করা ভালো।”
    --
    #GIS #spatial #mapping #remotesensing #Bangladesh #earthobservation #water #hydrology #premoonsoon #moonsoon #drought #CHIRPS #MODIS #SPI #vegetation #temperature #indices #parameters #SPI #NDVI #VCI #TCI #VHI #monitoring #droughts #agriculture #farming #crop #cultivation #yield #foodsecurity #weather #rainfall #precipitation #Pearsoncorrelation #geostatistics #irrigation #watersecurity #foodsecurity #policy #planning

  5. Remote Sensing And GIS-Supported Framework Of Pre-Monsoon Drought Assessment In Bangladesh (2000–2022) Using CHIRPS-Based SPI-3 And MODIS-Derived Vegetation And Temperature Indices
    --
    doi.org/10.1007/s12665-026-128 <-- shared paper
    --
    H/T MD. ABDULLAH AL MAMUNM | Studying PhD in Rural and Environmental Sciences
    “১ বছর ২০ দিন লেগে গেল! প্রথম ৪ জন রিভিউয়ারের প্রায় ৫০+ কমেন্টের পর মনে হয়েছিল আর এগোব না। তবে আমার সুপারভাইজার বলেছিলেন, “রিজেকশনের চেয়ে কমেন্ট ফেস করা ভালো।”
    --

  6. Exploring large-scale landscape structures with a simple Principal Component Analysis (PCA) applied to a MODIS composite.

    The PCA reveals striking contrasts in surface materials, vegetation patterns, and terrain transitions across western Canada — from the Rocky Mountains into the prairies and the Canadian Shield.
    Even with moderate-resolution MODIS data, spectral variability produces a surprisingly rich view of regional geography and geomorphology.

    #RemoteSensing #EarthObservation #Geology #PCA #MODIS #OpenData #GIS #QGIS #Geomorphology #Geoscience #RStats #Lithology #GeoDataArt #GeoSpectralArt #BorealForest #Taiga #Canada #Alberta

  7. 🛰️ Today I’m sharing one of my favourite large-scale remote sensing experiments:
    a Principal Component Analysis (PCA) of MODIS composite data for the three central provinces of Canada (Alberta, Saskatchewan, Manitoba).

    On this map:
    - PC1 emphasizes broad ecological zones and vegetation productivity
    - PC2 highlights soil and surface moisture differences
    - PC3 captures subtle spectral variations — often linked to geology, wetlands, disturbance patterns, or local microclimates

    Even though it looks abstract, PCA is a kind of “spectral fingerprint” of the land. It summarises thousands of square kilometres into a single visual structure that shows how the Canadian Prairies and Boreal regions differ and transition into one another.

    #RemoteSensing #MODIS #Geospatial #EarthObservation #Rstats #DataVisualization #PCA #SatelliteData #Canada #Alberta #Saskatchewan #Manitoba #EnvironmentalData #GeoDataArt #GeoSpectralArt

  8. Mithilfe von #Satellitendaten erkennt ein neues Verfahren das winzige #Zooplankton Calanus finmarchicus, das für Nordatlantische #Glattwale lebenswichtig ist.

    Die Methode hilft, Fressgebiete im Golf von Maine vorherzusagen. Das könnte Kollisionen mit Schiffen und Verwicklungen in Fischereigeräte reduzieren. Die Daten stammen von NASA-Instrumenten wie #MODIS und künftig #PACE.

    dx.doi.org/10.3389/fmars.2025.

    #Weltnaturschutz #Meeresforschung #Klimawandel #NASA #Ozeanbeobachtung #Wale #Artenschutz

  9. "Satellite-based evidence of recent decline in global forest recovery rate from tree mortality events" by Yuchao Yan et al 2025.
    Fascinating and educational. All the more for us in Germany and Finland, and likely other Europeans, whose forests morphed from CO2 sink to source. The study ends with 2020 data tho, Europe with 2018.
    Only non-fire mortality events were analyzed.
    I learned how recovery after a drought-driven forest mortality event depends on🌡️💧during recovery; not so much the event severity.
    nature.com/articles/s41477-025
    Free e-pdf provided by one of the authors:
    rdcu.be/eigV4

    Don't know about you but to me, a paper is particularly "good" if I'm left with a host of new pressing questions. "Why did they..? Was it maybe ..? What if it had been...?"

    For a recovery phase, they differentiate between recovery of the canopy greening and recovery of water content in the canopy. Both are based on satellite obs only. And if a satellite image suggests greening is recovered to pre-mortality level, it might not actually be re-greening from recovered old or new young trees but could be merely dense shrubbery. The Greening parameter is often used to glean carbon stock. Shrubs have less biomass=less carbon than trees.
    The water content in the canopy then somehow helps to clarify the actual recovery state. How? 🤷‍♀️

    Water content in canopy always takes far longer to recover than re-greening.
    Longer = years and years longer.
    Always = in the 1980s as well. Which I take as: that's the normal baseline behaviour for a given biome, a given latitude zone, a given climate zone, a given elevation, a given human intervention etc.

    Supplementary Fig. 5. c and d show numbers for North America and Tropics static-content.springer.com/es .
    Recovery Time in years for water in canopy in North America
    in the 1990s took 2 - 12, average 6.
    in the 2000s took 2 - 18, average 9.

    in the Tropics:
    in the 1990s took 2 - 12, average 6.
    in the 2000s took 2 - 11, average 7.

    Europe is missing an extra whiskers plot. Maybe they saved this for their next paper. But European events are included up to 2018, if I got it right.

    With all the factors to be considered, and bias in numbers of events in any given factor, making recovery comparable across regions, across biomes, across climate zones, a global average doesn't seem very useful.
    However, here are the global numbers from Figure 1d for
    Recovery time RT for water in canopy. In the 1980s RT was between 2 and 15, average 8, median 6 .
    In the 1990s, RT was 2 - 22, average 8, median 6.
    In the 2000s, RT was 2 - 20, average 9, median 9 years.

    Am curious wrt the missing potential cause for the greatly reduced RecoveryTime in the 2010s in Fig.1d. Is that an artefact of the shortened observation time for these 10 most recent mortality yrs?
    And Greening recovered astonishingly quickly in the 2010s. is it the high CO2 fertilisation or a regional bias from the events in this period?

    #climate #ecosystem #drought #forest #satellite #MODIS #ForestMortality

  10. Due to #Eaton #WildFire mandatory evacuation orders at the Jet Propulsion Laboratory (#JPL) in #Pasadena, #California, processing of certain visualization products have been halted. This includes visualization products from #SMAP, #MLS, #GHRSST, #OSCAR sea surface currents, and some #MODIS Terra and Aqua Sea Surface Temperature products.

    #ClimateCrisis

  11. Next month at #AGU24.

    A multi-scale study assessing snow albedo variability in mountain terrain.

    Using #MODIS satellite data, AVIRIS-NG airborne spectroscopy, and ground-based measurements, they aim to improve the characterization of snow surface properties and albedo in complex environments, addressing limitations in coarse #satellite observations.

    🗓️🔗: bit.ly/AGU24_C23C14

    #snow #science #research #CriticalZone

  12. [lance-modis] Unavailability of SNPP L2+ EDR data products until further notice -- Update

    #satellites #modis #viirs #RemoteImaging

  13. The eruption cloud of #Shiveluch 🌋 #Kamchatka seen today by the Aqua #MODIS 🛰️polar orbiting satellite. The thick plume at the time of this image is about 400 km long

  14. Hurricane Helene has "significantly impacted operations at NOAA National Centers for Environmental Information (NCEI)." -- now impacting satellite operations for LANCE-MODIS.
    #HurricaneHelene #NCEI #NOAA #SatelliteImaging #MODIS

  15. Hmm, satellite hotspots in Southern Lebanon over the last 7 days 👀 #Lebanon #RemoteImaging #VIIRS #MODIS

  16. MODIS imaging is so cool but also jaw-dropping. This datapoint indicates that the 1km by 1km pixel grid point was estimated to be radiating almost 500 MW of power along the perimeter of the Airport fire (near Los Angeles) today.

    I've seen MODIS datapoints up to 3.2 GW, so, again to the jaw-dropping part, do 1km squares of wildfires occasionally produce nuclear power station levels of thermal emissions? Is that right?

    #nasa #modis #wildfire

  17. #NASA hat mit dem #GEOS-Modell eine beeindruckende globale Karte des #CO2-Ausstoßes erstellt. Das hochauflösende #Wettermodell nutzt #Supercomputer und Milliarden von Datenpunkten, um atmosphärische Ereignisse wie #Stürme und #Wolkenformationen darzustellen. Das Modell zieht Daten von #Erdbeobachtungen und #Satelliteninstrumenten, darunter #MODIS und #VIIRS, heran und liefert eine Auflösung, die über 100 Mal höher ist als die typischer Wettermodelle.

    #Klimaforschung

    svs.gsfc.nasa.gov/14631

  18. A brand new NASA video reveals the fascinating patterns of carbon dioxide moving around our atmosphere.

    The visualization shows #CO2 pouring out from major cities in the U.S., before being blown into swirling eddies by atmospheric currents.

    The video, which shows the CO2 patterns between January and March 2020, was created using a model named the Goddard Earth Observing System ( #GEOS ),

    which uses supercomputers to simulate the atmosphere based on data from satellite instruments including the Terra satellite's #MODIS and the Suomi-NPP satellite's #VIIRS, as well as ground observations

    youtu.be/zZ-lMDtiI-k

  19. Plume of Saharan #dust sweeping northeastwards across the Mediterranean yesterday (11 Mar) from Libya🇱🇾 to Greece🇬🇷 (in cloud) as seen by Aqua #MODIS 🛰️#duststorm

  20. Congrats to new World Champions!
    🍾🏆⚽️🛰️💕🇦🇷

    vegetation cycles

  21. After pushing a dangerous storm surge ashore along the coast, the slow-moving cyclone dumped tremendous amounts of rain inland. go.nasa.gov/2TQBOF6 #NASA #MODIS #Idai #CycloneIdai #MozambiqueFloods