Satellite Data for Environmental Monitoring

Spatial resolution changes everything. I came across a great comparison from Abhay Das showing pixelation captured by different satellites from 250 m down to 0.3 m. And it’s a clear demonstrations of why resolution determines the entire category of analysis you can do. At 250 m (MODIS), you’re basically working with regional to global aggregates. Jump to 30 m (Landsat) or 10 m (Sentinel-2) and you have the spatial context for land cover mapping, watershed analysis, vegetation health, and long-term environmental monitoring. Move into 3 m (PlanetScope) and you start resolving field boundaries, crop structure, forest edges, and urban growth with far more precision. Below 1 m with Cartosat and WorldView-3? Now we can see building footprints, transportation networks, roof geometry, fine grained urban morphology. Every satellite sits at a specific point in the capability curve and the "right" one depends entirely on your analytical goal. Love to see amazing content from geospatial creators. Keep up the great work Abhay. 🌎 I'm Matt and I talk about modern GIS, earth observation, AI, and how geospatial is changing. 📬 Want more like this? Join 10k+ others learning from my newsletter → forrest.nyc

🌍 NASA - National Aeronautics and Space Administration, in collaboration with data from the World Meteorological Organization, merges satellite observations, advanced models, and immense computing power to monitor aerosols in our atmosphere. These tiny, invisible solid or liquid particles — including black carbon (orange/red), sea salt (cyan), dust (magenta), and sulfates (green) — travel vast distances, affecting air quality, human health, climate, and visibility far from their source. 🔹 In South America, black carbon from wildfires burning in the Amazon rainforest drifts across the continent. 🔹 Over the Atlantic, massive plumes of dust from Northern Africa journey westward toward the Americas, influencing ecosystems, weather, and even hurricane formation. This striking visualization, powered by NASA’s Goddard Earth Observing System (GEOS) model and informed by WMO’s authoritative climate data, delivers realistic, high-resolution weather and aerosol insights. These data streams fuel #AI innovation and help provide customized environmental predictions — critical tools for #climateresilience and disaster preparedness #EW4ALL. ➡ A reminder: Every particle tells a story about the planet’s interconnected systems — and our shared responsibility to protect them

Me and my colleagues at Google DeepMind and Google Research are sharing our latest work on tropical cyclone prediction, now available through a research tool, Weather Lab: https://lnkd.in/dNtjmiYq Over the past 50 years, tropical cyclones, also known as hurricanes or typhoons, have claimed more than 779,000 lives and caused $1.4 trillion in economic losses [WMO]. For the millions of people living in their path, the accuracy of weather forecasting is the most critical line of defense. In an effort to protect lives and property from this threat, we’ve built a powerful new machine learning (ML)-based ensemble weather model, deployed it operationally on Weather Lab, and partnered with experts from the U.S. National Hurricane Center (NHC) who will assess its live predictions alongside their established forecasting tools. The ensemble mean cyclone track of our new model gains about 1.5 days of position error advantage over ECMWF ENS in tests based on NHC protocols. And surprisingly, our model has a lower average intensity error than NOAA’s high-resolution hurricane model, HAFS-A, in more than 60 of the 74 cyclones evaluated in 2023 and 2024 in the East Pacific and North Atlantic basins. We achieved this by building a new kind of ML weather model, FGN [Ferran Alet Puig et al., 2025], which substantially outperforms GenCast on probabilistic metrics, and specialising it for cyclone tracking by training it on a record of nearly 5,000 tropical cyclones from the past 45 years. Most human forecasters do not trust a weather model until its performance is demonstrated in a real-time setting. That’s why we built Weather Lab, available globally, providing access to live and historical visualisations of tropical cyclone predictions from our new ML weather model, with WeatherNext and ECMWF models shown for comparison. We recently enabled live data downloads in CSV and ATCF format for experts to evaluate. This is a powerful new tool in the toolbox, but no single model is perfect. It will remain key that human forecasters evaluate a wide range of both ML and physics-based predictions when issuing public warnings for cyclone threats. And of course, ML weather models continue to depend on the historical and real-time availability of atmospheric analysis datasets produced by physical modelling centres, and the continued quality and coverage of the Earth’s observing system. Tropical cyclones will likely become more destructive over time [IPCC, 2023]. It is crucial we continue improving our monitoring, prediction, and understanding of these complex beasts of physics. Try Weather Lab: https://lnkd.in/dNtjmiYq  Blog post: https://lnkd.in/dkj8cYan  FGN (Alet et al., 2025): https://lnkd.in/dJhP9Kj2  WMO: https://lnkd.in/dPt94VX5 IPCC, 2023: https://lnkd.in/dj5n-Rqg 

🌍 Earth’s MRI scan has begun and a new era of nature monitoring is here European Space Agency - ESA’s Biomass satellite is now in orbit and the first images are spectacular. Using powerful P-band radar (capable of seeing through clouds and even dense jungle canopies), it’s mapping something we've never seen from space before: The woody biomass of our forests, the trunks, branches and stems that hold most of the carbon. Why this is a game changer: ✅ First-of-its-kind radar ✅ Global forest scans every 6 months ✅ Carbon mapping on a planetary scale ✅ Even revealing what’s beneath the forests, sand and ice Already it’s uncovered: 📍 The winding Beni River in Bolivia, deep within the Amazon rainforest 🌋 Volcanoes hidden beneath the canopy in Brazil and Indonesia’s Halmahera 💧 Wetlands and savannas in Gabon and ancient riverbeds in the Sahara ❄️ And even glacier flows and buried valleys in Antarctica’s Transantarctic Mountains It’s like giving the planet an MRI scan. And it’s doing something we urgently need, measuring what matters in the fight to protect and restore nature. Over the next 5 years, Biomass will help answer some of the biggest questions in climate and nature science: ⤷ How much is being lost? ⤷ Where is nature bouncing back? ⤷ Where is carbon stored in forests? This goes beyond just forests and will help us with climate stability, smarter policies and the data we need for a nature-positive future. 🔍 Should we be measuring biomass alongside GDP? #NatureTech #Biomass #Forests #NatureRestoration #EarthObservation 🎥 European Space Agency

#Satellites sitting more than 22,200 miles (35,700 kilometers) above the Earth’s surface have been capturing storms and weather data for decades. Now, scientists are essentially #hacking the data coming back for another purpose: spotting methane emissions. The breakthrough is the latest in a series from a group of young scientists affiliated with Harvard University, the Universitat Politècnica de València (UPV) and the United Nations’ International Methane Emissions Observatory that have rapidly expanded researchers’ ability to spot leaks using a wide range of satellites not originally designed to track methane. The innovation could have far-reaching consequences for fossil fuel operators unable or unwilling to halt major #methane releases because it allows researchers to observe emissions every five minutes and estimate the total amount emitted. The approach, which uses shortwave infrared observations from the NOAA: National Oceanic & Atmospheric Administration's Geostationary Operational Environmental Satellites (GOES), can detect large-emitting events of around tens of metric tons an hour or larger in North America. The new approach enables near continuous, real-time coverage and contrasts with other satellites currently used to detect methane, which are in low-Earth orbit and snap images as they circumnavigate the globe at speeds of around 17,000 miles per hour, only allowing scientists to estimate emission rates. “GOES can detect brief releases that the other satellites miss, and it can trace detached plumes back to their sources,” said Daniel Varon, a research associate at Harvard's Atmospheric Chemistry Modeling Group who first proposed the concept in 2022. “It can also quantify total release mass and duration, rather than just instantaneous estimates of emission rate.” The new technique is already being used by geoanalytics firms and scientists to quantify major emissions events in North America. Kayrros used the approach to estimate that a fossil gas pipeline spewed about 840 metric tons of methane into the atmosphere after it was ruptured by a farmer using an excavator. The short-term climate impact of the event was roughly equal to the annual emissions from 17,000 US cars. Read more in my latest for Bloomberg Green through the gift link below: https://lnkd.in/gVjYnNYE

Orbiting #methane "speed cameras" are catching #oilandgas companies in the act. Satellite images are so clear it's possible to see methane #emissions at the individual asset level. At least two dozen high-resolution satellites are expected to be in orbit by the end of this year. The images sent back are crystal clear and leave little doubt about WHO is responsible for the leaks. These missions will usher in a new era of climate transparency and will help keep oil and gas companies accountable 👏 For example, the image below is of a methane release observed on 5th Feb near Exxon Mobil's Big Eddy Unit 156 that Exxon initially failed to disclose to state officials. After Bloomberg shared the imagery with Exxon, the company notified state regulators. Exxon blamed the omission on "human error" and said "someone forgot to file a form" 🙄 While fines and enforcement vary, companies increasingly face reputational risks and potential loss of business if their operations are seen as contributing more than peers to the climate crisis. Methane has 86x the warming power of carbon dioxide during its first two decades in the atmosphere. Halting emissions of the greenhouse gas could do more to slow climate change in the near-term than almost any other single measure. Facility-level information on emissions is hugely valuable because it's directly actionable. The methane observations are also exposing flaws in decades-old reporting approaches used by companies and government agencies that have typically underestimated emissions. For example, satellite data published earlier this year shows that in the US, methane emissions from oil and gas operations from 2010-2019 were 70% higher than amounts reported by the Environmental Protection Agency. This year could see a wave of new reports on operator leaks, as new orbitals increase the coverage and frequency of observations. For operators unable to halt their emissions, that may mean a loss of credibility, fees or trouble insuring future projects. Fossil fuel companies are running out of places to hide. #energy #sustainability #energytransition #emissionsreduction

Today, Nature Communications published our latest research, led by Amit Misra from Microsoft’s AI for Good Lab: a global flood detection model built using 10 years of Synthetic Aperture Radar (SAR) satellite data. It can detect floods through clouds, at night, and in remote areas—filling a critical gap in global disaster data. Already in use in Kenya and Ethiopia, this open-source tool is helping governments respond faster and plan smarter. It’s a powerful example of how AI can drive climate resilience.

There is a massive archive of flash flood data hidden inside the news. 📰 Unlike earthquakes, which are tracked by a global network of sensors, the world is living in a “data desert” when it comes to smaller, fast-moving events like flash floods. This data gap is a major hurdle for climate resilience. Without a historical baseline, it’s nearly impossible to predict future hazards at a local level. To solve this, my colleagues at Google Research are introducing a new approach called Groundsource. It uses Gemini to turn 25 years of news articles, government reports, and local bulletins into a historical flash flooding map. The team created a massive global archive of 2.6 million flood events across 150 countries. Using this data, they can provide near-global urban flash flood forecasts up to 24 hours before an event. These forecasts are now being rolled out in Google’s Flood Hub, our free and public platform that provides AI-driven flood forecasts to help communities stay safe. For me, the most exciting part is seeing how AI can transform the world’s “unstructured memory” into a robust scientific baseline. Manually extracting this information at scale would be impossible, but using AI to organize relevant historical data lets us build a more resilient future where communities everywhere can better prepare for flash floods. Learn more about how we’re turning the news into life-saving data. 👇 goo.gle/4ryKfzH

Thought I’d share more about Philip Morris International’s geospatial analysis program, that represents a great intersection of technology and purpose. Since 2020, we've been using satellite imagery and advanced mapping to literally see our environmental footprint across 76.4 million hectares (roughly the size of Chile 🇨🇱 ♥️ ). The technology is transforming our decision-making across multiple contexts: • Carbon sequestration: Identifying optimal areas in Brazil 🇧🇷 to enhance local carbon sinks through our Perfect Forest approach • Sustainable sourcing: Assessing potential tobacco locations in India 🇮🇳 to ensure alignment with environmental targets and minimize deforestation risk • Land use evaluation: Verifying farm locations in Brazil 🇧🇷 maintain zero conversion of natural land while exploring carbon removal opportunities • Water optimization: Guiding projects in Italy 🇮🇹that saved over 6,800 cubic meters of water annually by precisely targeting high-impact interventions What moves me about this approach is how it connects our global #sustainability strategy to specific places and communities. As we navigate an increasingly complex landscape of #climate and #biodiversity challenges, these tools help us anticipate future risks while addressing immediate needs. The most powerful strategies do more than react to today's problems, they build capabilities that help us see around corners — this is what #sustainability means for us. By continuously advancing our geospatial capabilities, we're creating the foundation for more informed decisions, driving innovation, and making tangible progress on our efforts to preserve nature, build resilience, and ensure business continuity. Read more about it here ➡️ https://lnkd.in/eDAeGDHF #SustainableBusiness #GeospatialTechnology #EnvironmentalStewardship Cc: Michele Pisetta, Mariano Mejía Valenzuela, Nthabiseng Nooe, 🌎 🌱 💚

Unlocking the Power of GeoAI: From Raw Geospatial Data to Actionable Insights GeoAI is fundamentally changing the way we work with geospatial data. Today, artificial intelligence is not just a research topic, but a practical tool that helps us turn massive amounts of aerial imagery and lidar data into real, actionable information. By combining neural networks with proven photogrammetry and rule-based quality assurance, we can now extract detailed land cover maps, analyze urban surfaces, and even simulate urban climate with a level of precision that was unthinkable just a few years ago. One of the most exciting aspects is how GeoAI enables us to move beyond traditional mapping. With AI-powered segmentation, we can distinguish even the smallest features in urban environments and keep our data up to date. Thanks to TrueOrthos and advanced photogrammetric workflows, geometric distortions are a thing of the past, so data from different times and sensors can be perfectly aligned. This is essential for reliable change detection and multi-source analysis. But the possibilities go even further. Automated analysis of sealed and unsealed surfaces helps cities identify where to prioritize “desealing” for climate resilience. Parcel indexing allows us to aggregate key indicators like green space, building area, or solar installations at any scale, supporting truly data-driven decisions in urban planning and environmental monitoring. And with urban climate simulation, we can combine pixel-precise land cover data with 3D voxel models and CFD to visualize the effects of new trees, green roofs, or lighter pavements, before any construction begins. Even lidar point cloud classification benefits from GeoAI. By combining AI with rule-based checks and external data sources, we achieve robust, scalable, and quality-assured 3D mapping, reducing manual effort and increasing reliability, even in complex or changing environments. GeoAI is already a productive, scalable approach that is shaping the sustainable, data-driven development of our cities and landscapes. With annual updates and hybrid workflows, we ensure that results are not only precise and up to date, but also trusted and actionable. If you want to learn how to turn your geospatial data into valuable information using GeoAI, just reach out or send me a message. Let’s move from data to information, using GeoAI. 💡 Comment | Like | Share   👉 Follow me (Dr. Uwe Bacher) for more Information on exciting topics from the world of geospatial #GeoAI #Geospatial #AerialImagery #Lidar #UrbanPlanning #AI #SmartCities