Satellite Data for Environmental Monitoring

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

#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

🧠 GPT changed language. Clay might change the way we understand Earth. Clay is an open-source foundation model for Earth: trained on massive amounts of satellite imagery across location and time. It transforms the complexity of environmental data into powerful embeddings that can be used to: ✅ Identify land cover, crop types, or urban expansion ✅ Detect change like wildfires, floods, or deforestation ✅ Power downstream models for prediction, classification, and mapping ✅ Serve as a backbone for custom geospatial AI pipelines The result? A model that understands Earth the way LLMs understand language. Training models is tough, plus you need access to massive amounts of data. As foundational models start to get better, the data backbone being built by Cloud-Native Geospatial Forum (CNG) data and computing systems that can leverage these models like those we are working on at Wherobots can help bring these models to global scale. This is bigger than just another geospatial model. It’s a signal that foundation models are coming to remote sensing, and with them, a new paradigm: 🧠 Pre-trained models that can be adapted everywhere 📡 Build models with fewer labels 🌱 Tackle climate, agriculture, and environmental challenges with speed If you’re working in geospatial AI, Earth observation, or climate data: Clay is worth watching. And using. It's open source and live on Hugging Face and GitHub. The geospatial foundation model era is bound to be an exciting one. 🌎 I'm Matt and I talk about modern GIS, geospatial data engineering, and how spatial thinking is changing. 📬 Want more like this? Join 5k+ others learning from my newsletter → forrest.nyc

Rapid methane mitigation is one of the fastest, most effective #ClimateAction solutions to slow near-term warming. As our data and expertise grow, inaction on methane is no longer an oversight - it is a choice. And that choice means countries are missing out on major benefits: cleaner air, better public health, economic gains and stronger crop yields. Last week the United Nations Secretary-General António Guterres issued a Call to Action on Methane, outlining nine priority actions across three key sectors — fossil fuels, agriculture and waste — to be achieved by 2030. The UN is here to support countries deliver this promise, and we at UN Environment Programme are pleased to have been asked by the Secretary-General to advance this Call to Action. This work will include: ➡️ Action to make methane measurable, reportable and verifiable: All companies, including Oil and Gas Decarbonization Charter signatories, must move to direct, asset‑level measurement across operations – aligned with the UNEP International Methane Emissions Observatory - IMEO Oil and Gas Methane Partnership 2.0. ➡️ Advances in remote sensing now make it possible to detect major methane emissions in near real time: UNEP’s Methane Alert and Response System (MARS) has issued more than 5,000 alerts across 33 countries. We need to achieve at least an 80% response rate by 2030. And why UNEP and Bloomberg Philanthropies announced we will work together to support countries deliver this target, with data and technical support to make informed decisions and address methane emissions in near real-time. ➡️ Voluntary action alone is insufficient: Stronger regulations and support through initiatives like the Climate & Clean Air Coalition’s Fossil Fuel Regulatory Programme will be essential to accelerate methane reductions. Explore the Secretary-General’s Call to Action on how we #CutMethane: https://lnkd.in/dddWGKkw More on the UNEP and Bloomberg partnership: https://lnkd.in/dHXfF2E2

🌍 How much can better observations reduce uncertainty in methane emissions? I’m pleased to share our new paper in Advanced Science: “A Global Methane Observation System to Reduce Uncertainty for Anthropogenic and Natural Sources and Sinks for Detecting and Attributing Climate Feedbacks.”  Open access : https://lnkd.in/e2jVx-yC  A major challenge in methane science is not only estimating emissions, but determining which observations are most effective at reducing their uncertainty. In this study, we quantify current uncertainties across major methane sources and explore how different types of observations could reduce them over the next 5–10 years. Current uncertainties remain substantial: about 32% for total anthropogenic emissions, while uncertainties for major natural sources such as tropical and boreal wetlands and fires are generally much larger.  The key result is that different sources require different observing strategies. 🛰️ High-resolution satellites are particularly effective for concentrated point sources such as fossil-fuel production and landfills. 🌿 Flux towers, improved wetland maps and Earth-observation data are needed to constrain spatially diffuse wetland emissions. 🐄 Agricultural sources require better activity data, emission factors and atmospheric constraints. 🌍 Atmospheric CH₄ measurements provide the large-scale constraint needed to detect missing emissions and correct biases in bottom-up estimates. Our observing-system simulations illustrate the potential impact. For Africa, adding 20 strategically located ground-based column CH₄ instruments could theoretically reduce uncertainty in total continental emissions by about 85%. Extrapolating this approach suggests that approximately 50 instruments across Africa, South America and Southeast Asia could greatly improve constraints on tropical methane emissions.  The broader message is that uncertainty reduction is not simply a matter of “more observations”. It requires matching the observing strategy to the spatial structure and processes of each methane source, and combining bottom-up measurements, satellites and atmospheric inversions. Such an integrated system would strengthen our ability both to verify anthropogenic methane mitigation and to detect emerging climate-driven changes in natural methane emissions. Many thanks to all co-authors and collaborators involved in this work #Methane #ClimateChange #EarthObservation #AtmosphericScience #GreenhouseGases #RemoteSensing #CarbonCycle #ClimateScience Advanced Science - 2026 - Ciais - A Global Methane Observation System to Reduce Uncertainty for Anthropogenic and Natural.pdf

Tree-planting projects often fail to put in place the monitoring programs needed to track newly planted forests. This piece in Claire Asher's four-part mini-series, which examines the latest technological solutions to help tree-planting projects achieve scale and long-term efficiency, looks at how remote sensing is being used to monitor planting: 🌳 Satellites are mapping and remapping the entire planet daily, providing real-time data that can be used to monitor forests remotely. 🌳 Drones can fly over or through forests to collect data on tree growth, bridging the gap between on-site measurements and distant satellites. 🌳 Sensors can be installed to monitor individual trees directly, while people can collect and analyze the data electronically from a safer and easier-to-access location. 🌳 Multiple sensors can form a distributed network that returns detailed information on the growth of each tree within huge reforestation plots. "Remote-sensing technologies are an important tool in the reforestation toolkit, making large-scale, long-term monitoring of forests possible," writes Asher. "Satellites, drones and in-forest sensor networks each have their strengths and weaknesses, but taken together, they offer an unparalleled view of the natural world. Combined with other data, they could bring the once-formidable task of forest monitoring within reach." https://lnkd.in/e9ET5vGY

Beef vs Rice. What do they have in common beyond a lot of methane emissions? 🐄🍚 Spend-Based vs Activity-Based which one is better? It still amazes me how many enterprises believe spend-based data can drive any real reliable action for decarbonization. Emissions should be managed like a business and financial data. Data matters, and choosing the right data type can make all the difference! Let me share a real customer example of how spend-based vs. activity-based emissions data can paint drastically different pictures. When calculating Scope 3.1 emissions, we used **granular activity-based data** to provide more accurate insights compared to broader spend-based data. Here's what we found: 📍 Beef Knuckle: Moving from spend-based to activity-based data highlighted a much higher emissions footprint. Instead of using "animal slaughter" as a generic category, we went specific – and saw a significant increase in emissions. Talk about transparency! 🔍 📍 Jasmine Rice: Surprisingly, the shift in approach had the opposite effect. Spend-based data showed rice as the highest hotspot. But with activity-based data, emissions dropped significantly – pushing rice down from the top spot to just one of the top 10 emitters. This is crucial when clients ask if activity-based data always leads to higher emissions. It doesn't! It's about **better matching, greater accuracy**, and understanding true hotspots to take targeted action. 📈✅ Initially, spend-based calculations showed rice as the highest emission hotspot. But by shifting to activity-based calculations, we could see a more accurate footprint, which led to rice dropping significantly – allowing more focus on impactful changes. 🌱💡 👉 Key takeaway: Choosing between spend-based vs. activity-based is more than a technical choice – it's a strategic one. More granular data means better decisions, and better decisions mean faster progress towards sustainability goals. Let's manage our emissions like we manage our businesses – with data, strategy, and results in mind. 💚📊✨ #Sustainability #EmissionsManagement #Scope3 #ActivityBasedData #SpendBasedData #CarbonFootprint #Decarbonization #DataMatters #Terrascope #GranularData #ClimateAction #NetZero

Are you keeping track of your company’s emissions in real-time? It might sound like a small step, but monitoring emissions continuously could be the shift we need for more sustainable industries. Imagine knowing every hour – or even every minute – exactly what’s going into the air, especially in fields like oil and gas, where methane leaks are a growing concern. The stakes are high, with increasing regulatory pressure worldwide and ambitious goals from global conferences like COP26. In this environment, knowing your emissions isn’t just good business; it’s essential. Continuous Emissions Monitoring (CEM) systems offer businesses real-time data about pollutants in the air, water, and even noise pollution. It’s no longer about random sampling or occasional checks; instead, CEM provides a steady, live feed of emissions data directly to the cloud, often powered by IoT. From methane to volatile organic compounds (VOCs) and beyond, companies can see their environmental impact unfold in real time, offering a unique opportunity to act fast on unexpected trends or leaks. For instance, imagine an oil company that can catch a small methane leak early because of real-time monitoring, preventing it from turning into a costly – and environmentally damaging – problem. By having a clear picture of emissions data as it happens, companies can save time, meet regulatory expectations, and ultimately reduce their environmental footprint. Switching to continuous monitoring may seem challenging, especially for large or remote facilities. However, newer IoT solutions have brought down costs and increased accessibility, allowing even larger companies to deploy CEM across wide areas or multiple locations. Instead of using traditional detection methods that are often expensive and labour-intensive, businesses can adopt a system that’s more adaptable to their needs and budget. With emissions monitoring, we’re not just tracking data – we’re getting insight that drives better decisions, enhances accountability, and ultimately pushes us closer to a cleaner, more sustainable future. Is your organization ready to embrace that kind of visibility?

Ever felt like your datasets were just sitting there, lonely and a little bored? You're not alone. The world is awash in data, but without the right tools, it's just a bunch of numbers. A mind-boggling 80% of all data is estimated to have a geospatial component. 🤯 But for many organizations, that rich, locational information is often overlooked, trapped in silos, or too complex to analyze alongside other business data. It's like having a map without knowing how to read it. 🗺️ The Problem: The Geospatial Data Gap 👉 Think about it. You have sales figures, customer demographics, and supply chain logistics. But what if you could overlay that with satellite imagery to see how weather patterns are impacting your delivery routes? Or analyze how a new construction project is affecting foot traffic? 👉 Previously, this was a massive undertaking, requiring specialized GIS (Geographic Information System) software, complex data pipelines, and a team of experts. It was a huge barrier to entry for most data professionals. The Solution: Earth Engine + BigQuery Geospatial 👉 This is where the game-changer comes in. The general availability of Earth Engine in BigQuery and the new geospatial visualization capabilities in BigQuery Studio have made a huge leap forward. It’s like bringing the world's largest public satellite imagery and geospatial data catalog right into your data warehouse. 👉 Now, data analysts can seamlessly combine their own structured data with petabytes of pre-analyzed geospatial data. No more moving massive datasets around! 🚀 Benefits for Your Organization: This isn't just a technical upgrade; it's a strategic one. Here's what this can mean for your business: 👉 Risk Assessment: An insurance provider can quickly analyze changes in extreme weather events to better assess risk and price policies. ☔ 👉 Supply Chain Optimization: Retailers can integrate traffic data and weather forecasts to find the most efficient delivery routes and avoid delays. 🚚 👉 Sustainable Practices: Companies can monitor deforestation or agricultural land changes to ensure their supply chain is sustainable. 🌳 👉 Unified Platform: Analysts can go from data discovery to complex analysis and interactive visualization, all in one place. No more switching between multiple tools. 💻 This unified approach democratizes geospatial analysis, making it accessible to a much broader audience and unlocking powerful new insights that were once out of reach. We're moving beyond static dashboards. The ability to ask "what if" questions and visualize the answers directly on a map is a game-changer. It’s no longer about just analyzing what happened, but understanding where it happened and why. So, let your data explore the world, and see the amazing new stories it has to tell. 💖 Follow Omkar Sawant for more. More details in the comments. #EarthEngine #BigQuery #Geospatial #DataAnalytics #DataScience #CloudComputing #GIS #GoogleCloud #TechTrends #Innovation

𝗡𝗼𝘁 𝗮𝗹𝗹 𝘀𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲𝘀 𝗮𝗿𝗲 𝗰𝗿𝗲𝗮𝘁𝗲𝗱 𝗲𝗾𝘂𝗮𝗹... If you’re working in 𝗚𝗜𝗦, 𝗥𝗲𝗺𝗼𝘁𝗲 𝗦𝗲𝗻𝘀𝗶𝗻𝗴, 𝗼𝗿 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴, then you already know this: Choosing the 𝘄𝗿𝗼𝗻𝗴 𝘀𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 can completely ruin your analysis. But choosing the 𝗿𝗶𝗴𝗵𝘁 𝗼𝗻𝗲? That’s where the magic happens. In this infographic, I’ve broken down some of the 𝗺𝗼𝘀𝘁 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗘𝗮𝗿𝘁𝗵 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻 𝘀𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲𝘀, including: • Landsat 9 • Sentinel-2 • Sentinel-1 • WorldView-3 • PlanetScope • RADARSAT-2 • ALOS-2 • ICESat-2 • GeoEye-1 • TerraSAR-X 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗺𝗮𝗻𝘆 𝗽𝗲𝗼𝗽𝗹𝗲 𝗼𝘃𝗲𝗿𝗹𝗼𝗼𝗸: Different satellites = Different strengths. • Need 𝗳𝗿𝗲𝗲, 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗹𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗱𝗮𝘁𝗮? → Landsat • Need 𝗵𝗶𝗴𝗵-𝗿𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗮𝗴𝗿𝗶𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀? → Sentinel-2 • Need 𝗮𝗹𝗹-𝘄𝗲𝗮𝘁𝗵𝗲𝗿 𝗶𝗺𝗮𝗴𝗶𝗻𝗴 (𝗲𝘃𝗲𝗻 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗰𝗹𝗼𝘂𝗱𝘀)? → Sentinel-1 / SAR satellites • Need 𝘂𝗹𝘁𝗿𝗮-𝗱𝗲𝘁𝗮𝗶𝗹𝗲𝗱 𝗺𝗮𝗽𝗽𝗶𝗻𝗴 (𝟯𝟬 𝗰𝗺!)? → WorldView-3 • Need 𝗱𝗮𝗶𝗹𝘆 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗼𝗳 𝗰𝗵𝗮𝗻𝗴𝗲? → PlanetScope • Need 𝗲𝗹𝗲𝘃𝗮𝘁𝗶𝗼𝗻 & 𝗰𝗮𝗻𝗼𝗽𝘆 𝗵𝗲𝗶𝗴𝗵𝘁? → ICESat-2 𝗣𝗿𝗼 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 (𝘁𝗵𝗮𝘁 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗲𝘀 𝗯𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀 𝗳𝗿𝗼𝗺 𝗲𝘅𝗽𝗲𝗿𝘁𝘀): It’s not about using 𝗼𝗻𝗲 satellite… It’s about 𝗰𝗼𝗺𝗯𝗶𝗻𝗶𝗻𝗴 𝘁𝗵𝗲𝗺. Imagine this workflow: • Use Sentinel-2 for vegetation health • Use Sentinel-1 for flood detection • Use PlanetScope for daily change tracking Now you’re not just mapping… You’re 𝘁𝗲𝗹𝗹𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝘀𝘁𝗼𝗿𝘆. This infographic simplifies: • Spatial Resolution • Sensor Type (Multispectral, SAR, LiDAR) • Real-world Applications So you can quickly answer: “𝗪𝗵𝗶𝗰𝗵 𝘀𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝘀𝗵𝗼𝘂𝗹𝗱 𝗜 𝘂𝘀𝗲 𝗳𝗼𝗿 𝘁𝗵𝗶𝘀 𝗽𝗿𝗼𝗷𝗲𝗰𝘁?” If you find this post valuable, kindly consider reposting. #GIS #RemoteSensing #EarthObservation #Geospatial #SatelliteData #SpatialAnalysis #ClimateChange #Mapping #QGIS #ArcGIS #Landsat #Sentinel #USGS #ESA