Multi-sector data for climate modeling

𝗧𝗵𝗲 𝗡𝗚𝗙𝗦 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗯𝗶𝗴— for the first time, we now have 𝘴𝘩𝘰𝘳𝘵-𝘵𝘦𝘳𝘮 𝘤𝘭𝘪𝘮𝘢𝘵𝘦 𝘴𝘤𝘦𝘯𝘢𝘳𝘪𝘰𝘴 tailored for 𝘀𝘁𝗿𝗲𝘀𝘀 𝘁𝗲𝘀𝘁𝗶𝗻𝗴, 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝘀𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗻𝗲𝗮𝗿-𝘁𝗲𝗿𝗺 𝗺𝗮𝗰𝗿𝗼 𝗿𝗶𝘀𝗸. 🔸 This isn't about 2050. It's the next five years, i.e. 𝟮𝟬𝟮𝟱–𝟮𝟬𝟯𝟬. 🔸 This isn't abstract. It's 𝗚𝗗𝗣 𝘀𝗵𝗼𝗰𝗸𝘀, 𝗰𝗿𝗲𝗱𝗶𝘁 𝗿𝗶𝘀𝗸, 𝗶𝗻𝗳𝗹𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝘂𝗻𝗲𝗺𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁. 𝗧𝗵𝗲𝘀𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝘀𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀: 1.  A smooth transition ("Highway to Paris") 2.  A delayed, abrupt policy shift ("Sudden Wake-Up Call") 3.  Physical risk disasters without transition ("Disasters & Policy Stagnation") 4.  A fragmented world with climate chaos and policy misalignment ("Diverging Realities") These scenarios are a wake-up call for taking short-term climate risks seriously. ➤ Delaying climate action could increase global 𝗚𝗗𝗣 𝗹𝗼𝘀𝘀𝗲𝘀 𝗯𝘆 𝗼𝘃𝗲𝗿 𝟯𝘅, and unemployment spikes by 1.3 percentage points (Sudden Wake-Up Call vs Highway to Paris). ➤ Climate disasters aren’t just regional anymore. Floods, fires and droughts in Asia or Africa can cut European 𝗚𝗗𝗣 𝗯𝘆 𝟭.𝟳%, driven by supply chain exposure. ➤ Credit risk spreads explode in carbon-intensive sectors. In some cases, default probabilities jump by 20–30 percentage points, stressing banks and insurers alike. ➤ Green sectors could lose out if the transition is abrupt, fragmented, or disrupted by physical shocks. 𝗛𝗲𝗿𝗲 𝗶𝘀 𝘄𝗵𝘆 𝘁𝗵𝗲𝘀𝗲 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 𝗮𝗿𝗲 𝗮 𝗴𝗮𝗺𝗲-𝗰𝗵𝗮𝗻𝗴𝗲𝗿 ➤ For the first time, compound hazards—droughts, floods, wildfires—are modelled together, showing how climate risk can become systemic through trade, finance, and supply chains. ➤ Monetary policy is now integrated, so climate shocks affect interest rate paths, inflation dynamics, and macroeconomic volatility. ➤ Financial contagion is now factored in. Using advanced modelling, the framework maps how climate-related losses feed into default risk, cost of capital, and sectoral investment flows. ➤ Sector-by-sector and region-by-region outcomes now include asset-level exposure, probability of default, and sovereign bond repricing, offering tools fit for risk management. 𝗠𝘆 𝘁𝗮𝗸𝗲 This release is a step-change in how we understand and model climate risk. These scenarios are critical because they model economic and financial impacts on business over the next five years. A timeline relevant for senior management, boards and shareholders. Because these scenarios capture dynamic feedback loops, sector-specific capital costs, and second-round effects that ripple through the financial system, the risk science is taken to a whole new level. These real-world complexities have been missing from science to date, which is why these scenarios are so critical. #NGFS #NetZero #ClimateRisk _____________ For updates, follow me on LinkedIn: Scott Kelly

🌾New Dataset Out 🌾 🌾 How do we make crop monitoring truly climate-aware at scale? In many EO/ML pipelines, we can model #crop dynamics reasonably well — but linking them consistently with #weather variability, drought, and #climate extremes across large geographies is still difficult. A major reason is simple: 👉 the community still lacks large-scale, multimodal, ML-ready datasets that unify satellites + climate signals + agricultural outcomes. So my PhD student Adrian Höhl (Technical University of Munich) built one. 📢 Very excited to share our new #ScientificData paper introducing #CropClimateX: a large-scale, multi-task, multi-sensory dataset for climate-aware crop monitoring in the contiguous US (2018–2022). 🔍 What makes CropClimateX different? ✅ 15,500 “minicubes” (each 12×12 km) spanning 1,527 counties ✅ Multi-source EO inputs (including Sentinel-1/2, Landsat-8, MODIS) ✅ Climate + extremes context (e.g., Daymet, U.S. Drought Monitor, heat/cold wave indicators) ✅ Supporting multi-task learning targets such as crop yield and broader crop monitoring applications To keep the dataset representative yet scalable, we use an optimized sampling strategy (Sliding Grid + Genetic Algorithm), reducing redundancy while retaining broad cropland coverage. 🚀 Why we hope this helps CropClimateX is designed to support research on: 🌱 climate-aware crop modeling 🛰️ multi-sensor fusion & spatiotemporal learning 🌍 generalizable EO foundation models for agriculture If you’re working on crop monitoring, climate resilience, or geospatial ML, take a look at CropClimateX. 🔗 Link to paper: https://lnkd.in/d3W3mnFZ 🔗 Link to dataset: https://lnkd.in/dVp4s-Mh 🔗 Link to Github: https://lnkd.in/d8DvYkMD This is a collaboration with Stella Ofori-Ampofo, Miguel Ángel Fernández Torres (Universidad Carlos III de Madrid), and Rıdvan Salih (German Aerospace Center (DLR)). The project is funded by the Deutsche Raumfahrtagentur im DLR in the framework of #ML4Earth (project page: ml4earth.de) #RemoteSensing #EarthObservation #GeospatialAI #ClimateAI #AgTech #CropMonitoring #Datasets #MachineLearning International Future AI4EO Lab, TUM School of Engineering and Design (ED)

CMIP7 climate model data will start to roll out soon and will ultimately feed into the next IPCC AR7 report expected around 2028. This new round is a big step forward in making data application‑ready for users across society, including financial institutions. A key shift is that CMIP7 is explicitly designed around Impacts & Adaptation (I&A) Opportunities: 60 variable groups that bundle what is needed to run impact models or derive climate indicators, for example for Agriculture and Food Systems Impacts or Energy System Impacts. Instead of treating bias‑correction and downscaling as second-class components, CMIP7 plans for them from the start, with more standardised, archive‑wide access to sub‑daily data, higher spatial resolution and the variables needed for bias‑adjusted and downscaled products. This should make it easier to build robust climate services, local risk assessments and asset‑level analyses on top of the same data. Compared with CMIP6, CMIP7 aims to reduce methodological fragmentation by standardising variables and by explicitly connecting Earth system outputs to real‑world decision needs, including those of regulators and financial institutions. In practice, that means better hazard inputs for heat, drought, flood and wind risk models, and in addition more comparable products across providers. In general, we should see a smoother pipeline from global climate scenarios to portfolio‑ and asset‑level insights. CMIP7 remains a complex, protocol‑based global community effort, but with a much stronger focus on decision‑relevant variables for sectors such as energy, agriculture, water, ecosystems, cities, health and finance. For anyone interested in how this is being set up, this paper is an excellent overview of the initiative and its priorities: https://lnkd.in/evjrpxYz

🌍 30 Free Geospatial Datasets Every GIS & GeoAI Professional Should Know If you’re working with ArcGIS, QGIS, Python, Deep Learning, or GeoAI, having the right datasets can make or break your project. Here’s a curated list of 30 free sources that I use regularly for flood mapping, land cover analysis, carbon estimation, and climate applications: Satellite Imagery: 1. Sentinel-1 SAR (Copernicus) 2. Sentinel-2 MSI (Copernicus) 3. Landsat 8 & 9 (USGS) 4. MODIS (NASA) 5. PlanetScope (Free Trial / Education) 6. VIIRS Nighttime Lights (NASA) Elevation & Topography:
7. SRTM DEM (NASA)
8. ASTER GDEM
9. ALOS World 3D
10. USGS National Elevation Dataset Land Cover & Vegetation:
11. ESA WorldCover
12. Copernicus Global Land Service
13. GlobeLand30
14. NLCD (US Land Cover)
15. CORINE Land Cover (EU) Hydrology & Water:
16. HydroSHEDS
17. GRWL River Network
18. Global Lakes and Wetlands Database (GLWD)
19. OpenStreetMap Water Layers
20. FAO Aquastat Climate & Environmental:
21. ERA5 Reanalysis Data (ECMWF)
22. CHIRPS Precipitation
23. WorldClim (Climate Layers)
24. Copernicus Climate Data Store
25. NASA POWER Data Socio-Economic & Infrastructure:
26. OpenStreetMap (OSM)
27. WorldPop (Population)
28. Global Human Settlement Layer (GHSL)
29. Natural Earth
30. UNEP Environmental Data ✅ These datasets are perfect for ArcGIS, QGIS, Google Earth Engine, Python, and Deep Learning workflows. Whether you’re doing flood modeling, forest change detection, or carbon mapping, these free sources can accelerate your project. 💡 Tip: Always combine multiple datasets to improve accuracy and generate robust GeoAI & machine learning models. Which dataset do you use the most in your projects? Let’s exchange tips! #GIS #RemoteSensing #GeoAI #DeepLearning #ArcGIS #QGIS #GoogleEarthEngine #EnvironmentalAnalytics #ClimateTech #OpenData #EarthObservation #SatelliteData #MachineLearning #FloodMapping #LandCover #CarbonMapping #SustainableDevelopment

🌎 Top 10 GIS Data Used in Climate Change Analysis Climate change is not just an environmental issue — it is a spatial data challenge. Accurate climate modeling depends on reliable geospatial datasets collected from satellites, ground stations, and global monitoring systems. Here are the key GIS datasets — along with their trusted global sources: 🌡️ 1️⃣ Land Surface Temperature (LST) Used for Urban Heat Island & heatwave risk analysis Data Sources: • NASA (Landsat, MODIS) • USGS 🌊 2️⃣ Sea Surface Temperature (SST) Used for cyclone studies & marine climate monitoring Data Sources: • NOAA • ESA 🏔️ 3️⃣ Digital Elevation Model (DEM) Used for flood modeling & sea-level rise simulation Data Sources: • USGS (SRTM) • ISRO (CartoDEM) 🌧️ 4️⃣ Precipitation Data Used for extreme rainfall & drought assessment Data Sources: • NOAA • IMD 🌱 5️⃣ Vegetation Indices (NDVI, EVI) Used for biomass & carbon sequestration studies Data Sources: • NASA (MODIS) • ESA (Sentinel-2) 🌍 6️⃣ Land Use / Land Cover (LULC) Used for deforestation & urban expansion analysis Data Sources: • USGS • ESA 🌾 7️⃣ Soil Moisture Data Used for drought & hydrological modeling Data Sources: • NASA (SMAP) • ESA ❄️ 8️⃣ Glacier & Snow Cover Data Used for melting trend analysis Data Sources: • NASA • NSIDC 🌫️ 9️⃣ Atmospheric Data (CO₂, NO₂, Aerosols) Used for greenhouse gas monitoring Data Sources: • ESA (Sentinel-5P) • NASA 🌊 🔟 Coastal & Shoreline Change Data Used for erosion & sea-level rise vulnerability mapping Data Sources: • USGS • NOAA Climate resilience begins with accurate geospatial intelligence. At SmartGIS Solutions, we believe data-driven spatial analysis is the foundation for sustainable planning and climate adaptation. hashtag #GIS hashtag #ClimateChange hashtag #RemoteSensing hashtag #SpatialAnalysis hashtag #GeospatialData hashtag #Sustainability hashtag #ClimateResilience hashtag #EarthObservation hashtag #SmartCities hashtag #Landsat hashtag #Sentinel hashtag #EnvironmentalMonitoring hashtag #SmartGISSolutions

Free Sources of Geospatial Climate Data I Actually Use in Research During my PhD in Water Resources Engineering, I realized good modeling starts with good data. Whether I was working on hydrological simulations, climate projections (CMIP6), or spatial analysis in the Upper Satluj River Basin — these platforms became part of my research workflow. If you’re working in GIS, hydrology, climate science, or environmental modeling, here are 10 powerful free data sources you should know: * Climate Models & Projections • NEX-GDDP-CMIP6 https://lnkd.in/g-zNkPPe • CMIP6 – Climate model simulations used in IPCC reports https://lnkd.in/gFnfZ95M • World Bank Climate Portal – Country-level climate risk insights https://lnkd.in/giriauwe * Remote Sensing & Satellite Data • Sentinel Hub – Sentinel-1 (SAR) & Sentinel-2 (optical) https://lnkd.in/gZ8pe5Eu • Google Earth Engine – Cloud-based geospatial processing https://lnkd.in/g68dyH5t • NASA Earthdata – MODIS, VIIRS, Landsat https://earthdata.nasa.gov • USGS Earth Explorer – Landsat & other satellite imagery https://lnkd.in/gs9eJvMf * Precipitation & Reanalysis • ERA5 (ECMWF) – Hourly reanalysis climate variables https://lnkd.in/gA4YVnSt • CHIRPS – High-resolution rainfall data https://lnkd.in/gAyPVFwV • Copernicus Climate Data Store – Climate projections + reanalysis https://lnkd.in/g29vXaPP Which data source do you use most in your work? #GIS #ClimateData #Hydrology #RemoteSensing #CMIP6 #WaterResources #PhDJourney #SpatialAnalysis