Connecting field data to climate science

Most discussions around #forest carbon still frame it as a choice between field data or #LiDAR. And I feel like this comes up all the time in conversations. But I was reading a study from Imaflora recently that made me pause and look at this differently. Instead of comparing one versus the other, they looked at what happens when you actually combine airborne LiDAR with field data and the results are quite striking. In their case: – Variance reduced by up to 73.8% – Sampling efficiency increased by 3.8x – Error reduced from 39.5% to 12.9% – And the approach was also associated with major cost reductions compared to conventional inventory methods So this is not just about making things faster or cheaper. It’s about improving the quality of the estimates in a very meaningful way. For a long time, forest #carbon estimation has relied heavily on field inventories — robust, but difficult to scale. LiDAR has helped change that. It gives a much more direct way to capture forest structure (height, canopy, vertical complexity), which is essential for estimating #biomass and carbon. In many ways, LiDAR is helping solve the “structure problem”. But it also highlights something important: LiDAR is still spatially limited and often represents a snapshot in time. And if we want monitoring systems that are not only accurate, but also scalable, continuous, and transparent, especially for mechanisms like #Article6 or jurisdictional REDD+, we need to go one step further. Not replacing field data. Not replacing LiDAR. But integrating them. → Field data for calibration → LiDAR for structural accuracy → Satellite time series for scale and monitoring over time This is where things start to shift. The transition is not from field → remote sensing. It’s from sampling → spatially explicit, continuous monitoring. Curious to see more work in this space, especially approaches that combine these data sources in a way that can support real-world implementation at national and jurisdictional levels. And congrats to the authors (Natali Vilas Boas Silveira, Gabriela Celina Paes, Humberto Menecheli Filho, Lara Aranha da Costa) and institutions involved (Imaflora Institute for Climate and Society Instituto Socioambiental Forlidar), really valuable work!!! #Forests #Climate #RemoteSensing #NatureBasedSolutions #EarthObservation #JREDD+ #UNFCCC

ERA5-Land 2m Temperature Analysis for South Asia (2024) I have applied machine learning and geospatial analysis techniques to explore monthly 2-meter air temperature trends across South Asia in 2024 using ECMWF’s ERA5-Land reanalysis data. Data & Tools: 1. Source: ERA5-Land monthly aggregated temperature data (2m above ground) 2. Platform: Google Earth Engine 3. Language: Python with geemap and Cartopy for spatial processing and visualization Key Findings: 1. Seasonal Temperature Variation: Clear month-by-month temperature variation, with cold winters in the Himalayan region and intense heat peaks during summer across the Indian subcontinent. 2. Spatial Insights: Temperature gradients reflect diverse climate zones, critical for environmental and climate impact studies. Methodology: 1. Extracted temperature data for each month in 2024. 2. Converted Kelvin to Celsius for meaningful interpretation. 3. Generated detailed spatial maps using Python visualization libraries with Cartopy projections. 4. Created a comprehensive multi-panel figure showcasing monthly variations for easy comparison. Significance: This analysis demonstrates how integrating open climate data with cloud-based geospatial tools and machine learning enables high-quality, reproducible climate monitoring. These insights can support regional planning, agriculture, disaster preparedness, and climate resilience initiatives. #ClimateScience #DataScience #GeospatialAnalysis #MachineLearning #GoogleEarthEngine #ClimateChange #uthAsia

With over 10 years of experience in hydrology and climatology, I’ve built a toolkit of free remote sensing data platforms that every student and researcher should know about. These sources are reliable, globally comprehensive, and widely used in water resources, snow/ice monitoring, land cover change, drought/flood assessment, and climate trend analysis. Whether you’re modeling river flows, mapping snow cover, or assessing climate impacts, these datasets will boost your research with minimal cost. 1. NASA Earthdata (LAADS & more): https://lnkd.in/dqjMGG63 Datasets: MODIS, VIIRS, ASTER, cloud products, land surface temperature, albedo 2. USGS EarthExplorer: https://lnkd.in/dMBMVc92 Datasets: Landsat (1972–present), SRTM, DEMs, USGS aerial imagery 3. Copernicus Open Access Hub: https://lnkd.in/du3jTmTx Datasets: Sentinel-1 (SAR), Sentinel-2 (optical), Sentinel-3 (ocean/land) 4. Google Earth Engine (GEE): https://lnkd.in/dRqSBubq Datasets: Global catalog (Landsat, Sentinel, climate reanalysis, socioeconomic) 5. OpenTopography: https://lnkd.in/dfAhtyer Datasets: High-resolution LiDAR, DEMs, SRTM derivatives 6. NOAA Climate Data (NCEI): https://www.ncei.noaa.gov/ Datasets: Global climate normals, precipitation, temperature, reanalysis (e.g., NCEP/NCAR) 7. ESA Climate Change Initiative (CCI): http://cci.esa.int/data Datasets: Sea level, soil moisture, glaciers, land cover, fire, albedo 8. GLDAS / NASA GMAO Land Data: https://lnkd.in/diVuMcPY Datasets: Global Land Data Assimilation System (hydrologic states & fluxes) 9. CHIRPS Precipitation: https://lnkd.in/ddJkkBFs Datasets: High-resolution rainfall (1981–present) 10. SMAP & SMOS Soil Moisture SMAP: https://smap.jpl.nasa.gov/ SMOS: https://lnkd.in/d33mDhYp Datasets: Surface soil moisture, freeze/thaw states hashtag #RemoteSensing hashtag #Hydrology hashtag #ClimateChange hashtag #EarthObservation hashtag #OpenData hashtag #WaterResources hashtag #ClimateResearch hashtag #GIS hashtag #SatelliteData hashtag #Climate hashtag #Climateaction hashtag #EarlyCareerResearchers hashtag #EnvironmentalScience hashtag #SustainableDevelopment

Game-changer for Geospatial AI & Climate Science Google DeepMind just introduced AlphaEarth Foundations — a “virtual satellite” that fuses petabytes of Earth-observation data into a unified planetary embedding at ~10 m resolution, now available in Google Earth Engine. Huge step forward for mapping, monitoring, and actionable climate insights. So what? Treat AlphaEarth as your canonical geospatial feature store, then fuse it with near-real-time streams to move from maps ➜ decisions: Fuse with near-real-time data • Aerial & drone imagery for sub-10 cm change detection (post-event damage, coastal erosion, illegal mining). • SAR/Optical satellite nowcasts for all-weather flood, landslide, wildfire front tracking. • Geosensing/IoT (river gauges, air-quality, flux towers, soil moisture, tide gauges) for ground truth & model calibration. • Numerical forecasts (weather, waves, wildfire spread) to turn “what’s happening” into “what happens next.” Pipeline pattern Base layer: AlphaEarth annual embeddings (2017–2024) as consistent, compact features. Streaming layer: Real-time imagery + sensor feeds (Kafka/Kinesis). Reasoning & prediction: Spatiotemporal models + LLM agents for triage/explanations. Ops & action: Geofenced alerts, playbooks, and digital twins feeding emergency response, insurers, utilities, and cities. Impact examples • Wildfire: early-warning + resource allocation. • Floods: road passability & cross-border impact in hours, not weeks. • Agriculture: field-level yield & water stress forecasting. • Biodiversity: continuous habitat change & restoration ROI. • Urban heat: micro-zone cooling plans and grid load shaping. If you’re building climate decision systems, this is a foundational layer worth piloting. https://lnkd.in/e5zgYpTT #GeospatialAI #EarthObservation #ClimateTech #DigitalTwins #RemoteSensing #AIforGood #Sustainability #DisasterResponse #Insurance #ESG

🌦️ How GIS Supports Climatology Geographic Information Systems (GIS) play a key role in understanding and addressing climate-related challenges. By combining spatial data with environmental models, GIS helps scientists and planners visualize, analyze, and predict climate patterns more accurately. Here’s how GIS contributes to climatology: 1️⃣ Data Integration – combines data from satellites, weather stations, and remote sensors to build complete climate maps. 2️⃣ Trend Analysis – identifies temperature changes, rainfall patterns, and extreme weather over time. 3️⃣ Impact Assessment – evaluates how climate affects land use, water resources, biodiversity, and urban areas. 4️⃣ Risk Mapping – pinpoints areas vulnerable to floods, droughts, heatwaves, or sea-level rise. 5️⃣ Adaptation Planning – supports sustainable urban design, disaster preparedness, and environmental protection strategies. In short, GIS turns raw environmental data into actionable climate intelligence, helping us plan for a safer and more resilient future. 🌍 #GIS #Climatology #ClimateChange #DataScience #SpatialAnalysis #Sustainability #UrbanPlanning #QGIS #EnvironmentalData #Resilience

The Data Challenge in Grassland Carbon Accounting  Grasslands store approximately one-third of terrestrial carbon. Yet measuring that carbon accurately remains one of the hardest challenges in climate science. Why? Because grassland carbon exists in two forms: - Above-ground biomass (grass, plants) - can be remotely measured, but grass biomass is difficult to quantify. - Below-ground biomass (roots, soil organic matter) - extremely difficult to measure at scale, requiring extensive field sampling Most carbon accounting protocols rely heavily on models and assumptions for below-ground carbon and the relationship to above-ground biomass turnover, introducing significant uncertainty. This is particularly problematic for nature-based solution projects seeking high-integrity carbon credits. At Proveye, we focus on what remote sensing can measure reliably: above-ground biomass. We use multi-platform satellite data integrated with weather, landuse, soil type, and ground-truth validation to model biomass quantity at the field level. For below-ground carbon, we're transparent about the limitations. We use established scientific models (like those from IPCC guidelines) but clearly differentiate between measured data and modelled estimates. The emergence of foundation models and AI has been an exciting development over the last few years that promises to increase the level of accuracy that can be achieved. This matters because carbon market credibility depends on scientific rigour. Over-claiming accuracy undermines trust. Being clear about what we can measure precisely versus what requires modelling builds confidence with project developers, investors, and auditors. The future of grassland carbon accounting will likely involve combining AI, remote sensing (for above-ground measurement), with strategic field sampling (for below-ground validation). That's the approach that delivers both scalability and scientific credibility. For others working in this space—how are you approaching the above-ground/below-ground measurement challenge? #CarbonAccounting #Grasslands #RemoteSensing #ClimateScience