Ranjith Gopalakrishnan’s Post

15 FREE SATELLITE IMAGERY DATA SOURCES 1. USGS Earth Explorer Web: earthexplorer.usgs.gov Data Types: Landsat optical, declassified spy (e.g. CORONA) imagery, hyperspectral (e.g. Hyperion) 2. Sentinel / Copernicus Browser Web: https://lnkd.in/dZknHaYp Data Types: Sentinel-2 (optical multispectral), Sentinel-1 (SAR) 3. NASA Earthdata Search Web: search.earthdata.nasa.gov Data Types: Derived Earth science products, land cover, atmosphere, biosphere, cryosphere, etc. 4. NOAA Data Access Viewer Web: coast.noaa.gov (Data Access Viewer) Data Types: Satellite imagery, aerial imagery, LiDAR (especially for coastal regions) 5. Maxar Open Data Program Web: maxar.com (Open Data Program) Data Types: High-resolution optical (for disaster / emergency events), sample data including true color, stereo, building footprints, elevation 6. Geo-Airbus Defense Web: intelligence.airbus.com (sample images) Data Types: Optical (SPOT, Pleiades), Radar (TerraSAR-X), DEM (WorldDEM) 7. NASA Worldview Web: https://lnkd.in/dQ_AjabE Data Types: Scientific satellite product layers (hazards, climate, atmospheric, disaster) — can download associated data layers (GIS Geography) 8. NOAA CLASS (Comprehensive Large Array-data Stewardship System) Web: www.class.noaa.gov Data Types: Oceanic imagery, atmospheric imagery (e.g. GOES, POES), environmental/climate products (aerosols, ozone, etc.) 9. Bhuvan Indian Geo-Platform (ISRO) Web: bhuvan.nrsc.gov.in Data Types: Indian satellite imagery (IRS, Cartosat, ResourceSat, OceanSat), NDVI, elevation models (CartoDEM) 10. JAXA Global ALOS 3D World Web: eorc.jaxa.jp (ALOS portal) Data Types: Global DSM / elevation (30 m) via L-band radar (ALOS) 11. NOAA Digital Coast Web: https://lnkd.in/dSRG6r-Z Data Types: Coastal datasets including imagery, elevation, land cover, radar, infrared, true color composites, and socio-economic data for coastal zones 12. PROBA-V MEP (GeoViewer) Web: viewer.probav.vito.be Data Types: PROBA-V, SPOT-Vegetation, METOP — coarse resolution vegetation / global patterns 13. Satellite Land Cover (Global Land Cover Facility etc.) Web: (global land cover data sources via e.g. landsat, MODIS, AVHRR) Data Types: Global land cover / land use maps (vegetation, agriculture, urban, hydrology, etc.) 14. UNAVCO (SAR Archive / Geodesy Data) Web: unavco.org (SAR archive interface) Data Types: Synthetic Aperture Radar (SAR) data, geodesy / tectonic data, radar imaging for earth science applications 15. INPE (Brazil’s National Institute for Space Research) Web: dgi.inpe.br (INPE Image Catalog) Data Types: CBERS satellite imagery (optical), ResourceSat, UK-DMC, other joint mission data (especially for South America & Africa) Source: gisgeography.com If you find this post valuable, kindly consider reposting. #GIS #Geospatial #RemoteSensing #QGIS #ArcGIS #Landsat #Sentinel #NASA #NOAA #MODIS #GoogleEarthEngine #GEE #Geography #Mapping #Cartography #DataScience #DataViz

With more data comes great responsibility. In an age where decisions are driven by analytics, AI, and insights, the real challenge isn’t just collecting data — it’s using it wisely and ethically. As professionals, we’re entrusted with information that can shape strategies, influence behavior, and impact lives. That’s why responsible data use isn’t just a compliance checkbox — it’s a reflection of our integrity and respect for the people behind the numbers. - Protect privacy - Ensure accuracy - Be transparent about how data is used - Always consider the human impact Data can empower — but only if it’s handled with care. #DataEthics #AI #ResponsibleLeadership #Trust #Innovation #DataisKing

Geospatial Technologies essential keywords, daily Tips 🌎 : Keyword : Hyperspectral Imagery Category :Key Concepts in Remote Sensing **Hyperspectral Imagery** 🌐🔍 Hyperspectral imagery refers to the collection and processing of data from the visible, near-infrared, and short-wave infrared parts of the electromagnetic spectrum. 🌌 This type of imagery captures the reflected radiation from the Earth's surface at hundreds to thousands of narrow spectral bands, allowing for a higher spectral resolution than traditional multispectral or panchromatic imaging. 📈 By analyzing the spectral signatures of various materials, hyperspectral imagery enables the detection and identification of subtle changes in the environment, such as soil moisture, vegetation health, and mineral composition. The application of hyperspectral imagery has numerous benefits in various fields, including natural resource management, environmental monitoring, and military operations. 🚀 For instance, hyperspectral sensors can be used to monitor crop health, detect water pollution, or identify potential mineral deposits. 🌎 By leveraging the capabilities of hyperspectral imagery, geospatial professionals can gain a deeper understanding of the Earth's surface and make more informed decisions in their respective domains. #GIS #Geospatial #HyperspectralImagery #RemoteSensing #EarthObservation #SpatialAnalysis #TechGeoMapping #EssentialKeywords

Radiometric Resolution: The Power of Pixel Sensitivity! 🛰️✨ Ever wondered how satellites can tell the difference between light and “slightly lighter” areas on Earth? 👀 That’s the work of Radiometric Resolution! 🌍 Radiometric resolution refers to how finely a sensor can detect variations in energy (brightness or reflectance) coming from the Earth’s surface. 🎚️ Think of it like this: ✅A low radiometric resolution (8-bit) sensor can record 256 shades of brightness. ✅A high radiometric resolution (12-bit or 16-bit) sensor can record thousands of subtle shades, capturing finer details — like the difference between dry soil and slightly moist soil 🌾, or light vs. dense vegetation 🌳. In short, the higher the radiometric resolution, the more “sensitive” your satellite eyes become! 👁️🌈 #RemoteSensing #RadiometricResolution #SatelliteImagery #EarthObservation #GIS #GeospatialIntelligence #ClimateMonitoring #Geoinformatics

SNOW, RAIN, OR IN BETWEEN: Using nearly a decade of high-speed footage, an unsupervised machine learning approach distinguishes nine distinct precipitation types, including varieties of rain, snow and mixed-phases. The results could improve weather forecasting and winter driving safety. “In the short term, better forecasting can help people adjust their daily commute or prepare for big events like floods or an ice storm. On longer time scales, it can help predict how snowpack or runoff timing will change fresh water availability for a region,” said Claire Pettersen, an assistant professor of climate and space sciences and engineering at U-M. The footage used here came from a NASA-developed specialized camera system deployed at seven sites in the United States, Canada and Europe. The instrument uses a high-speed camera system and a bright light to record precipitation falling through the camera’s view while measuring particle sizes, size distribution, density, concentration and fall speeds. Read the full story here: https://bit.ly/3VUNpRe

🌍 Understanding the Ellipsoid and WGS84 . Have you ever wondered how GPS and mapping systems define the shape of the Earth? Here’s a simple breakdown: 1️⃣ The Earth is not a perfect sphere It’s slightly flattened at the poles and bulging at the equator. To model this, scientists use a mathematical shape called an ellipsoid. 2️⃣ What is an Ellipsoid? An ellipsoid is a smooth, regular surface that approximates the Earth’s shape. It’s defined by two main parameters: Semi-major axis (the equatorial radius) Flattening (the amount the poles are “squashed”) 3️⃣ Why do we need it? The ellipsoid provides a consistent mathematical model of the Earth. Without it, we couldn’t build accurate coordinate systems or global maps. 4️⃣ Enter WGS84 (World Geodetic System 1984) WGS84 is the global reference system used today. It defines: The ellipsoid parameters (size and shape of the Earth) The reference frame that ties satellite positions to real-world coordinates Simply put: WGS84 = Ellipsoid + Reference Frame 5️⃣ Why is WGS84 important? It’s the foundation of GPS technology. Used in navigation, mapping, surveying, aviation, and many engineering applications. Every time you open Google Maps or use GPS, you’re relying on WGS84. 💡 In summary: The ellipsoid is the mathematical model. WGS84 is the global standard that applies this model for real-world positioning. #Geodesy #WGS84 #Ellipsoid #GIS #Surveying #Mapping #GPS

Understanding Spectral Resolution in Remote Sensing! 🛰️📸 Ever wondered how satellites can tell the difference between a forest, a lake, and a city road — all from space? 🤔 That’s the magic of Spectral Resolution! 🌍 Spectral resolution refers to how finely a sensor can distinguish between different wavelengths of light. 👉 A high spectral resolution sensor captures many narrow wavelength bands — perfect for detecting subtle differences like healthy vs. stressed crops 🌾 or clean vs. polluted water 💧. 👉 A low spectral resolution sensor captures fewer, broader bands — great for general land cover mapping 🗺️. In short, the higher the spectral resolution, the more color details the sensor “sees” — just like upgrading from a black-and-white TV to ultra-HD color! 🎨📺 #RemoteSensing #SpectralResolution #EarthObservation #GIS #SpaceTechnology #Geoinformatics #SatelliteImagery

Many GeoAI tasks lack high-quality labels (e.g. territorial boundaries, crater detection). New research is now bringing weak supervision + spatial priors (Tobler’s law, point pattern stats, graph encoding) into GeoAI. These hybrid models let us train with fewer labels but still respect spatial structure. That’s how we’ll scale GeoAI from niche labs to planetary scale. GeoAI from niche labs to planetary scale. #geoai #AIForGood #AgenticAI #SpatialComputing #MapAPIs #APISecurity #GIS #innovation #FutureOfWork

Geospatial Technologies essential keywords, daily Tips 🌎 : Keyword : Atmospheric Correction Category :Key Concepts in Remote Sensing **Atmospheric Correction** 🌎 Atmospheric correction is a crucial step in remote sensing and GIS applications that accounts for the effects of the Earth's atmosphere on satellite or aerial sensor images. The atmosphere scatters and absorbs electromagnetic radiation, affecting the spectral characteristics of the recorded data. As a result, the apparent radiance of the target area is altered, leading to inaccuracies in spectral classification, band shifting, and misty effects. Atmospheric correction algorithms, such as MODTRAN, ATCOR, or FLAASH, are designed to remove these atmospheric effects by applying mathematical models of atmospheric transmission and scattering. Effective atmospheric correction enables more accurate extraction of meaningful information from remote sensing data, facilitating applications in crop monitoring, land cover analysis, climate modeling, and more. By removing the atmospheric interference, researchers and analysts can gain a clearer understanding of the underlying processes and phenomena, ultimately leading to better decision-making in various fields. With advancements in remote sensing technologies and computational power, atmospheric correction has become an essential tool in the geospatial industry. #AtmosphericCorrection #RemoteSensing #GIS #Geospatial #AtmosphericScience #SpectralAnalysis #ImageProcessing #EarthObservation #GeographicInformationSystem #TechGeoMapping #EssentialKeywords

🚀 How real is real? This morning we pulled fresh satellite data—and what you’re seeing isn’t CGI. It’s live, raw signals from space. Take a look: Humberto and Imelda, locked in a Fujiwhara-type interaction, where Humberto’s stronger circulation is tugging Imelda away from the U.S. coastline. Instead of landfall, Imelda is being redirected. Meanwhile, rainfall/flooding risk is shifting — South Carolina is still in the mix, but the forecasts are trending toward a lower chance of extreme inland flooding. This is exactly the kind of event students using Signal Hunters get to explore firsthand. They don’t just see satellite imagery — they capture, decode, and analyze it in real time, translating space signals into weather insight. 👉 Learn more about the Signal Hunters STEM program here: https://lnkd.in/eJQfY9mG In the comments I’ll drop full-resolution RGB and infrared images so you can inspect the detail yourself. Let’s make science live, not just taught. #SpaceKitz #SignalHunters #SatelliteScience #STEM #WeatherSatellites #Fujiwhara #NOAA #RealData