Satellite & Intelligence Glossary

The Watchfloor is the common operating picture for daily use: a Global Pulse of recent activity, the Delta Signals feed, active fires, and your watchlist of saved areas. Browsing is public and free; signing in unlocks "what changed since you last looked" and AI deep-dives via Delta Agent.

Delta Signals maps real-world events so analysts can overlay them on monitored areas and understand what is happening on the ground. Event signals are distilled from global news media and retain source attribution. Browsing the feed is free; deeper AI analysis is available through Delta Agent.

Ask a question in plain language and Delta Agent synthesizes relevant event signals into a cited intelligence brief with areas of interest you can task for monitoring — a tip-and-cue workflow from news to satellite. Available to any registered user; each question is metered by tokens.

For global events, a situational awareness dashboard maps geolocated incidents, surfaces what is new since your last visit, lets you look back over time, and links each signal to supporting evidence such as satellite imagery. The Delta Watchfloor is Off-Nadir Delta's situational awareness dashboard.

A common operating picture removes the gap between people looking at different sources by presenting one authoritative, current view. On the Delta Watchfloor, the COP fuses geolocated event signals, active fires, and your saved areas into one screen.

In Off-Nadir Delta, a geolocated event in Delta Signals can tip an analyst to cue continuous satellite monitoring over the affected area. This connects open-source event intelligence to satellite tasking, focusing limited observation resources on the locations that matter most.

Off-Nadir Delta's intelligence layer uses only public, open sources — geolocated event data distilled from global news media combined with open satellite imagery. Outputs cite their sources and are intended for situational awareness, not the tracking or targeting of individuals.

GDELT continuously processes news in many languages and extracts events, actors, locations, and tone. It is the open data source behind Delta Signals. The GDELT Project is credited wherever signal data is displayed in Off-Nadir Delta.

GEOINT fuses imagery intelligence (IMINT) with maps, terrain, and other geospatial data to answer where something is, what it is, and how it is changing. Delta Agent applies GEOINT tradecraft to open sources — interpreting events spatially and recommending which imagery would confirm them.

IMINT is a core input to GEOINT. Analysts read optical, SAR, and infrared imagery to detect, recognize, and identify features. The required image quality depends on the task: detecting that something exists needs far less detail than identifying exactly what it is.

All-source analysis cross-checks independent sources to raise confidence and expose gaps. Delta Agent works in an all-source style over open data: it reasons across event signals and imagery, cites its sources, and calibrates confidence rather than asserting a single feed as fact.

Good collection management matches the right sensor (optical vs SAR), resolution, and revisit cadence to the question being asked, under constraints like weather and cost. Tip-and-cue is part of it: a signal cues a targeted observation. Delta Agent recommends the sensor and area of interest to collect next.

The AOI scopes both analysis and collection. In Off-Nadir Delta you draw an AOI to monitor it over time as a time series, run change detection, or task imagery. Keeping the AOI tight focuses limited observation resources on what matters.

Establishing a pattern of life means measuring what is normal — typical vehicle counts, vessel traffic, vegetation, or nighttime light — so that departures become detectable. Off-Nadir Delta builds a baseline from a time series and flags anomalies using a ±2σ statistical threshold.

Satellites support MDA by revealing vessels even when they are not broadcasting their position. Ships appear as bright returns in SAR imagery, which works day or night and through cloud, so SAR vessel detection can be paired with AIS to find "dark" ships. Off-Nadir Delta detects vessels in Sentinel-1 SAR.

Higher NIIRS ratings mean finer detail and more demanding interpretation tasks become possible. NIIRS helps decide whether free ~10 m imagery is enough (regional change) or whether sub-meter commercial imagery is required (identifying individual vehicles or aircraft) — the difference between detecting, recognizing, and identifying an object.

SAR works by transmitting microwave pulses and measuring the reflected signal. Unlike optical sensors, SAR can operate day or night and penetrate clouds, smoke, and light rain. The "synthetic aperture" technique combines multiple radar pulses to achieve high spatial resolution. Sentinel-1 uses C-band SAR at 5.405 GHz (5.6 cm wavelength).

InSAR uses the phase difference between SAR acquisitions to detect millimeter-scale ground movements. Applications include monitoring earthquakes, volcanic activity, landslides, subsidence, and glacier flow. Differential InSAR (DInSAR) removes topographic effects to isolate surface displacement. Sentinel-1's 6-day repeat cycle enables regular InSAR monitoring.

Copernicus is the world's largest single Earth observation programme. It includes the Sentinel satellite family: Sentinel-1 (SAR), Sentinel-2 (optical), Sentinel-3 (ocean/land), Sentinel-5P (atmosphere), and upcoming Sentinel-6 (altimetry). All Copernicus data is free and open under the Copernicus Open Access policy, enabling commercial and non-commercial use with attribution.

Multispectral sensors capture light in specific wavelength ranges (bands). Sentinel-2 has 13 spectral bands from visible (blue, green, red) through near-infrared (NIR) to shortwave infrared (SWIR). Different bands reveal different surface properties: red/NIR for vegetation, SWIR for moisture, coastal aerosol for water quality. Combining bands enables vegetation indices and land cover classification.

GRD products have reduced speckle noise due to multi-looking (averaging multiple looks). The data is projected from slant range to ground range, making it easier to use for geographic applications. Available in VV and VH polarizations. Resolution is approximately 10m for Sentinel-1 IW mode.

RTC products provide more accurate backscatter values in mountainous or hilly terrain by accounting for local incidence angle variations. This correction is essential for quantitative analysis and time-series comparisons. Microsoft Planetary Computer provides Sentinel-1 RTC products processed with the Copernicus DEM.

C-band radar provides good balance between penetration capability and resolution. It can penetrate vegetation canopy partially and is sensitive to surface roughness and soil moisture. Compared to L-band (longer wavelength), C-band has less vegetation penetration but higher resolution.

L-band radar has a longer wavelength than C-band, enabling deeper penetration through vegetation canopy to reach tree trunks and the forest floor. This makes L-band well suited to forest biomass estimation, deforestation detection, and subsurface moisture mapping. Note: Off-Nadir Delta provides C-band SAR (Sentinel-1 and OPERA RTC-S1), not L-band.

The OPERA product available in Off-Nadir Delta is OPERA RTC-S1, derived from Sentinel-1 (C-band): Radiometrically Terrain Corrected (RTC) backscatter distributed as Cloud-Optimized GeoTIFFs via the Alaska Satellite Facility (ASF). RTC corrects terrain-induced radiometric distortions using a DEM, making backscatter values more comparable across slopes and improving quantitative and time-series analysis in hilly terrain.

IW mode uses the TOPSAR (Terrain Observation with Progressive Scans SAR) technique, capturing data in three sub-swaths with burst synchronization for interferometric applications. This is the standard mode used over most land surfaces.

VV (vertical-vertical): transmits vertically polarized waves and receives vertically polarized returns. Sensitive to surface roughness and soil moisture. VH (vertical-horizontal): transmits vertically and receives horizontally polarized returns (cross-polarization). Useful for vegetation analysis and flood mapping as water has low cross-polarization return.

Each band captures different surface properties. Common bands include: Blue (coastal/aerosol), Green (vegetation vigor), Red (chlorophyll absorption), NIR (vegetation structure), SWIR (moisture content). Sentinel-2 bands: B2 (Blue, 490nm), B3 (Green, 560nm), B4 (Red, 665nm), B8 (NIR, 842nm), B11/B12 (SWIR). Band combinations create different visualizations.

Landsat 8 (2013) and Landsat 9 (2021) carry the OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), capturing 11 bands at 30m resolution (15m panchromatic) with a 16-day revisit. The unbroken 50+ year archive makes Landsat the reference for long-term land cover, deforestation, and urban change studies. All data is free and open under the USGS open data policy.

HLS applies atmospheric correction, cloud masking, spectral bandpass adjustment, and a common grid (Sentinel-2 MGRS tiles) so that imagery from different sensors can be used interchangeably in a time series. The HLSL30 (Landsat) and HLSS30 (Sentinel-2) products together give far denser temporal coverage than either sensor alone — valuable for agriculture, phenology, and rapid change monitoring.

MODIS captures 36 spectral bands with a wide swath that images the entire Earth every 1-2 days. Its coarse resolution is offset by exceptional temporal frequency and a 20+ year archive, making it the backbone of operational monitoring: vegetation indices (MOD13), land surface temperature (MOD11), active fire (used by NASA FIRMS), snow cover, and aerosols. MODIS is being succeeded by VIIRS.

The VIIRS DNB measures low-light radiance in the 500-900nm range. NASA's Black Marble (VNP46) products calibrate and atmospherically correct DNB radiance for analysis. Nighttime light intensity is a widely used proxy for economic activity, electrification, population distribution, and power-outage detection after disasters.

The DNB has extremely high sensitivity, detecting radiances from full sunlight down to quarter moon illumination. NASA Black Marble products use DNB data to create nighttime lights composites, useful for monitoring urbanization, economic activity, and power outages.

VIIRS provides 22 spectral bands from visible to thermal infrared. For Off-Nadir Delta, we use the Day-Night Band (DNB) products from NASA Black Marble (VNP46A2) for nighttime lights analysis. Resolution is approximately 500m.

L2A products are processed from Level-1C (top-of-atmosphere reflectance) using the Sen2Cor algorithm. They provide orthorectified, atmospherically corrected surface reflectance values at 10m, 20m, and 60m resolution depending on the band. Off-Nadir Delta uses L2A products for all Sentinel-2 imagery.

COGs use internal tiling and overviews (pyramids) organized to allow HTTP range requests. This means you can request just the portion of the image you need without downloading the entire file. Essential for web-based satellite imagery viewing and analysis. Off-Nadir Delta streams COG data directly from cloud storage.

GeoJSON supports geometry types including Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon, and GeometryCollection. It is widely used for web mapping applications due to its simplicity and compatibility with JavaScript. Off-Nadir Delta uses GeoJSON for vector data export and footprint display.

WMTS serves map images as tiles (typically 256×256 pixels) organized by zoom level, row, and column. This tiled approach enables efficient caching and fast map display. Related protocols include WMS (Web Map Service) for on-demand rendering and XYZ tiles (used by OpenStreetMap, Google Maps). Off-Nadir Delta uses tile-based streaming for satellite imagery.

STAC provides a common language for describing satellite imagery metadata including spatial extent, temporal coverage, and available assets (bands, formats). Major providers like AWS Earth Search and Microsoft Planetary Computer use STAC APIs. Off-Nadir Delta queries STAC APIs to search and access satellite imagery.

GeoTIFF stores coordinate reference system (CRS), map projection, and georeferencing information within the TIFF file structure. This allows GIS software to correctly position the image on the Earth. Most satellite imagery is distributed in GeoTIFF or Cloud Optimized GeoTIFF format.

NDVI = (NIR - Red) / (NIR + Red). Values range from -1 to +1. Healthy vegetation typically shows values of 0.3-0.8 due to high NIR reflectance and low red absorption. Water bodies and bare soil typically show values near 0 or negative. NDVI is widely used for agriculture monitoring, drought assessment, and land cover classification.

EVI uses blue band information to correct for atmospheric aerosols and is less sensitive to soil brightness variations. It performs better than NDVI in high-biomass regions where NDVI tends to saturate. Formula: EVI = 2.5 × (NIR - Red) / (NIR + 6×Red - 7.5×Blue + 1).

Multiple NDWI formulations exist. McFeeters NDWI uses (Green - NIR) / (Green + NIR) for water body detection. Gao NDWI uses (NIR - SWIR) / (NIR + SWIR) for vegetation water content. Both help distinguish water from land and monitor drought conditions.

Nadir means "directly below" - a nadir view looks straight down. Off-nadir imaging tilts the sensor to view targets at an angle, enabling more frequent revisits, stereo imaging for 3D models, and better views of building facades. However, off-nadir images have more geometric distortion and atmospheric path length. The name "Off-Nadir Delta" reflects the value of viewing Earth from different perspectives.

Sensors are complementary: optical data shows vegetation and color but is blocked by clouds, while SAR penetrates clouds but is harder to interpret. Fusion approaches range from simple gap-filling (using SAR when optical is cloudy) to harmonized datasets like HLS, to machine-learning models that ingest multiple data streams. The result is denser, more reliable time series for monitoring.

Backscatter intensity depends on surface roughness, dielectric properties (moisture content), and local geometry (slope facing toward/away from sensor). Smooth surfaces like calm water produce low backscatter (specular reflection away from sensor), while rough surfaces and urban structures produce high backscatter.

Raw satellite images have distortions from sensor tilt, terrain relief, and Earth curvature. Orthorectification uses a Digital Elevation Model (DEM) and sensor geometry to correct these distortions, producing images where features are in their true geographic positions. Essential for accurate measurements and GIS overlay. Sentinel-2 L2A products are orthorectified.

DEMs represent the bare earth surface (without vegetation or buildings). Related products include DSM (Digital Surface Model, including buildings/trees) and DTM (Digital Terrain Model). Common sources include SRTM (30m global), Copernicus DEM (30m global), and ALOS World 3D (30m). DEMs are essential for RTC processing and orthorectification.

True color (natural color) composites use visible bands: Red→R, Green→G, Blue→B. For Sentinel-2: B4→R, B3→G, B2→B. The result looks similar to what human eyes would see. However, some features are better visualized using false color composites that include non-visible bands like near-infrared.

Common false color combinations: NIR-Red-Green (vegetation appears red, useful for agriculture), SWIR-NIR-Red (distinguishes vegetation types and moisture), Urban (SWIR-SWIR-Red for built-up areas). For Sentinel-2 vegetation: B8→R, B4→G, B3→B shows healthy vegetation as bright red.

Surface reflectance (Bottom of Atmosphere, BOA) represents the true reflectivity of the Earth's surface after atmospheric correction. Top of Atmosphere (TOA) reflectance includes atmospheric effects. Sentinel-2 L2A products provide surface reflectance values. Reflectance values are used to calculate vegetation indices and perform quantitative analysis.

Higher resolution (smaller pixel size) reveals more detail but covers less area. Sentinel-2: 10m (visible/NIR), 20m (red-edge/SWIR), 60m (atmospheric). Sentinel-1: 10m (IW mode). VIIRS: ~500m. Resolution trade-offs affect revisit time, data volume, and processing requirements. "Resolution" also refers to spectral (number of bands), temporal (revisit frequency), and radiometric (bit depth) properties.

Clouds obstruct the view of Earth's surface in optical imagery. Cloud masks classify each pixel as clear, cloudy, cloud shadow, or other categories. Sentinel-2 includes the Scene Classification Layer (SCL) with cloud/shadow detection. Cloud masks are essential for time-series analysis and composite generation. SAR imagery is not affected by clouds.

Footprints show the spatial extent of available imagery. Off-Nadir Delta displays search result footprints on the map, allowing you to see coverage and select specific scenes. Footprints may overlap, and the same location may have multiple images from different dates or orbit passes.

Also called a "granule" or "tile" depending on the data provider. Sentinel-2 scenes are organized into 100×100 km tiles in the Military Grid Reference System (MGRS). Each scene has associated metadata including acquisition time, cloud cover percentage, and processing level. Multiple scenes are often mosaicked for large-area analysis.

Speckle appears as a salt-and-pepper pattern and can obscure fine details. Multi-looking (averaging multiple observations) reduces speckle but decreases resolution. GRD products are multi-looked for reduced speckle. Speckle filters can be applied for further noise reduction.

Sentinel-1 constellation has a 6-day exact repeat cycle. Sentinel-2 has a 5-day revisit at the equator (with both satellites). VIIRS provides daily coverage. Shorter revisit times enable more frequent monitoring but may reduce spatial resolution.

Sentinel-1 IW mode has a 250 km swath width. Sentinel-2 has a 290 km swath. Wider swaths provide more coverage per orbit but may have lower resolution at swath edges. Trade-off exists between swath width and spatial resolution.

Sentinel-2 Level-2A products are atmospherically corrected, providing surface reflectance values instead of top-of-atmosphere radiance. This correction is essential for quantitative analysis and time-series comparisons. SAR is less affected by atmosphere due to microwave wavelength.