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Remotely Sensed Data in ArcGIS Online

Revised 17 May 2026

Remote sensing is "the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance from the targeted area" (USGS 2019).

While remote sensing is commonly used as a synonym for satellite data, the concept of remote sensing can also be applied to aerial photography or lidar from drones. The remote part of remote sensing means that you are gathering data from a distance.

Raster Data

Satellites almost always capture data as raster data. Raster data represent characteristics of areas on the earth as regular grids of rectangular pixels.

Raster data is often contrasted in GIS with vector data. Vector data stores locations as discrete geometric objects: points, lines or polygons.

Geographic phenomena can usually be represented using either vectors or rasters, but some types of phenomena are better suited to one or the other.

• Vector data is generally more useful for representing human creations that have clear boundaries, like built structures or political boundaries.
• Raster data is generally more useful for environmental characteristics that do not have clear or stable boundaries, like elevation or vegetation types.

Landsat

Landsat is a long-running space-based earth observation program run by NASA. If you work with remote sensing, you will probably use Landsat data on multiple occasions.

Landsat 1 was launched on 23 July 1972 and subsequent satellites have provided continuous satellite imagery of the Earth. Of the hundreds of remote sensing satellites launched in the past half century, the Landsat program has proved especially valuable for civilian earth science.

Landsat 8 (launched 11 February 2013) and Landsat 9 (launched 27 September 2021) are currently operational (NASA 2022).

Landsat scenes area available from the Landsat Level-2 imagery layer in ArcGIS Online.

Temporal Resolution

The temporal resolution of satellite data is how frequently data is available for any particular location on the surface of the earth. Some satellite systems return to the same location daily, while others that are designed to observe the entire earth may take days or weeks to return to the same location.

Each Landsat satellite covers the entire earth every 16 days, and since the two satellites (Landsat 8 and 9) are eight days out of phase, this gives Landsat data a temporal resolution of eight days. Each Landsat satellites completes just over 14 orbits a day in a near-polar, sun-synchronous orbit so they spend as much time as possible looking at areas of the earth lit by the sun.

Landsat data is publicly available as scene images that cover an area around 100 miles square. Scenes are designated by a path number (scanning path west to east) and row number (scene location on the path south to north).

• On the right toolbar, set the Appearance, Blending to Multiply so you can see the base map through the layer and know what area you are viewing.
• Zoom to the area you want to view (Urbana, IL)
• Select the Image Collection Explorer on the right toolbar
• Open the List settings dialog and display the acquisitiondate and cloudcover fields.
• Sort by acquisitiondate
• Filter to limit the range of dates to the desired time period (summer 2025)
• Find a scene with low cloud cover and Add to map with a meaningful title (Summer Scene).
• The row and path numbers are combined in the third set of six digits in the name. For example, with this Landsat 8 scene covering an area in Illinois and Indiana with the name LC08_L2SP_022032_20250610_20250618_02_T1, the path number is 22 and the row number is 32, captured on 10 June 2025.

Because Landsat satellites return to the same location over the earth every 16 days and Landsat 8 and 9 are eight days out of phase, a scene in the same path and row should be available eight days before your selected scene.

For example, scene LC09_L2SP_022032_20250602_20250603_02_T1 is the same path and row as the scene above but captured by Landsat 9 (LC09) eight days earlier (2 June 2025).

While temporal resolution of captured data is determined by the orbit, the temporal resolution of usable data can be affected by cloud cover. If an area is obscured by clouds when the satellite passes over, that data may be unusable and no data can be captured for that area until the satellite passes again when there are fewer clouds. This can be a significant problem when attempting to use Landsat data for areas that have cloudy climates where long successions of scenes may be partially or totally obscured.

For example, on the next Landsat 9 pass on 18 June 2025, 73% of the scene was obscured by clouds.

Spatial Resolution

The level of spatial resolution determines the level of detail that can be distinguished in a remotely sensed image. As you zoom in closer to the earth, the amount of information available diminishes and the images appear more fuzzy.

Landsat 8 and 9 spatial resolution is 30 meters for most bands, except for 15 meter resolution with panchromatic (grayscale) band 8 and 100 meter resolution with thermal infrared sensor bands 10 and 11 (USGS 2017).

Resampling is the technique used to calculate how to display raster pixels when the number of raster pixels is different from the number of computer pixels available for showing the raster pixels.

If you change the Resampling type to Nearest Neighbor, you can zoom in and see the individual square pixels.

Spectral Resolution

Remote sensing takes advantage of the emission and reflection of electromagnetic radiation by objects on the surface of the earth to capture what is where on the surface of the earth.

The spectral resolution of a remote sensing system is the range of electromagnetic radiation frequencies captured by the sensor. Satellite sensors capture ranges of frequencies in bands and the spectral resolution of a satellite sensor is usually specified by the numbers of different bands available and the ranges of frequencies covered by each band. The appropriate spectral resolution depends on the purpose of the satellite.

Landsat 8 and 9 capture data in 11 different bands, giving Landsat 8 and 9 a spectral resolution that includes visible light, near-infrared, short wave infrared, and thermal infrared bands (USGS 2017).

The diagram below shows the relationship of Landsat bands 1 - 9 to the visible light spectrum. Bands 10 and 11 captured by the Thermal Infrared Sensor (TIRS) are much lower frequencies and are not shown.

By default, the Landsat Level-2 displays using Natural Color for Visualization with the the red, green, and blue bands (4, 3, and 2). However, you can combine and display different bands to observe different types of geological or biological phenomena.

For example, Landsat band 10 (Surface Temperature) can be used to detect urban heat islands.

• Duplicate your scene layer and give it a meaningful name (Surface Temperature).
• View the Processing templates and select Surface temperature Colorized (Fahrenheit) for Visualization.
• Note the hot spots in the heavily built-up Campustown area to the west of the University of Illinois campus, but the cool spot on the grassy Main Quad in the center of campus.

Normalized Difference Vegetation Index

Bands other than visible light are useful for analyzing a variety of phenomena.

Normalized difference vegetation index (NDVI) is based on a characteristic that photosynthetic green plants tend to reflect infrared light to avoid overheating, but absorb red light to power the process of photosynthesis.

The range of the index is negative one to positive one.

When near infrared is high and red is low, that is when living photosynthetic plants are reflecting infrared and absorbing red, making NDVI high and closer to one.

1 - 0
----- = 1
1 + 0

When near infrared is low and red is high, such as with bare ground or water, NDVI is low and closer to negative one.

0 - 1
----- = -1
0 + 1

The normalization specified in this formula provides NDVI values that are useful regardless of variations in the level of lighting or the angle of view. It also makes it possible to distinguish between photosynthetic vegetation and non-photosynthetic green areas (like artificial turf). Accordingly, this gives NDVI an advantage over simply looking at an RGB image for areas that appear green.

NDVI can be calculated using Landsat near infrared band 5 and the red band 4 to give values that are higher in areas with large amounts of vegetation, and lower in areas of low vegetation.

Because NDVI creates a set of values from -1 to +1 and has no color of its own, maps of NDVI are commonly visualized with false-color that assigns different colors to ranges of values and makes areas with high and low NDVI easier to distinguish.

• Duplicate your scene layer give it a meaningful name (NDVI).
• View the Processing templates and select NDVI Colorized for Visualization.
• Be aware that for visualization, this layer stretches the NDVI values from 0 (NDVI = -1) to 255 (NDVI = +1)
• Note that the low NDVI values in the heavily built areas of the campus and Campustown compared to the high NDVI values in the parks and heavily forested residential areas to the east of the campus.

Other Raster Layers Available in the Living Atlas

The ESRI Living Atlas has raster layers from a variety of other sources.

National Agricultural Imagery Program

The USDA National Agriculture Imagery Program (NAIP) is a collection of aerial imagery captured during growing seasons in the US. NAIP is the latest in a historical archive of imagery from the USDA Farm Service Agency (FSA) Aerial Photography Field Office (APFO) that provides a photographic history of the United States stretching back to the 1950s. NAIP imagery at 1 meter resolution from 2010 to 2023 is available through the USA NAIP Imagery: Natural Color Living Atlas layer.

As with Landsat data, NAIP data is available in scenes that can be selected using the image collection explorer.

Digital Elevation Models

Digital elevation models (DEM) are raster data sets containing rows and columns of pixels, where each pixel represents the elevations at a specific location on the surface of the earth. Elevation data is useful in a variety of fields like geology, hydrology, biology, civil engineering, urban planning, transportation, and disaster management.

Although DEM are often rendered in perspective views that give a three-dimensional appearance, DEM are considered to be 2.5-D rather than 3-D because they only have one possible elevation value for each lat/long pixel. True 3-D requires the ability to model multiple values at each lat/long, such as caves in hills or multiple floors of buildings.

Terrain Living Atlas layer provides access to elevation data from a variety of authoritative sources.

Because DEM do not have a color of their own, they are commonly visualized in ways that make the elevation changes clearer and give the visualizations more aesthetic appeal. The Elevation_Tinted_Hillshade processing template provides a false color visualization with hillshading.

While DEM commonly represent "bare earth" elevation without the buildings, indentations for water bodies or excavations are often visible. For example, the Boneyard Creek and flood management detention ponds around the University of Illinois campus are visible in the Terrain layer.

National Land Cover Database

The National Land Cover Database is maintained by a consortium of US federal government agencies with data for predominant categories of man-made and natural surface cover.

The USA Annual NLCD - Land Cover imagery layer from the Living Atlas is a time series of annual NLCD data.

The layer is time enabled so that you can scroll across years to observe changes in land use.

In this scene from Urbana, IL, you can see the sprawling of residential development into agricultural area southeast of town over the late 1990s and early 2000s.

ArcGIS Pro

While ArcGIS Online can be used to visualize raster layers, more complex software is needed to perform more complex visualization or analysis, such as hydrological analysis.

ArcGIS Pro is desktop geographic information system software for advanced mapping, analytics, and data management. The software runs only on Windows computers, and an installer is available in ArcGIS Online at no additional charge to members of the U of I community.