A definitive guide to buying and using satellite imagery

Editor's note: this article was originally published on January 15th 2021 by Nikita Marwaha Kraetzig. It was updated on October 28th 2026 by Kevin Enright.

So, you’ve decided to buy and use satellite imagery, but aren’t sure where to start? It's okay. Every single day, satellites capture trillions of pixels of our planet. Sorting through it all can be challenging.

What types of satellite imagery are out there? What are the different kinds of orbits? Where can you find imagery and how do you analyze it? Read on to find out.

This is the definitive guide to satellite imagery: what it is, how to buy it, and all the ways in which you can use it.

At a glance

• Two sensor types: Satellite imagery comes from passive sensors (optical: multispectral, hyperspectral, thermal), which rely on sunlight or naturally emitted heat and usually can’t see through clouds, or active sensors (SAR and lidar), which supply their own energy and work day or night. SAR can also penetrate cloud cover.
• Resolution classes: UP42 classifies spatial resolution by ground sample distance (GSD): very high resolution is ≤50 cm, high resolution >50 cm–2 m, medium >2 m–20 m, and low >20 m.
• Free data: Free, low- and medium-resolution data from Copernicus Sentinel-2 (10 m) and NASA/USGS Landsat (30 m, recording since 1972) offers global coverage, long archives and rich spectral bands. It can’t be tasked and usually can't show individual objects like trees or cars.
• Commercial very high resolution: Imagery from providers such as Vantor (formerly Maxar), Airbus (Pléiades Neo) and Planet reaches 30 cm, or 15 cm as HD products. That’s enough to identify cars, houses, and trees, but it's more expensive.
• Orbits: Most Earth observation satellites fly in sun-synchronous low Earth orbit at about 700 km and 98° inclination, so they pass over each location at the same local time for consistent comparisons. Geostationary satellites sit about 36,000 km above the equator and are mainly used for weather and telecoms.
• Revisit rate: The revisit rate is how often a satellite can image the same point. It depends on orbit, swath width, latitude, agility and constellation size. For example, the Pléiades constellation revisits every 1–2 days, and constellations like BlackSky can image the same site several times per day.
• Tasking vs. archive: While archive data means data that's already been captured, tasking commissions new imagery of a specific area and date range. It costs more than archive imagery and has larger minimum orders, but it delivers current data with set specifications such as maximum cloud cover.
• Price factors: Commercial imagery prices depend on area (above a minimum charge), resolution, archive vs. tasking, recency, cloud cover, incidence angle, priority, acquisition mode, bands, processing level, delivery time, and licensing terms.
• Buying through UP42: UP42 offers optical, SAR, hyperspectral, thermal, methane, aerial, drone, lidar and elevation data from dozens of providers through one platform. Users get catalog search, upfront pricing and per-AOI ordering, with data delivered as cloud-optimized GeoTIFFs in STAC format through its cloud-native asset model (CNAM). Users can access the platform via the console, the API, the Python SDK or the ArcGIS Pro add-in.
• Analysis and use cases: Common analysis includes pre-processing (orthorectification, pansharpening, co-registration), spectral indices like NDVI and EVI, object detection, and change detection. Some example applications include climate and infrastructure monitoring, deforestation, precision agriculture, insurance, vegetation encroachment, and mining.

Earth observation satellites

Before we dive into buying and using satellite data, let’s talk about the basics.

The Earth is a complex system with a huge variety of geophysical processes, many of which are interrelated. Earth-orbiting satellites are among the best ways to understand these from both local and global scales.

Earth is surrounded by satellites, many of which carry sensitive instruments to observe the planet.

These Earth observation (EO) satellites circle our planet in space, collecting information and sending it down to the ground.

Operating alone or as part of a constellation, they provide us with a global perspective from above. They help us monitor and protect our environment, respond to humanitarian emergencies, enable sustainable development, and much more.

There's a wide range of Earth system properties that can be assessed using EO satellites, from vegetation health and ocean salinity to fire intensity and carbon monoxide levels.

Types of satellite imagery

The type of imagery a satellite generates is dependent on the image-capturing method it uses.

Earth observation satellites use remote sensing technologies to monitor and assess changes in Earth’s physical, biological, and chemical compositions.

Remote sensing means the acquisition of information from a distance. EO satellites can be divided into two main types based on how they capture imagery: passive and active.

Passive and active sensing

Passive (Optical)

Remote sensing systems that measure naturally available energy are called passive sensors.

Passive satellites detect and collect reflected radiation off the Earth’s surface and atmosphere, with sensors designed to detect electromagnetic emissions.

These emissions can be locally produced, such as thermal radiation in the infrared spectrum, or reflected sunlight in the visible spectrum.

The sun is a handy source of energy for remote sensing. Its energy is either reflected, as it is for visible wavelengths, or absorbed and then re-emitted for thermal infrared wavelengths.

That’s why passive imagery is usually dependent on the day-night cycle.

Note that, in general, passive sensors aren't able to ‘see’ through clouds, so passive EO satellites can be limited when observing areas with dense cloud cover.

But repeated acquisitions over the same area dramatically increase the chance of acquiring a cloud-free image. Optical EO satellites deliver images that are easy to interpret visually as well as through hundreds of processing algorithms, though they still need to undergo orthorectification, the process of removing distortion.

Active

On the other hand, active sensors provide their own source of energy for illumination.

The sensor emits a beam of radiation towards Earth and collects the signal that is reflected.

Compared to visible satellite imagery, which relies on sunlight and can only be acquired during daylight hours, active sensors can obtain measurement anytime, regardless of the time of day or season.

Active sensors can be used for studying wavelengths that are not sufficiently provided by the sun, such as microwaves, or to better control how a target is illuminated.

However, active sensors require the generation of more energy, compared to passive sensors such as optical sensors.

Examples of active sensors

Synthetic Aperture Radar (SAR)

Examples of active sensors are synthetic aperture radar (SAR) and lidar. SAR instruments transmit electromagnetic pulses towards the Earth’s surface, where they are then reflected or scattered by the surface features.

SAR antennas can detect and record the returned pulses. The intensity of the returned pulse and the time taken to arrive back at the antenna are used to generate SAR imagery.

Oil spill detection, ship detection, and ground deformation are good examples of things you can do with SAR. For more info on SAR, see our dedicated article.

Lidar

Lidar (Light Detection And Ranging) uses a laser radar to transmit a light pulse down towards the planet’s surface. A receiver with sensitive detectors then measures the backscattered or reflected light, building a point cloud.

Lidar’s advantage lies in its ability to capture data in a dense field. This accuracy allows lidar satellite systems to map forest canopies, including tree structure, and the underlying topography.

The technique can penetrate forest canopies and map topography, making it ideal for forestry applications such as tree growth and volume analysis and canopy height measurement.

What is the spatial resolution of satellite imagery?

Spatial resolution is one of the key parameters in satellite imagery and refers to the level of detail that can be retrieved from a scene.

It is the size of one pixel on the ground and can be measured in several ways.

One of the most common is the Ground Sample Distance (GSD), the distance between center points of each pixel. GSD is a measure of how big each pixel is on the ground.

The lower this number, the finer the detail.

Generally, we divide resolution into the following categories:

• VHR (very high resolution): ≤ 50 cm
• HR (high resolution): from > 50 cm to ≤ 2 m
• MR (medium resolution): from > 2 m to ≤ 20 m
• LR (low resolution): > 20 m

For example, Landsat data has a 30 m resolution. This means that each pixel represents a 30 m x 30 m area on the ground. So it's considered a low-resolution image, which can image an entire city without a level of detail that is fine enough to distinguish specific objects like cars.

To help you determine which option covers your needs, let’s look at the advantages and disadvantages of low and medium resolution, and how they compare to high and very high resolution satellite imagery. Or for a deeper dive into this topic, check out our dedicated blog post.

Low and medium resolution satellite data

Advantages

• A vast wealth of freely available, historical data with global coverage at no charge

Satellite imagery sources such as ESA’s Sentinel data and NASA/USGS Landsat data both provide historical data, and are accessible for free through UP42.  They offer various spectral bands for you to use in a wide range of applications, from crop monitoring and forestry to regional planning and fire detection.

• A variety of spectral bands for remote sensing analysis

The valuable spectral information contained within each low and medium-resolution satellite image is a significant advantage of using this data. As a result, you can calculate combinations of these bands and spectral indexes, revealing previously unseen information about features or objects.

Use spectral bands in low and medium-resolution imagery to do image pre-processing (pansharpening, atmospheric corrections), and processing (land cover classification, land use).

Having so many spectral analytical opportunities, low and medium resolution, although not high resolution, has significant benefits if you are looking to understand the bigger picture.

Disadvantage

• A low level of detail with only large features visible

As mentioned above, although low and medium-resolution satellite data has significant advantages, this comes with the tradeoff that is image quality.

If you are looking to spot general features such as bridges, street patterns, or land use, this kind of data is perfect. But, you won’t be able to distinguish individual objects such as cars, houses, or trees.

Use low and medium resolution imagery to make the most of its spectral value and if you are interested in understanding larger-scale features over time.

High and very high resolution satellite data

Advantages

• Tasking capabilities

With commercial high resolution satellites, it is possible to pinpoint precisely where and when you are looking to image. Known as satellite tasking, this allows you an agile ability to task satellites to capture the areas of interest (AOIs) you need when you need it most.

• Very high level of detail

The striking advantage of high resolution satellite imagery is that you can distinguish and identify small objects such as individual cars, houses, or trees. Leading commercial providers such as Vantor, Planet, and Airbus make extensive archives of the highest resolution commercially available.

With imagery collected over the past two decades, it is possible to find high resolution imagery for almost any place on Earth.

Today’s sharpest commercial satellites, such as Airbus’s Pléiades Neo and Vantor’s WorldView Legion, collect imagery at 30 cm.

Such high spatial resolution is ideal for:

• Detailed mapping
• 3D city models
• Precision agriculture
• Forest inventory
• Soil erosion mapping
• Humanitarian aid
• Infrastructure monitoring
• Insurance indexes
• Urban planning
• Encroachment detection
• Maritime monitoring

Disadvantages

• High resolution has a higher price

One thing to keep in mind when using high and very high resolution imagery is that precise details and high resolution imagery are only possible with higher quality sensors and satellites. So this comes with a larger price tag.

• A smaller area is covered

Usually, the higher the resolution, the less total ground area can be seen in an image. That is good news if you are looking to do small-scale monitoring or analysis.

Because a smaller area is covered, it is important to keep in mind that if you would like to image an area the size of London (1,572 sq km) then you would need four or more images from high resolution satellites such as Pléiades-1 (400 sq km).

On the other hand, a single Landsat-8 image can capture an area roughly the size of 22 Londons.

Orbits

Satellites are designed on the ground and launched into orbits that are tailored to their purpose.

Here are a few examples.

Low Earth orbit (LEO)

Most Earth observation satellites are in what’s known as a Low Earth Orbit, or LEO. This means that they orbit north to south or south to north as the Earth rotates below them.

In orbit, EO satellites image a swath—a variable width—below the spacecraft using their imaging instruments.

Looking for higher spatial resolution? You’ll need a satellite with a narrower swath width and a longer time between imaging instances.

Geostationary

Much further away from the Earth, geostationary satellites are in orbit around the Earth and sit just above the equator.

These orbits are generally used for telecom and weather satellites and not for high resolution EO satellites.

But, geostationary orbits are worth knowing about when looking at the kinds of orbits satellites travel in and if you are interested in weather data to complement your data investigations.

Geostationary orbits are positioned 36,000km away from the Earth, which is just far enough, so it rotates at the same angular rotational rate as the Earth itself.

This orbit was given the name geostationary because satellites appear to be stationary compared to the Earth itself.

The disadvantage of geostationary orbits is that because of their nature, you cannot image the entire planet with one geostationary satellite.

To tackle this, geostationary satellites are positioned in a ring around the Earth with multiple satellites to paint a global picture.

With any satellite in orbit, the satellite’s velocity is governed by the balance between the centripetal force—which keeps objects moving along a curved path—and the gravitational force.

So, in the case of geostationary satellites, by positioning them 36,000km away from the Earth, the satellite’s velocity means that it takes one day to go around its orbit. This is the same time that the orbit takes to make one revolution on its axis, so geostationary satellites appear to hang in the same position above the Earth continuously.

Sun-synchronous orbit (SSO)

Low Earth-orbiting satellites are usually placed in what is called a sun-synchronous orbit, or SSO.

This means that they pass over the Earth at the same local time each day—allowing easy comparison between images taken on different dates.

If illumination conditions were to change drastically, it would be difficult to tell whether changes in the imagery received are due to changes on the Earth’s surface or due to illumination conditions.

So, using a sun-synchronous orbit, we can identify changes in the Earth’s environment much more easily.

Satellites in SSO are useful for weather satellites as well as imaging due to their consistent lighting. To monitor changes across many days, weeks, months, or even years, comparing imagery at around the same time every day is crucial to uncovering changes.

That’s why scientists use SSO satellites to predict storms, monitor forest fires, or accumulate data on long-term challenges such as rising sea levels or deforestation.

SSO is a particular kind of polar orbit. This means that they usually travel past Earth from north to south, rather than from west to east, and pass roughly over Earth’s poles.

Coverage

Satellites can allow for truly global coverage even above the most remote locations. Coverage essentially refers to the amount of land that a satellite can image.

Swath width

Mentioned earlier, the width of the area viewed by the satellite is referred to as the swath width.

As a satellite orbits the Earth, the sensor “sees” a particular portion of the surface. This area is referred to as the swath.

Imaging swaths vary between tens and hundreds of kilometers wide.

Since the Earth rotates (from west to east) beneath satellites, the swath can cover a new area with each consecutive pass because of this movement.

In this way, the satellite’s orbit and the Earth’s rotation work together to allow complete coverage of the Earth’s surface once a satellite has completed one cycle of orbits.

Satellites with different swath widths have different temporal resolutions. This is the time between observations of the same point on Earth, also known as the revisit rate.

Let’s look at the revisit rate next.

Revisit rate

As well as covering the globe, satellites enable regular repeat observations. A satellite’s revisit rate, or revisit period, is the time elapsed between observations of the same point on Earth by a satellite.

Revisit rate depends on a satellite’s orbit, target location, and the swath width of the sensor.

Typically, large-swath satellites can revisit the same location more often. Most EO satellites are in the aforementioned low Earth polar orbit named SSO. This kind of orbit typically has an altitude of around 700 km and an inclination of 98˚—the orbit’s angle in relation to Earth’s equator.

Due to this high inclination, a satellite’s revisit time is longer for equatorial areas than for polar regions.

How do we define the revisit rate?

When it comes to defining the revisit rate or time, it entirely depends on the point of view.

For a satellite, its revisit time is the elapsed time before it traces its path over the exact point on the Earth’s surface again. From the point of view of an EO user looking to use satellite data (such as yourself), then revisit time is the length of time we need to wait for the satellite system to observe the same point on Earth.

We differentiate between these two points of view and definitions because of the “agility” feature that most EO satellites have. This is its ability to modify its *attitude* (its orientation in space), to observe scenes outside its ground track or ground trace—the path directly beneath the satellite.

Therefore, the ground trace is the projection of the satellite’s orbit onto Earth’s surface.

With revisit rate, it’s important to keep cloud cover in mind. Too many clouds can mean that you may encounter data gaps when looking for imagery.

How can we reduce the revisit rate?

In some cases, a short revisit rate or time makes all the difference. EO constellations can be used to reduce the time needed to wait for imagery acquisition over your chosen area.

By augmenting the number of satellites in orbit, we can lower the waiting time between observations of the same scene. An excellent example of that is the two Pléiades constellation satellites.

Orbiting on an SSO, they can acquire images in a +/- 30˚ corridor around their ground trace. This means that while the periodic cycle of their orbits is 26 days, the satellites’ agility and the phased orbit of the Pléiades constellation offer a two-day revisit for any point on Earth.

Using steerable sensors, a satellite-borne instrument can view an area before and after the satellite’s orbit passes over—resulting in a revisit time that is less than the orbit cycle time. This is known as satellite tasking. Steering satellites can also increase the incidence angle—the angle from the target point of view. It represents the angle between the ground normal and look direction from the satellite.

Why is the revisit rate important?

The revisit rate is a key consideration for several monitoring applications, especially when frequent imaging is required to monitor environmental disasters such as the extent of flooding or the spread of an oil spill.

Especially when events are unfolding on Earth, whether it be disaster relief, business intelligence, or energy-related, the revisit rate plays a crucial role in acquiring the images that are needed when they are needed.

Rapid revisit refers to the ability to make repeated image captures with short time intervals. Satellite constellations such as BlackSky can take multiple images per day of the same location anywhere on Earth.

Tasking a satellite

As mentioned earlier, satellite tasking provides you with the agile ability to task satellites to capture the areas of interest (AOIs) you need when you need it most.

This means that you can request and collect imagery on-demand in the case of time-sensitive projects that require close to real-time insights. As aforementioned, tasking means that you are guaranteed a specific area is captured during a particular date range and with any other preferred specifications you may have (within reason, and subject to weather).

UP42 tasking is available for you to commission a wide range of constellations, from optical sources like Pléiades Neo and radar sources like Umbra and ICEYE, easily—with priority tasking for urgent requests.

Requesting and collecting tasked imagery allows you to monitor recent changes across geographically distributed areas—revealing reliable insight for challenges such as disaster monitoring, maritime surveillance, and encroachment detection. Find out more in our dedicated tasking blog post.

Historical satellite imagery

For some use cases, such as land-use change analysis, you may want to look back several years in time for archive satellite imagery of a given area.

Something to consider: EO instruments of the past were not nearly to the standards we see in today’s satellites.

So, the further back in time you look, the lower the quality of the images may be.

Still, are many great satellites in operation today that are great candidates for looking into the past. We've also got a dedicated archive imagery blog post, if you'd like to find out more.

How does satellite imagery pricing work?

There are two types to consider when budgeting for satellite data: free or commercial data sources.

Free data sources

There are many types of free data sources available, primarily from governments or research institutions, but also distributed on many commercial platforms.

These data sources often come at lower resolutions and cannot be tasked.

However, free data sources offer significant advantages like more regular revisits, increasing the number of observations made of the same point on Earth.

Additionally, because of their broader availability and use, occasionally free data sources have more research-backed applications. On UP42, we offer open data from ESA's Sentinel-2. This collection contains archive optical imagery from 2017 in Europe and 2018 worldwide onwards, in up to 10 m resolution.

Some other free data sources include USGS/NASA's Landsat series, and NASA's MODIS.

These data sets are generally distributed as full scenes, meaning the entire swath or footprint is downloaded.

Commercial data sources

Commercial datasets are available from various providers and distributed from an even wider range of resellers at a range of prices.

Thus, it isn’t easy to generalize pricing formats since every provider will have their own guidelines.

No two providers are the same. But, most have a minimum AOI size that must be purchased, with options of various levels of pre-processing, some that are only performed for an additional fee.

When buying satellite imagery, you may pay per tile or square kilometer. Volume discounts are common, and prices can vary according to the following factors:

• Cloud coverage and incidence angle (only applies for tasking)
• "Freshness" or recency of acquisition (for archive data)
• Archive vs. Tasking
• Delivery time
• Subscription service vs. pay-per-use
• Pre-processing
• Bands (e.g panchromatic vs. full multispectral)
• Priority level

With commercial satellites, images can either be from archives or, if you are looking for up-to-date imagery, can be tasked on demand.

It’s important to note: Tasked data is more expensive than archive and usually requires a larger minimum area size.

However, it comes with the guarantee of the requested specific date range and any other preferred specifications (within reason).

Over the past three decades, commercially available satellite imagery has evolved into a mature and thriving industry.

Rich, crisp imagery that is global and cloud-free was once reserved for government intelligence eyes only. Now, commercial satellite imagery is a treasure trove of valuable information for anyone looking to purchase and use it.

So, how can you do that? Let's find out.

How to access satellite imagery

With trillions of pixels of our planet being captured from space every day, there’s never been so much data within reach. You can find free EO data in lots of places, including on UP42. But when you need commercial satellite imagery, purchasing and using satellite imagery can become a little more complicated. Historically, it's often been a tedious and time-consuming process.

Before, it would involve:

1. Identifying what kind of data you need, depending on what you’re investigating—for example, archive visible imagery at around 1m resolution to assess land cover change.
2. Determining your ideal AOI(s) and date range, e.g, North Africa in the summer of 2017 and summer of 2019.
3. Scouring the internet for which providers have that data, at that resolution, for your AOI(s) and time frame(s).
4. Finding a suitable provider or reseller and contacting them directly, sometimes viewing a catalog of data, but more often not.
5. Making a formal request for a specific data set.
6. Waiting for providers or resellers to get back to you with a price quote and a contract for you to sign.
7. Confirming the data you want to purchase and waiting for data delivery electronically via File Transfer Protocol within the week, or pay extra fees for a rush delivery (even for archive data).

There's been a lot of conversation surrounding the struggles of buying satellite imagery—especially if you're not a large corporation or have a smaller AOI or budget.

Akin to finding a needle in a haystack, navigating the infrastructure set up by some providers, and finding affordable data that you can work with, is an industry problem that many are trying to solve.

At UP42, we advocate for and work towards making it easier for people to access, analyze, and afford and utilize all kinds of geospatial data.

Platforms like UP42 bring the data providers to you.

Our provider list includes:

• Optical satellites: Pléiades, Pléiades Neo, SPOT, Vantor, BlackSky, Planet, Satellogic, KOMPSAT-3/3A, Beijing-3A/3N, GEOSAT, TripleSat, EROS, AxelGlobe, Sentinel-2, and Landsat 8
• SAR satellites: Capella Space, ICEYE, TerraSAR-X, KOMPSAT-5, StriX, and Umbra
• Hyperspectral satellites: Pixxel and Dragonette-1
• Thermal satellites: SatVu, Constellr, and Hydrosat
• Methane monitoring: GHGSat
• Aerial and drone imagery: Hexagon, Vexcel, Getmapping, Bluesky, IO Aerospace, and Globhe
• Lidar: NV5
• Elevation models: WorldDEM Neo, WorldDEM4Ortho, NEXTMap, Vivid Terrain, Vexcel Elevate, and Hexagon
• Derived products: Airbus Basemap and ground control points

The UP42 platform allows you to browse a variety of different data options, explore coverage through the catalog, and see price estimates before placing orders. This way, you only buy and pay for your chosen AOI.

One more thing: A lack of standardization in the geospatial industry often leads to low availability of plug-and-play integrations. If you want to build a robust backend and easily explore data from different providers, STAC (a standard for geospatial data) can give you the foundation.

Regardless of provider or file type, data on UP42 is standardized, structured, and easy to sort through. Check out our blog post on CNAM for more information.

And if you're on our Enterprise plan, you don't need to download a gigantic file and sort through it to find the one scene you need: you can stream imagery straight into GIS tools without downloading it. The Enterprise plan is built for organizations running Earth observation at scale. Instead of pre-purchasing credits, teams pay through a subscription plus monthly usage-based billing, with consolidated invoices and detailed transaction reporting. A master account can split pre-approved budgets across teams and projects and set project-level limits, so automated pipelines can’t cause unexpected spending spikes.

Enterprise customers also get Premium or Premium+ support, which includes a dedicated Customer Success Manager, a private Slack channel, a two-hour response target for urgent issues and up to 200 hours of expert help a year. On compliance, UP42 handles EULA management across providers and is backed by audited SOC 2 Type II controls and a GDPR-compliant architecture. See our Enterprise page for more information.

Analyzing satellite imagery

Now that we’ve seen what kinds of satellite data exist and how you can find it—where next?

Processing algorithms

Satellite imagery provides essential insights, allowing you to detect changes, spot trends, find patterns, identify objects and derive vegetative indices.

To extract those insights, you will need to apply machine-learning algorithms or processing modules, such as quality enhancement.

Here are some examples of common algorithms.

Pre-processing

These enhance image quality, convert input data, or clip raster images into tiles to create Analysis Ready Data (ARD) to feed your algorithms.

Indices, bandmath, and statistics

These types of algorithms can do things like help to analyze crop health with an Enhanced Vegetation Index (EVI) or a Normalized Difference Vegetation Index (NDVI), or to map the impact of environmental changes, such as flooding or burnt areas, in your area of interest.

Other processing types

These range from object identification, area classification, and change detection to vertical-specific algorithms.

Computing power

When analyzing data, computing power is vital. But crunching terabytes of imagery on a planet-wide scale is no easy feat. You’ll need significant processing capabilities and computing power. That’s where our infrastructure comes in.

UP42 gives you direct access to the EO datasets you need and makes it easy to integrate them into your own workflows via our API and Python SDK.

Using satellite imagery

Now that we’ve looked at what satellite imagery entails and where you can find and analyze it, how can you make use of it?

Satellite images have become a priceless repository of geospatial data that drive solutions in a wide variety of fields—spanning across industries and creating significant societal, environmental, and economic impacts.

Climate change

Global surface temperatures are increasing, global sea levels are rising, ice is melting, and changes in the pattern of precipitation are bringing intense rainfall and floods to some areas and devastating droughts to others.

Satellite measurements are essential to improving our understanding of climate change and formulating effective strategies. As our world warms, knowing information such as surface warming trends and heat flux areas is more important now than ever.

Use satellite imagery to inform decision making, fill the data gap, create climate action, and predict the future.

Deforestation

The economic, social, and environmental impacts of deforestation can be drastic. Often the result of poor land management and illegal logging, deforestation ends in loss of critical habitat for wildlife, loss in government revenues, and distortion of global markets.

Remote forests are difficult to monitor. Satellite monitoring enables the true extent of deforestation to be understood. Use UP42 to combine satellite data and off-the-shelf analytics to detect indicators linked to deforestation such as the presence of trucks or increase in settlements.

Precision agriculture

Agriculture consists of complex interdependent processes. In order to maximize yield and sustainability, resources should be organized efficiently. In contrast to conventional agricultural methods, the use of digital satellite data can increase resource and cost efficiency drastically.

Optical imagery can provide invaluable insights into crop growth and development. In other cases with high cloud cover, radar monitoring can be used.

Use satellite imagery to build intelligent solutions to increase crop yields, effectively manage field use, and save resources in an industrial farming environment.

Satellite imagery coupled with agriculture-specific algorithms enable targeted application of fertilizers, pesticides, and irrigation—advancing agriculture both economically and ecologically.

Insurance

When disaster strikes and losses occur, there’s nothing more important to insurance policyholders than recovery.

In the key moments between disaster and disaster relief, customers rely on their insurers to respond as quickly and efficiently as possible. For remote locations especially, assessing claims and fraud can be cumbersome and costly.

Use the latest satellite information and high-speed, scalable cloud-computing, so you can do:

• Site identification
• Fraud verification
• Claim assistance

Satellite insights allow you to expedite and automate insurance claim verification and fraud identification for homes, crops, and other assets. You can read more about how our customer, AI Superior, helped develop an insurance solution through satellite imagery in this case study.

Encroachment

Encroachment ranges from the violation of tenure rights in protected public or private lands to settlement growth near powerlines, railways, roads and other critical infrastructure.

In all these cases, encroachment can increase hazard vulnerability, worsen socio-economic disparity, and lead to conflict and environmental degradation.

Satellite imagery enables encroachment to be detected and insights to be gathered on the rate of development, size of encroachment, and projected costs and risks.

Explore near real time Earth observation data, processing algorithms, and powerful infrastructure to detect and mitigate encroachment for all of your AOIs.

Humanitarian crises

Thousands of people across the globe are forced to flee their homes every day due to disasters and conflict. Accurate, relevant, and timely data is crucial to humanitarian crisis management.

Under-estimating the number of displaced people can mean delayed response and prolonged suffering—while overestimation can lead to unnecessary costs and loss of credit.

Gain access to evidence-based knowledge to support disaster risk reduction and response plans with satellite imagery and analysis data. With satellite insights, you can get the right answer at the right time.

This allows you to track humanitarian crises as they unfold around the world and respond with timely and targeted aid.

Maritime

The ocean covers more than 70% of the Earth’s surface. As a result, maritime activity tracking can require analysis covering thousands or even millions of square kilometers.

Having a satellite’s-eye-view optimizes your maritime activity—combined with additional data streams such as AIS and weather parameters.

Satellite imagery enables you to track, predict, and optimize maritime activity on a global scale.

Integrate an array of data types and build maritime products to monitor and improve vessel, port, and open water activity.

This way, you are able to automatically detect changes and prevent asset loss by enhancing your situational awareness.

Read A Complete Guide to Marine Traffic Tracking Technologies and AIS Data for more information.

Mining

Satellite imagery helps with many different stages of mining, from exploration and active site monitoring to reclamation. Hyperspectral and short-wave infrared imagery can show specific mineral signatures from above.

High resolution imagery and elevation models also allow you to track erosion over time, filling intelligence gaps between lidar surveys without the need to send crews to remote sites.

Remote sensing data also gives a permanent record of reforestation efforts, water quality, and land cover changes. This data is invaluable for legal compliance around mine site closures. For more info, see our dedicated blog post on mining.

Next steps

You've made it all the way through. There's only one thing left to do. Sign up to UP42 and start exploring.