Table of Contents
What is satellite data?
Satellite data provides satellite imagery and earth observation data of the earth’s surface and its atmosphere. Satellites also provide images of other planets. Resolution images of the earth indicate changes in land cover, cloud cover, ocean levels, ice cover, and atmospheric composition.
This data is used for tracking weather patterns and climate changes. It is also used for powering multiple research projects.
Where does the data come from?
Satellite imagery data is collected using remote sensing technologies, such as Optical Imagery or Synthetic-Aperture Radar (SAR). They gather and transmit data to earth. The data and images (from various data sources) are processed, interpreted, augmented with other data, and then made available as data products. Some data may have restricted access due to security reasons.
Most of the satellites used to collect this data are launched and controlled by governments such as the USGS, the NOAA, and the European Space Agency (ESA). Some private companies operate in this field, too.
Earth observation satellites and geostationary satellites have relatively low orbits that enable more precise images. Besides satellite images, aerial and ground-based sensors are used to collect this data. For example, satellite data about tectonic plate movements is enriched with sensors recording ground movement.
Passive remote sensing systems use optical and thermal sensors. They can work only during sunlight and clear skies. Active remote sensing systems use Real Aperture Radar(RAR), Synthetic-Aperture Radar (SAR), and lasers. The satellite sends radiation to Earth and measures the energy reflected from the surface or atmosphere. The active remote sensing systems can work at any time.
What types of attributes should I expect?
The data mostly comes as images and interactive maps, with the attributes of latitudinal and longitudinal coordinates.
Additional attributes vary based on the use case requirements and may need specialized instruments to measure them.
Some of the attributes are:
- Spatial resolution: Identifies the separate objects in an image.
- Geometric resolution: Measures the size of an area in a single-pixel that an image can record.
- Temporal resolution: Denotes the time-series of the image, critical to identify changes over a period and map them to natural events or disasters.
- Radiometric resolution: Records the levels of brightness and contrast, expressed in 8-bit / 11-bit / 12-bit /16-bit.
- Spectral band resolution: Measures the wavelength size and records the number of intervals between wavelengths.
How should I test the quality of the data?
The accuracy, resolution, regular maintenance, and calibration status of the equipment drive data quality. Most satellites are maintained by the military or government (such as the landsat 8), and they are usually in excellent condition. Image processing often uses sophisticated tools and delivers high-quality data.
Testing the quality of the data depends on the intended use. Evaluate if the data attributes are relevant to your use case and if their precision matches your requirement.
- Temporal resolution: Data timeliness is critical for most use cases. Some images are updated every three hours, while some are updated daily. Test and choose the most suitable temporal resolution for your use case.
- Spatial resolution: Very high-resolution radar images provide less than 5 meters of spectral resolution, while very high-resolution optical images can go below 1 meter. Test and choose the method of data collection appropriate for your intended use.
To test the quality of the data:
- Verify that the data attributes are suitable for your use case.
- Validate that the temporal and spectral resolution matches your requirements.
- Test with a small sample to ensure the best match.
Who uses satellite data?
Global coverage satellite data is used extensively for mapping and reviewing natural resources, studying ecosystems, and monitoring global events. Researchers use the data for studying weather patterns and tracking climate change.
Governments leverage this actionable data to measure and forecast how humans affect their environments. Cumulative data over a long period helps them assess the impact and make policies for improving forest cover, agricultural yield, and environmental protection. When planning urban development and infrastructure projects, governments use satellite data and derived insights. Governments leverage this data in several political situations to accurately determine country borders, identify military targets, and track illegal activities. This data plays a significant role in evaluating and predicting the extent of natural disasters and epidemics by feeding predictive machine learning algorithms and models.
Businesses use satellite data to power a variety of analytical use cases. They leverage the predictive value of this data to estimate future growth and potential investment opportunities.
Satellite data about other planets and astronomical bodies is used in space exploration programs.
What are the common challenges when buying the data?
Buying satellite data presents unique challenges. The data accuracy is usually high, as the military or government maintains the satellites and imaging equipment. Data timeliness is a concern, similar to all categories of geospatial data. It is amplified by the limitations of data bandwidth. Another challenge with satellite data is ensuring the data attributes and resolution match your requirements. The data resolution depends on the imaging methods, and you need to confirm that your vendor can guarantee the expected level.
- Data timeliness: For most of the use cases, the data needs to be regularly updated. Some data providers provide datasets that are updated every few hours, while some are updated over a longer time period. You may need to test with samples to decide the most suitable data update frequency.
- Data bandwidth: Satellites transmit images and data to the earth, where they get processed. The available bandwidth restricts the amount of data that can be transmitted during a period. It also limits spectral capabilities to the common visible bands of red, green, blue, and near-infrared.
- Spatial resolution: The resolution of the images depends on several factors. Radar images can go up to less than five meters of spectral resolution, while optical images can be more precise, often going less than one meter of spectral resolution. Spatial resolution is also limited by the condition of the satellite and equipment, which can deteriorate over time. The angle of image capture, atmospheric distortion, focus, diffraction are some more factors that affect the image resolution. It may be possible to get data customized to your requirements, though it can be costly.
What are similar data types?
You can find a variety of examples of geospatial data in the Explorium Data Gallery.
Sign up for Explorium’s free trial to access the data available on the platform.
What are the most common use cases of satellite data?
Some common use cases include location intelligence, weather forecasting, precision farming, and smart cities. This data is also used for military and government strategic activities, as well as in space exploration. Satellite data can be used to enrich other types of datasets for a variety of use cases.
- Location Intelligence: Insights derived from satellite data and other forms of geospatial data are visualized by layering on maps, and using them in a wide variety of use cases. Governments and military use location intelligence from satellite data in several activities. Police and emergency services also rely on location intelligence for helping people in disaster conditions. Businesses use these insights for marketing, customer profiling, advertising campaigns, store location optimization, and many more use cases.
- Weather Forecasting: It leverages technology, historical data and patterns, and real-time data to predict weather conditions for a location and time. Satellite data provides data and the patterns of atmosphere, land, and ocean conditions built over a long period. This information powers the weather forecasting model. Satellite data also provides real-time tracking of hurricanes, tornadoes, and other natural disasters to alert the population and help them prepare.
- Precision Farming: The use of technology, data, and analytics to manage farms helps improve crop yield. Termed as precision farming, it also covers horticulture and aqua culture. Using satellite data and derived insights about soil, weather, and topological information, farmers can optimize the crops and deliver a better crop yield. Precision farming proactively uses the data to optimize watering and fertilizer and minimize wastage to improve farm efficiency.
- Smart Cities: A smart city plans its infrastructure and services to collect and share data, and leverages this data to improve constantly. Smart cities deliver access, convenience, safety, and comfort to citizens while ensuring optimized resource utilization. Providing highly localized information on utilities, movement, and weather, satellite data enables smart cities to perform efficiently.
Which industries commonly use this type of data?
Industries commonly using the data include transport, logistics, shipping, retail and CPG, tourism, travel, hospitality, entertainment, sports, healthcare, and financial services.
How can you judge the quality of the vendors?
Vendors delivering satellite data should be able to match your requirement of data attributes, spatial resolution, and data timeliness. Judging the vendor quality in advance ensures that the data you purchase can successfully power your use cases. You can leverage the vendor website to make the preliminary analysis, shortlist some vendors, and discuss your project with vendor reps to make the final decision.
- Customer reviews, ratings, and testimonials: The majority of the vendors list customer reviews and ratings on their websites. They also often spotlight customer testimonials describing vendor strengths. All this information can provide you with the level of vendor experience, types of projects, range of supported data categories, ability to deliver custom datasets, level of engagement, innovative solutions, ease of integration, and overall vendor competency. This information can help you eliminate vendors that cannot fulfill your requirement of expected resolution or data timeliness.
- Case studies: At the next step, case studies help you analyze the number of successful projects the vendor has delivered, project sizes, the industry verticals, the range of challenges undertaken, and the level of vendor engagement. These details can help you further narrow down your choice of vendors.
- Demo: Demos help quickly understand how vendors handle projects and contribute with their skillset. Vendor websites usually provide one or two demos to give you an opportunity to view data in action. You can also request more demos or a live demo for your specific requirements. You can use demos to assess how easily the datasets can integrate with your systems and the quality of datasets provided by the vendor. After using demos, you can shortlist a few vendors and move on to the next step of discussing your project with the reps.
- Interacting with vendor reps: This is the best opportunity to resolve your queries, assess the vendor capabilities, confirm if the vendor can deliver custom datasets, and fine-tune engagement details. During the discussion, you can judge the vendor’s suitability and also the possibility of building a long-term business relationship.