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What Is the STAC API for Satellite Imagery and How Does It Work?

Writer: Anvita Shrivastava
Anvita Shrivastava
2 days ago
5 min read

The use of satellite imagery is prevalent in Geographic Information Systems (GIS), remote sensing, environmental monitoring, agricultural practices, urban planning, disaster management, and geospatial analytics. But it might sometimes be difficult to find the exact satellite imagery in huge and constantly expanding repositories.


STAC API (SpatioTemporal Asset Catalog API) is a standardized protocol that enables the discovery of geospatial assets by using spatial and temporal as well as metadata-driven queries. It allows people and applications to find satellite imagery without having to browse through huge collections of data.


STAC API for Satellite Imagery
STAC API for Satellite Imagery

What is the STAC API?


STAC API is an open standard for searching and accessing catalogs of geospatial data and earth observation resources. STAC means SpatioTemporal Asset Catalog.


In the STAC format, geospatial resources are arranged by standardized metadata and facilitate searching for datasets based on features like:


  • Geography

  • Time stamp

  • Amount of clouds

  • Coverage area

  • Type of sensor or satellite

  • Resolution

  • Coordinate reference system

  • Type of data

  • Resources available


A STAC catalog may include satellite imagery, aerial imagery, drone imagery, LiDAR data, elevation models, and other geospatial information.


STAC API builds upon the idea of the STAC catalog using API endpoints on the web, allowing for a programmatic search for geospatial datasets by various applications, including GIS and Python scripts.


Importance of STAC for Satellite Imagery


The satellite imagery database may consist of millions of scenes from vast geographic regions and across significant time frames. Performing searches manually is inefficient.


A conventional process would involve a user:


  1. Picking the geographic region.

  2. Specifying the date range.

  3. Searching for scenes in the archive.

  4. Reading metadata.

  5. Assessing cloud cover.

  6. Downloading the suitable images.

  7. Processing the downloaded data.


STAC offers an organized way to perform the discovery process.


For example, the application may call the STAC API for Sentinel-2 imagery from a certain region within a specific date range that has less than 10% cloud cover.


How Does the STAC API Work?


Here are some of the steps involved in working with the STAC API.


  1. Access a STAC Catalog


Users and applications connect to the STAC API endpoint.


From the endpoint, one can access the collection and individual geospatial items. The collection may refer to a certain satellite mission, sensor, data, or product type.


  1. Define the Search Area


The user defines the geographic extent of interest through spatial coordinates or geometry, such as a bounding box or a polygon.


Examples of such areas are a city, agricultural field, forest, or coastal zone.


  1. Specifying a Time Range


STAC supports temporal searching. One can define the time range between which images are collected.


For example:


  • January 1, 2026

  • March 31, 2026


It is important when there is a need to monitor seasonal change, vegetation growth, flooding, construction, or land use changes.


  1. Specifying Additional Filters


Other filters help in narrowing down the results.


Some of these filters include:


  • Cloud cover

  • Ground sample distance (GSD)

  • Satellite platform

  • Instrument

  • Level of processing

  • Collection

  • Spectral bands


Users could specify a search for satellite scenes over a certain area that have a certain cloud cover percentage.


  1. Retrieval of Matching STAC Items


Matching STAC Items are retrieved via the API.


An Item describes a particular spatiotemporal observation/dataset and holds metadata related to the particular resource.


An Item may also have links to one or more assets.


  1. Retrieve the Data Asset


A returned Item may have links to assets, including the following types of data assets:


  • GeoTIFF image

  • Cloud Optimized GeoTIFF (COG)

  • Thumbnail image

  • Metadata

  • Quality mask

  • Digital elevation model


Using STAC API With Python


STAC APIs are particularly useful in automated geospatial workflows because applications can query data without manually downloading and browsing catalogs.

Python libraries can be used to connect to STAC services, perform searches, and work with returned Items.

A simplified workflow looks like this:

from pystac_client import Client

catalog = Client.open("https://example-stac-api.com")

search = catalog.search(
    collections=["example-collection"],
    bbox=[-74.10, 40.70, -73.85, 40.85],
    datetime="2026-01-01/2026-03-31"
)

items = list(search.items())

for item in items:
    print(item.id)

The exact endpoint, collection name, and query parameters depend on the STAC service being used.

After discovering suitable imagery, additional Python tools can be used to read and analyze raster data.


STAC API and Cloud Optimized GeoTIFF


STAC and Cloud Optimized GeoTIFF (COG) are complementary technologies.


While STAC is used to help find and describe geospatial datasets, COG can be used for efficient storage and access of raster imagery via the cloud.


STAC Item may contain an Asset pointing to COG.


Such an approach could be useful for scenarios where users:


  • Find a STAC API.

  • Find appropriate imagery.

  • Find Item metadata.

  • Find a COG asset.

  • Access the needed parts of the raster.

  • Perform imagery processing via the cloud.


This will make data transfers unnecessary.


STAC API vs Traditional Satellite Imagery Search


Traditional imagery portals often rely on web interfaces where users manually enter search criteria and download files.

STAC API provides a programmatic alternative.

Feature

Traditional Search

STAC API

Search method

Web interface

API queries

Spatial filtering

Usually available

Available

Temporal filtering

Usually available

Available

Metadata

Dataset dependent

Standardized structure

Automation

Limited

High

Large-scale workflows

More manual

Well suited

Cloud workflows

Dataset dependent

Well suited

Integration with Python

Varies

Strong

STAC is not intended to be an alternative for all imagery portals. Rather, it gives a standardized format for geospatial data discovery that is consistent and machine-readable.


Benefits of STAC API for Satellite Imagery


Metadata Standardization


A standard format for describing geospatial assets will make datasets easier to find and combine.


Faster Data Discovery


Users can use metadata parameters such as location, date, collection, cloud cover, etc., to filter imagery collections.


Automation


STAC APIs can be embedded into Python code and geospatial software.


Scalability


Rather than viewing thousands of satellite images, automated processes can look through large collections.


Cloud Native


STAC format is compatible with cloud-based geospatial assets and technologies like Cloud Optimized GeoTIFF.


Interoperability


Standardizing the catalog format makes it easier for different platforms and organizations to share information on geospatial assets.


STAC API and GeoAI


Another area where STAC can be used is GeoAI and machine learning processing.


The machine learning model needs to be trained with lots of spatially distributed data. Rather than manually acquiring the imagery, an automated pipeline can leverage the STAC API to search for imagery based on geographical and time-specific needs.


GeoAI processing could involve:


STAC API

↓

Search satellite imagery

↓

Filter imagery based on location, date, and cloud cover

↓

Acquire imagery assets

↓

Pre-process raster imagery

↓

Process the data using an AI/ML model.

↓

Generate geospatial outputs


This will help develop use cases like land cover classification, object detection, crop monitoring, wildfire detection, and satellite image change detection.


Limitations of STAC API


While STAC offers a lot of advantages, there are some limitations that need to be considered when implementing it.


Quality of metadata: Search results are dependent on how good and comprehensive metadata is provided by the data provider.


Different collections: Each STAC service might offer different datasets, properties, and assets.


Accessing the data: While STAC specifies what data is available, access rights, licensing, and downloading will depend on the data provider.


Processing requirements: Searching for an image is just one step. Satellite datasets can be large and still require substantial computing and bandwidth to analyze them.


The STAC API is a means by which satellite images and other geospatial information can be searched, discovered, and accessed through spatial, temporal, and metadata queries. With its catalog structure, large Earth observation datasets become easy to incorporate into GIS, Python, cloud platforms, and GeoAI.


Through the use of the STAC API in conjunction with Cloud Optimized GeoTIFF, cloud-native geospatial storage, Python, and GeoAI, more automation and scalability can be achieved in the process of handling satellite images.


As the geospatial data increase in size and complexity, discovery methods like STAC can be significant in helping satellite images to be found and managed easily.


For more information or any questions regarding Satellite Imagery, please don't hesitate to contact us at


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India: 98260-76466 - Pradeep Shrivastava

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