Medium-resolution satellite imagery is typically 10- to 30-meter resolution, gathered by satellites such as the Landsat missions or Sentinel-2. It's typically used for regional or landscape-scale analysis and visualization. It's generally multispectral, often containing many bands in the visible, near-infrared, and infrared regions of the electromagnetic spectrum, sometimes including panchromatic or cloud masking bands.
Organizations often manage large collections of medium-resolution satellite imagery and accompanying metadata, often including overlapping images gathered on multiple dates across large areas. This data may need additional processing before it is helpful for end users, or it may be analysis-ready. Users may also want to visualize the data in different ways—looking at only the newest imagery, for example, or filtering out low-quality images. Additionally, since the imagery is multispectral, users may want to view different band combinations, indexes, or pansharpened imagery.
ArcGIS Pro provides tools and user experiences to simplify the process of visualizing and managing collections of medium-resolution multispectral satellite imagery:
- For preprocessing—If necessary, ArcGIS Pro includes the tools necessary for any preprocessing your imagery requires, including geolocation, radiometric correction, georeferencing, and orthorectification.
- For single scenes—ArcGIS Pro uses raster products, which simplify the process of visualizing multiband images in your map. Drag the raster product onto the map, and it's visualized with default band combinations and processing, such as pansharpening, applied.
- For collections of images—You can manage scenes using a mosaic dataset configured for a specific type of satellite imagery. Mosaic datasets make it straightforward to visualize, query, and analyze your data. You can organize metadata, define mosaicking rules, and include raster function templates for different visualizations of spectral data (such as NDVI or color infrared). When configured for a particular raster type, such as Sentinel-2, the mosaic dataset identifies metadata, such as georeferencing, acquisition date, sensor type, and band wavelengths, along with a raster format for any images added. Commonly used processing templates, such as all bands, pansharpened imagery, or thermal data, can also be selected.
- For sharing imagery—Beyond direct use in ArcGIS Pro, mosaic datasets are also optimized to share imagery with end users and applications. Satellite imagery managed with mosaic datasets can be shared two ways. If end users will need dynamic access to the imagery (if you want to take advantage of raster functions to include color infrared, natural color, and NDVI views, for example, or control the display order of the imagery), the imagery can be shared as an image service using ArcGIS Image Server. If end users only need to visualize the imagery, as a basemap, for example, it can be shared as a three-band, 8-bit raster tile cache. The tile cache can be created in ArcGIS Pro, then uploaded to ArcGIS Online for hosting and sharing.
Explore the following resources to learn more about managing medium-resolution satellite imagery. (Not sure where to start? Look for the star by Esri's most helpful resources.)
Note:
To create and edit mosaic datasets, you need ArcGIS Pro Standard or ArcGIS Pro Advanced. To serve mosaic datasets as dynamic image services, you need ArcGIS Image Server. To host raster tile cache, you can use ArcGIS Online or ArcGIS Enterprise.Imagery Workflows resources
Review the community-supported tools and best practices for working with and automating imagery and remote sensing workflows:
- Download sample Python scripts for managing Landsat 8 scenes using best practices.*
- Download sample Python scripts for managing Sentinel-2 scenes using best practices.*
- Explore Esri's Sentinel-2 and Landsat image services directly, or use them in the Earth Observation Explorer app, to better understand how a mosaic dataset published as an image service works.
- Read documentation explaining best practices for creating mosaic datasets and structuring and formatting imagery and rasters.
ArcGIS help
Review the following links on reference materials for ArcGIS products:
- Learn about using mosaic datasets to manage imagery in ArcGIS Pro.
- Read about working with raster products and raster types to manage and visualize imagery.
- Consult a list of supported satellite sensor raster types.
- Read how to create a multidimensional mosaic dataset from a time series of images.
- Learn about using raster functions to visualize imagery in ArcGIS Pro, especially functions for Pansharpening, NDVI, NDVI Colorized, Extract Band, and Composite Band.
- Learn about orthorectifying imagery in ArcGIS Pro.
Tutorials and training
Review the following guided lessons and tutorials based on real-world problems and key ArcGIS skills:
- Watch Managing Raster Data Using ArcGIS to learn how to use mosaic datasets to enable efficient data storage and fast visual performance.* (2 hours)
Developer resources
Review the following resources and support for automating and customizing workflows:
- Visit the MDCS GitHub repository to download a customizable Python script to help automate the creation and configuration of mosaic datasets (used in the samples above).
- If you plan to manage your satellite imagery
in the cloud, or want to optimize the data format for faster access, visit the OptimizeRasters
GitHub repository for scripts and tools to optimize data transfer and
management.
Note:
OptimizeRasters can also be used to generate raster proxies for Sentinel-2 or Landsat 8 imagery that's freely accessible in Amazon Web Services, allowing you to visualize the imagery without storing it yourself. Consult the OptimizeRasters documentation for more information.
Esri Community
Use the online imagery community to connect, collaborate, and share experiences:
- See what the Imagery and Remote Sensing community is saying about managing medium-resolution satellite data.