Explore SAR satellite imagery

Satellites with Synthetic Aperture Radar (SAR) sensors produce images based on radar technology. One of SAR's strengths is that it can create clear images in both daytime and nighttime and regardless of the presence of clouds, smoke, and rain. It has many applications and is often used to detect changes in the environment due to natural- or human-induced events.

In this tutorial, you will explore three applications of SAR imagery: mapping deforestation, monitoring maritime activity, and identifying flooded areas. Your exploration will take you to Brazil, the Panama Canal, and Texas, and you will learn the basics of working with SAR imagery.

The SAR images you'll use in this tutorial were downloaded from SAR imagery providers as Ground Range Detected (GRD) data products, and were then processed using ArcGIS Pro tools from the Synthetic Aperture Radar toolset to produce analysis-ready SAR data. So, they are ready for you to start exploring and interpreting them.

This tutorial was last tested on April 26, 2023, using ArcGIS Pro 3.1. If you're using a different version of ArcGIS Pro, you may encounter different functionality.

Requirements

• ArcGIS Pro (see options for software access)
• ArcGIS Image Analyst

Map deforestation

In this first scenario, as an imagery analyst for the Brazilian Forest Service agency, you will inspect analysis-ready SAR imagery to identify deforestation activities in the state of Acre, Brazil.

Download and open the project

To get started, you'll download a project that contains all the data for this tutorial and open it in ArcGIS Pro.

1. Download the Explore_SAR_Satellite_Imagery.zip file and locate the downloaded file on your computer.

   Note:

   Most web browsers download files to your computer's Downloads folder by default.

2. Right-click the Explore_SAR_Satellite_Imagery.zip file and extract it to a location on your computer, such as your Documents folder.
3. Open the extracted Explore_SAR_Satellite_Imagery folder, and double-click Explore_SAR_Satellite_Imagery.aprx to open the project in ArcGIS Pro.

   

4. If prompted, sign in to your ArcGIS organizational account or into ArcGIS Enterprise using a named user account.

   Note:

   If you don't have access to ArcGIS Pro or an ArcGIS organizational account, see options for software access.

   The project opens.

   

   In the map pane, there are three map tabs: Deforestation Map, Maritime Monitoring Map, and Flooding Map. For the first scenario, you will look at the Deforestation Map tab.

   

Explore the map and understand the symbology

You will now explore the Deforestation Map tab and understand how the SAR imagery it contains is symbolized.

1. If necessary, click the Deforestation Map tab to select it.

   

   In the Contents pane, you can see that the map contains two images: AcreBrazil_20210722.crf and AcreBrazil_20210802.crf, displaying over World Imagery, an imagery basemap.

   

   Note:

   These analysis-ready SAR layers are derived from SAR Ground Range Detected (GRD) data products from ICEYE.

   You can learn more about SAR GRD and other product types commonly used to deliver SAR imagery in SAR Satellite Data.

   To find out more about these images (or any other images used in this tutorial), in the Contents pane, right-click the image name and select View Metadata. Alternatively, you can review both the Metadata and Source information by double-clicking the image name to show the Layer Properties window. In the Source information, under the Processing History section, you can also find information about the preparatory processing that was applied to obtain these analysis-ready SAR images.

   Both images represent the same extent in the Brazilian state of Acre, in the heart of the Amazon rainforest. They were captured on July 22, 2021, and August 2, 2021. On the map, you can currently see the top image, AcreBrazil_20210722.crf.

2. On the map, look at the SAR image.

   The way the landscape is represented may seem unusual, since it was captured by a SAR sensor and not an optical camera: it doesn't look like a photo. SAR satellites beam radar waves to the surface of the earth and map the reflected wave. The wave received by the SAR sensor is called the measured backscatter. A SAR image is a 2D rendering of the measured backscatter. The radar waves reflect differently on different kinds of surface on the ground, yielding different backscatter values.

   Note:

   Learn more about the concept of backscatter in the Fundamentals of Synthetic Aperture RADAR (SAR) guide.

3. In the Contents pane, review the legend for the AcreBrazil_20210722.crf image.

   The measured backscatter values vary from about -33 to -4 dB (the unit of the measurement is the decibel). The lower values appear in black and dark grays, and the higher values appear in light grays and white.

   

4. On the map, examine the different types of features displayed in the image.
   • The forested areas appear in medium gray tones (middle backscatter values) and as rough surfaces.
   • Bare earth areas appear in black or dark gray tones (low backscatter values) and as smooth surfaces. These are areas that were deforested at some point in the past and likely repurposed for agriculture.
   • Recently deforested areas appear in light gray tones (high backscatter values) and as rough surfaces, since it likely still contains logs and tree stumps.

   

   The image shows (A) forested areas, (B) bare earth, and (C) recently deforested areas.

   On tree canopies in the forest, the radar waves reflect in a specific way called volumetric scattering: the waves reflect off the trees' 3D features multiple times, changing direction randomly during the reflections. This yields the rough texture effect you can see in the image.

   Note:

   Learn about the different types of scattering in the Fundamentals of Synthetic Aperture RADAR (SAR) guide.

5. On the map, observe the recently deforested area in the middle of the image.

   

   The recently deforested area appears carved into the forest. It is delimited by black linear features (lowest backscatter values) on the west side of the clearing and a white linear feature (highest backscatter values) on the east side. The black line corresponds to shadows caused by the presence of the trees blocking the radar wave and the white line to the layover effect, caused when the radar wave reaches the top of a tall feature before it reaches the base.

   Tip:

   Visually, some users may view the deforested area as popping out instead of being carved in. Looking at the image from left to right and paying particular attention to the dark line representing tree shadows can help you interpret the image correctly.

   Effects like shadows and layover are typical SAR distortions that occur because SAR satellites capture imagery looking sideways. In this case, the satellite is descending and orbiting from north to south and capturing the scene by looking left (toward the east). It is analogous to sitting on the left side of an airplane and looking out the window.

   Note:

   Learn about the different distortions in the Fundamentals of Synthetic Aperture RADAR (SAR) guide.

6. Observe some other interesting features.
   • There is a strip of forest that runs along a river and seems to be escaping deforestation, probably because the banks of the river are too steep for agriculture.
   • Smaller creeks that are tributaries of the river also retain small strips of forest that follow their shape.

   

   Strips of forest run along a river (A) and along creeks (B and C).

   You'll review the settings used to symbolize this image in more detail.

7. In the Contents pane, if necessary, click the AcreBrazil_20210722.crf layer to select it.

   

8. On the ribbon, on the Raster Layer tab, in the Rendering group, click the Symbology button.

   

   The Symbology pane appears.

9. In the Symbology pane, observe that the image is currently displayed as a Stretch, with a black-to-white color scheme.

   

   As you saw earlier in the legend, this means that all the pixels in the image are shown in black-to-white tones, with the lower measured backscatter values appearing in black, the higher values in white, and a gray gradient in between for the middle values.

10. Review the Stretch type and Number of standard deviations parameters.

    

    These settings follow the general recommendation to display SAR images using Standard Deviation as the Stretch type setting and a Number of standard deviations value of 1.

11. In the Contents pane, click AcreBrazil_20210802.crf to select it.

    The Symbology pane updates to show the settings for that second image. You can observe that it uses a symbology similar to the first image.

Next, you will compare the two images and visualize how the area of interest has changed between July 22 and August 2, 2021, when the two images were captured.

Compare SAR images

You will now compare the two images using the Swipe tool.

1. In the Contents pane, verify that the AcreBrazil_20210722.crf layer is selected.

   

2. On the ribbon, on the Raster Layer tab, in the Compare group, click Swipe.

   

3. On the map, grab the swipe handle and drag repeatedly from top to bottom or side to side to peel off the July 22 image and reveal the August 2 one.

   

   Tip:

   To use the Swipe tool, the layer that you want to peel off must be selected.

   As you swipe, you can observe the differences between the two images. In the center of the study area, the deforestation activities have significantly extended in the western direction.

   

   Strikingly, this change happened only in the span of 12 days.

4. When you are finished exploring the images, on the ribbon, on the Map tab, in the Navigate group, click the Explore button to exit the swipe mode.

   

In this first scenario, you explored analysis-ready SAR imagery to visualize an active area of deforestation. As an imagery analyst for the Brazilian Forest Service agency, inspecting such imagery regularly will allow you to track deforestation and other forestry activities precisely and provide policy makers with highly accurate reports. Several SAR satellites have short repeat intervals that can image activity such as deforestation in near real time. For instance, ICEYE provides new images for every location on earth every 1 to 22 days, and Sentinel-1 every 6 to 12 days. In an area like the Amazon rainforest, which is very often covered with thick clouds, SAR imagery is particularly valuable, as it can penetrate clouds.

Monitor maritime activity

In the second scenario, as an imagery analyst for the Panama Canal Authority, you must locate and monitor the ships waiting to sail passage through the Panama Canal. You'll change the symbology of an analysis-ready SAR image and assess the presence of ships in it.

Symbolize the imagery

First, you'll switch to the map for this second scenario and change the default symbology color scheme of the analysis-ready SAR image to better visualize the measured backscatter.

1. Click the Maritime Monitoring Map tab.

   

   The map appears. It contains a single SAR image, PanamaCanal_20220207.crf, displayed over an imagery basemap.

   

   Note:

   Like the images used in the previous scenario, this analysis-ready SAR image is derived from a SAR GRD data product from the ICEYE satellite mission.

2. In the Contents pane, click the symbol for the PanamaCanal_20220207.crf layer.

   

   Tip:

   This is another way of opening the Symbology pane.

   The Symbology pane appears.

3. In the Symbology pane, for Color scheme, expand the drop-down list and verify that the Show names option is checked. Select the Viridis color scheme.

   

   On the map, the PanamaCanal_20220207.crf layer updates.

Interpret the data

You will now examine the image to identify the ships.

1. On the map, observe the updated imagery.

   

   The areas of highest backscatter values appear in bright yellow and lowest backscatter values in dark blue tones. The ocean appears in dark blue and green tones. The land appears in yellow and light green tones. In the ocean, the ships appear in bright yellow. Here again the values are expressed in decibels.

   In this image, the highest backscatter values correspond to a type of scattering named double-bounce. In this type of scattering, the radar signal reflects once off a vertical target onto a smooth surface and reflects a second time off the smooth surface back toward the sensor. This scattering type can take place on human-made structures, such as ships or buildings.

   Note:

   Learn more about double-bounce scattering in the Types of Scattering section of the Fundamentals of Synthetic Aperture RADAR (SAR) guide.

2. On the map, zoom in and out with the mouse wheel button and pan over the image to observe the ships located just outside the Panama Canal entrance.

   

   Most ships appear as small elongated yellow shapes. However, because of the double-bounce scattering effect, some can appear as bright plus signs or stars, as highlighted in the following example image.

   

   Note:

   Since ships give off double-bounce scattering, the backscatter contains high values that can sometimes be interpreted as bright plus signs or stars. This represents a common image artifact intrinsic to SAR imaging.

   You'll save your project.

3. On the Quick Access Toolbar, click the Save button.

   

In this second scenario, you symbolized and explored an analysis-ready SAR image to find and locate ships waiting to sail passage through the Panama Canal. This application of SAR imagery provides logistical information that can be used by companies tracking the transport of commodities and merchandise goods and cargo. It can also be used to monitor potential vessels that carry out illegal, unreported, or unregulated (IUU) fishing activities.

Identify flooded areas

In the last scenario, as an image analyst for the U.S. Federal Emergency Management Agency (FEMA), you will identify areas flooded by Hurricane Harvey in the area of Freeport, Texas. Working with analysis-ready SAR images captured before and after the hurricane, you'll learn about polarization bands and derive two color composites. Then you'll use the swipe tool to visualize the flooded areas.

Create color composites

First, you'll switch to the map for this third scenario, learn about polarization bands, and create color composites for pre- and post-hurricane images.

1. Click the Flooding Map tab.

   

   The map appears. It contains two analysis-ready SAR images: a pre-hurricane image, Texas_20170805.crf, and a post-hurricane image, Texas_20170829.crf. The images were captured on August 5 and 29, 2017.

   

   Note:

   These analysis-ready SAR images are derived from SAR GRD data products from the European Space Agency Copernicus Sentinel-1 mission.

   Currently the after-hurricane image, Texas_20170829.crf, is turned on and displays on the map. To better understand the structure of this image, you'll look at the Symbology pane.

2. In the Contents pane, click the symbol for the Texas_20170829.crf layer.

   

   The Symbology pane appears.

3. In the Symbology pane, examine the settings.

   The image is currently displayed with Stretch symbology, with a black-to-white color scheme.

   

4. For Band, expand the drop-down list.

   

   The image is composed of two bands: VV and VH.

   A single SAR image can contain several bands, each band representing a different polarization of the radar wave. In this case, one band is co-polarized (VV) and the other one is cross-polarized (VH). Each band can highlight different features on the earth's surface, so they all contain useful information.

   Note:

   Learn more about Polarization in the Fundamentals of Synthetic Aperture RADAR (SAR) guide.

   Each band can be visualized separately. Currently the band displaying on the map is VV. You'll switch to VH.

5. For Band, select VH.

   The map updates. You can see that the VV and VH polarizations each highlight different elements of the landscape.

   

   The first image shows the VV band and the second image the VH band for the post-hurricane Texas_20170829.crf image.

   Note:

   Some SAR imagery contain only one band and some contain two bands or more. It depends on the type of active sensing used. Active sensing allows you to control the polarization of the transmitted electromagnetic waves.

6. Optionally, use similar steps to visualize the VV and VH bands for the pre-hurricane Texas_20170805.crf image.

   While you can view each band independently, combining them will create a richer view of the landscape and will allow you to distinguish more clearly surface characteristics such as water, land, and urban structures. You can do that by creating a color composite, in which each band will be assigned to the red, green, or blue display channels. You'll first create the color composite for the post-hurricane image.

7. On the ribbon, on the Analysis tab, in the Geoprocessing group, click Tools.

   

   The Geoprocessing pane appears.

8. In the Geoprocessing pane, type Create Color Composite in the search box. In the list of results, click the Create Color Composite (Image Analyst) tool to open it.

   

9. On the Parameters tab, for Input Raster, choose Texas_20170829.crf.

   

   The tool automatically populates the remaining parameters. You will change some of these values.

10. Set the following parameter values:
    • For Output Raster, verify that the value is Texas_20170829_RGB.crf.
    • For Method, choose Band names.
    • For Red Expression, verify that the value is VV.
    • For Green Expression, verify that the value is VH.
    • For Blue Expression, type VV-VH.

    

    For the Method parameter, choosing Band names specifies that you will designate bands by their names, such as VV and VH.

    Because the original image has two bands (VV and VH), there is not a third band available to assign to the Blue Expression. Instead, you populate it with the math formula VV-VH. This means that for each pixel in the image, the value for the VH band will be subtracted from the value in the VV band. The result will act as a third band that will be displayed through the blue channel and will further highlight interesting features of the landscape.

    Note:

    The band math used depends on the units of your input SAR data. If your input SAR data is in decibels, the band combination must be VV for red, VH for green, and VV-VH for blue. If your input SAR data is in linear units, use VV for red, VH for green, and VV/VH for blue.

11. Click Run.

    A new raster layer, Texas_20170829_RGB.crf, appears on the map.

    

    The color composite shows water bodies (ocean, rivers, and flooded areas) in blue and purple tones, vegetated and forested regions in green, urban structures in yellow. Pink can be optimally oriented urban structures (oriented orthogonally to the radar look direction), debris in the water, or flooded vegetation.

    You will now create the color composite for the pre-hurricane Texas_20170805.crf image.

12. In the Geoprocessing pane, on the Parameters tab, for Input Raster, choose Texas_20170805.crf. Leave the other parameters with their current values and click Run.

    

    A new raster layer, Texas_20170805_RGB.crf, appears on the map.

    

Next, you'll fine-tune the symbology of the color composite layers to make them more meaningful.

Fine-tune the symbology

To compare the pre- and post-hurricane color composites, you must ensure that they use the same symbology. You already know that they contain the same bands, but you still must adjust the symbology to maintain the same value ranges between the two images.

1. Turn Texas_20170829_RGB.crf off and on to compare with Texas_20170805_RGB.crf.

   Currently the value ranges are different. For instance, you can see that the vegetated land displays in darker green in one image and lighter green in the other one.

2. In the Contents pane, click one of the symbols for Texas_20170829_RGB.crf.

   

   The Symbology pane appears.

3. In the Symbology pane, click the options button and choose Import from layer.

   

4. In the Geoprocessing pane, for the Apply Symbology From Layer tool, choose the following parameter values:
   • For Input Layer, verify that Texas_20170829_RGB.crf is selected.
   • For Symbology Layer, choose Texas_20170805_RGB.crf.
   • For Update Symbology Ranges by Data, verify that Maintain ranges is selected.

   

5. Click Run.

   The two color composites are now using the same symbology with the same range values. For instance, the vegetated land displays roughly with the same light green tones.

Next, you will examine imagery and interpret the data.

Compare the color composites

You'll use the Swipe tool to visualize the differences between the before- and after-hurricane color composites.

1. In the Contents pane, click the Texas_20170805_RGB.crf layer to select it.
2. On the ribbon, on the Raster Layer tab, in the Compare group, click Swipe.

   

3. On the map, grab the swipe handle and drag repeatedly from top to bottom or side to side, to peel off the pre-hurricane image and reveal the post-hurricane one.

   

   Observe the differences and identify the areas that have become flooded. Try to distinguish features that can be used as reference points. Many areas that were previously dry land have become blue, indicating that they are submerged with water after the hurricane, or purple indicating the presence of vegetation or debris in the flooded area.

4. When you are finished exploring the images, on the ribbon, on the Map tab, in the Navigate group, click the Explore button to exit the swipe mode.

   

   You'll save your project.

5. On the Quick Access Toolbar, click the Save button.

   

   Note:

   For more information on flood mapping with SAR imagery, see the Interpretation of SAR data for flood mapping documentation page.

In this third scenario, you created two color composites from the analysis-ready SAR backscatter for a co-polarized band and a cross-polarized band, and ensured the same value ranges were maintained between the two images. Then you used the swipe tool to compare the pre- and post-hurricane imagery and identify the flooded areas. This type of interpretation provides the foundation for some of the advanced analysis techniques used to generate flood maps. Flood maps have various purposes, such as supporting disaster management and search and rescue operations or helping mortgage lenders determine insurance requirements.

In this tutorial, you explored three applications of analysis-ready SAR imagery: mapping deforestation, monitoring maritime activity, and identifying flooded areas. You learned several methods to change the symbology of the imagery and highlight features of interest. You then explored and interpreted the data using tools to interact with the map.

You can find more tutorials in the tutorial gallery.

Acknowledgements

• Texas_20170805_RGB2.crf and Texas_20170829_RGB2.crf: SAR satellite imagery captured by the ESA Copernicus Sentinel-1 mission, Aug. 5 and 29, 2017. Retrieved via the Copernicus Open Access Hub. Processed by Esri.
• AcreBrazil_20210722.crf and AcreBrazil_20210802.crf: SAR satellite imagery captured Jul. 22 and Aug. 2, 2021 by ICEYE. Processed by Esri.
• PanamaCanal_20220207.crf: SAR satellite imagery captured Feb. 7, 2022 by ICEYE. Processed by Esri.
• Basemaps: World Imagery (Esri, Maxar, Earthstar Geographics, and the GIS User Community) and Imagery Hybrid (Esri and its users and partners).