How can you process and analyze remote sensing data from active microwave sensors?

Active microwave sensors are a type of remote sensing instrument that emit and receive microwave signals to measure the properties of the Earth's surface and atmosphere. They can provide valuable information on soil moisture, vegetation, snow cover, ocean waves, ice, and other features that are not easily detected by optical or passive microwave sensors. However, processing and analyzing active microwave data can be challenging, as they require specific software, techniques, and knowledge. In this article, you will learn how to handle active microwave data from different sources and formats, how to apply common preprocessing steps, and how to perform some basic analysis using Geographic Information Systems (GIS) tools.

1 Data sources and formats

The first step in processing and analyzing active microwave data is to obtain the data from reliable sources and understand the formats and metadata. There are many active microwave sensors operating on different platforms, such as satellites, aircraft, or ground-based systems, and each one has its own specifications, modes, and products. Some of the most widely used active microwave sensors are Synthetic Aperture Radar (SAR), Scatterometer, and Radar Altimeter. You can access the data from these sensors from various online repositories, such as the Copernicus Open Access Hub, the Alaska Satellite Facility, or the NASA Earthdata portal. Depending on the sensor and the product, the data may come in different formats, such as GeoTIFF, HDF, or NetCDF, and may contain different information, such as amplitude, phase, polarization, incidence angle, or backscatter coefficient. You should always read the documentation and metadata of the data to understand the characteristics and limitations of the data.

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Daniel Hermosillo

InSAR Analyst
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We must not forget that open-source data is not the only way to get access to SAR data. While sources like Sentinel-1 satellites are free-access, there are some considerations to take into account when choosing the right sensor for a specific project. Sometimes, simply Sentinel-1 is not feasible to perform InSAR on specific sites such as highly vegetated areas. When the needs of my project exceed the capabilities of Sentinel-1, we have to turn our eye to private companies that own and operate commercial-access data. The satellites that private companies operate are slightly different from Sentinel-1, in the wavelength, footprint extension, acquisition mode, spatial resolution, and acquisition frequency.

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2 Data preprocessing

The second step in processing and analyzing active microwave data is to apply some preprocessing steps to enhance the quality and usability of the data. Depending on the data source and format, you may need to convert, project, calibrate, filter, and mask the data. For example, you may need to convert the data from HDF to GeoTIFF using GDAL or HDFView, project it from polar stereographic to geographic using QGIS or ArcGIS, calibrate it from digital numbers to backscatter coefficients using SNAP or PolSARpro, filter it to reduce noise or speckle using SNAP or PolSARpro, and mask it to exclude unwanted areas or features using QGIS or ArcGIS. Doing so will make the data compatible with GIS software, consistent with other data layers or maps, comparable with other data sets or models, and more focused on regions or variables of interest.

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Daniel Hermosillo

InSAR Analyst
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SAR data can be used in a variety of ways, and the type of processing depends on whether you want to generate cartography or identify displacement on the surface. For identifying displacement, the technique is called InSAR which stands for Interferometric Synthetic Aperture Radar. This technique is based on analyzing the phase between images collected over the same site but in a different period of time, and the magnitude of the displacement is directly proportional to the difference in phase.

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3 Data analysis

The third step in processing and analyzing active microwave data is to perform some basic analysis to extract meaningful information and insights from the data. Depending on your research question or application, you may want to visualize the data to explore the spatial patterns, variations, and anomalies; classify the data to identify and separate different land cover types; extract the data to measure and quantify specific parameters; or compare the data to evaluate and validate the data quality, accuracy, or consistency. Tools like QGIS or ArcGIS can be used to display the data as raster images, apply supervised or unsupervised classification methods, apply empirical or physical models, or perform statistical or spatial analysis.

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Daniel Hermosillo

InSAR Analyst
(edited)
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If you are performing InSAR, analysis and interpretation of the results might be quite hard and require a high experience and background along with informatics skills. Processing InSAR is not like generating cartography with SAR data, there are a few algorithms with a command line interface that anyone can use, but there is where the informatics skills are required. It is not possible to perform InSAR in the common geographic information systems.

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4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you process and analyze remote sensing data from active microwave sensors?

1

2

3

4

1 Data sources and formats

2 Data preprocessing

3 Data analysis

4 Here’s what else to consider

Geographic Information Systems (GIS)

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How can you process and analyze remote sensing data from active microwave sensors?

1

2

3

4

1 Data sources and formats

2 Data preprocessing

3 Data analysis

4 Here’s what else to consider

Geographic Information Systems (GIS)

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