Extracting Urban Footprint using Radar Remote Sensing
- Arpit Shah
- Oct 15, 2020
- 5 min read
Updated: Dec 8
It has been a while since I last wrote about Remote Sensing applications—my previous post was on the Oil Spill in Mauritius (MV Wakashio running aground in August 2020). In this entry, I will use Synthetic Aperture Radar (SAR) Imagery acquired by the Sentinel-1 satellite to map the built-up area—technically known as the Urban Footprint—for a couple of Indian cities across two points in time, for change-detection purposes.
For context, India not only occupies three positions in the list of fastest growing cities today, but projections indicate that it could occupy all ten spots until 2035! While urban centers are engines of economic growth, innovation and opportunity, unchecked urbanization brings significant risk.
How does satellite imagery help monitor urbanization?
Remote Sensing enables us to extract information about surface characteristics based on how different features respond to electromagnetic radiation. The radiation may be:
Passive — primarily sunlight
Active — energy transmitted by the satellite itself
When this radiation interacts with the Earth’s surface, the reflected component is captured by the sensor. The amount and behaviour of this reflected energy depend mainly on:
The structural and biogeochemical properties of the surface, and
The physical and geometric characteristics of the wavelength used
Since the latter is already known, and since decades of scientific research have documented how different surfaces reflect radiation, we can infer the surface type using the captured signal. This is the foundational principle behind every Remote Sensing workflow—whether delineating urban areas, water bodies, vegetation, or distinguishing between categories such as healthy vs. distressed vegetation.
SAR imagery is acquired through active illumination—the satellite transmits microwave pulses (microwaves lie at the higher frequency-end of radiowaves in the electromagnetic spectrum) towards the Earth and measures their return, known as backscatter. SAR is preferred to Multispectral Imagery (acquired through passive illumination - solar radiation) for detecting built-up areas because:
Urban structures strongly reflect microwaves (single-bounce and double-bounce scattering)
Surrounding surfaces such as vegetation, water, or barren land reflect significantly less
Microwaves penetrate cloud cover, haze, and aerosols, unlike optical wavelengths
SAR works consistently at night or during adverse weather, enabling reliable temporal comparisons
This high coherence across acquisitions makes SAR ideal for multi-temporal change detection (refer to the technical explanation on the utility of the high coherence characteristic of radio waves).
The detailed method of extracting urban footprint from SAR imagery can be viewed here. Below, I share some of my own outputs.
The Slider is best viewed on PC
Slider 1: To the left is a RGB composite over a cross-section of New Delhi derived using the Mean, Difference and Coherence between two Sentinel-1 SAR images acquired in the month of October 2020. The underlying terrain can be seen in the natural-color Google Earth basemap on the right
(The slider depicting the RGB composite of the entire study area can be viewed here).
Interpretation of colours:
Red — Very high coherence, very low backscatter
Bare soil / rocky terrain
Surfaces that remain unchanged between acquisitions and reflect almost no microwaves
Yellow — Very high coherence, high backscatter
Built-up areas
Structures that remain unchanged and reflect large amounts of microwaves (single and/or double-bounce scattering)
Green — Low coherence, low backscatter
Vegetation / forested areas
Changes in leaf orientation reduce coherence; vegetation inherently has low microwave reflectance
Blue — Very low coherence, very low backscatter
Water bodies, or agricultural fields that have been freshly ploughed
Slider 2: Here, the yellow pixels from the RGB composite were isolated to delineate the built-up areas (white pixels), with all other surfaces masked out (black pixels). The corresponding basemap for reference appears on the right.
Slider 3: Two RGB composites of the same study area—January 2016 vs. October 2020—enable near-five-year change detection.
Can you spot areas where the urban landscape has expanded?
One such location appears as a cluster of new yellow pixels in the top-centre region of the 2020 composite (north-west of the Ghaziabad label).
Upon inspecting this region in Google Earth’s Historical Imagery, the newly emerged yellow cluster corresponds precisely to:
Slider 4: Google Earth Basemap from 2016 (left) and current Google Earth Basemap in 2020 (right)
WUPPTCL Ataur Power Substation (constructed between 2016 and 2020)
Radar Remote Sensing is remarkable in its ability to reveal such transitions.
You can download the two RGB composite KMZ files here and visualize them in Google Earth (PC version) to explore how New Delhi’s urban footprint has evolved.
Malappuram in Kerala, India, was ranked the fastest growing city in the world by The Economist, based on the total percentage change in population forecasted between 2015–2020—a staggering 44%. Since population growth typically necessitates an expansion in built-up area, this presented an excellent opportunity for me to extract and compare Malappuram’s Urban Footprint around this timeline-
Slider 5: Urban Footprint over Malappuram and adjoining areas — October 2016 (left) vs. October 2020 (right)
You may download the KMZ files here if you wish to view or compare the RGB composites and Urban Footprint layers yourself.
As anticipated, the increase in urban areas (white pixels) is clearly visible. Because we know the spatial resolution of Sentinel-1 imagery in Interferometric Wide Swath mode (each pixel covers 5 m × 20 m = 100 sq. m), we can compute the change in net built-up area over the four-year period-
The 2016 Urban Footprint contains approximately 17,000 white pixels.
whereas the 2020 Urban Footprint contains roughly 20,000 white pixels.
An increase of 3,000 pixels corresponds to:
300,000 sq. m
0.3 sq. km of additional built-up area
Equivalent to 21+ Eden Gardens cricket stadiums in size
While not directly proportional, it is reasonable to infer that the 44% projected population growth for Malappuram aligned with an approximate 18% increase in built-up area.
Do note that the Urban Footprint reflects the horizontal extent of development—not the vertical expansion through multi-storey buildings.
A single ~1 GB Sentinel-1 SAR dataset contains a wealth of information—Amplitude, Phase, Elevation, Orientation, and detailed metadata about the satellite’s position. The magic lies in how these data points are processed and interpreted. It is remarkable that we can derive such meaningful insights from nothing more than reflected microwave energy stored in tiny pixels.
Explore my other Remote Sensing workflows here.
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