looking at satellite data, it seemed totally impossible to me that a spacecraft that orbits the Earth at a distance of several hundred kilometers can actually see a flooded street in my city. Floods are very disorderly, dirty, and generally unpredictable. However, radar satellites have become very sensitive in the last couple of years, and algorithms have become very intelligent, so now it is possible to monitor the water that is flowing through the houses, fields, and riverbanks. I wrote this article to explain how the trick works. It is not the perfect “AI + satellites = magic” version, but the real one, from the perspective of a person who has spent numerous nights looking at SAR (Synthetic Aperture Radar) images full of noise, trying to figure out what they really mean.
My core message: to be able to locate floods in real-time and to be able to rely on such maps, one has to move beyond optical images and understand the geometry of SAR backscatter. India’s RISAT (Radar Imaging Satellite) program is an excellent example of how physics-based data pipelines can give the stability and weather independence required for the timely delivery of the flood intelligence that can be used in situations of extreme catastrophes, such as the monsoon season.
The Strange Beauty and Physics of SAR Data
Most people envision satellites as photo-taking devices, but SAR is quite different from a camera. It does not record light; in fact, it generates its own light. In the case of a satellite such as RISAT, it is an active operation in which the satellite sends a concentrated beam of microwaves to the Earth and records the very small part of the energy that is reflected back to it, which is called backscatter.
Why Water Appears Dark (The Specular Effect)
The brightness of the image produced is not a measure of visible light, but an account of how the radar energy is changing through interaction with the surface below. Such an interaction depends on how rough and what the properties of the surface are in relation to the radar’s wavelength.
- Dry, Rough Surfaces (Vegetation, Urban Areas) : The radar waves scatter in many different directions when they hit a rough surface, like light hitting a crumpled piece of foil. A large part of this scattered energy returns to the satellite → Bright Pixels.
- Smooth Water Surfaces : A calm water surface is like a very smooth mirror. When radar waves hit it, they reflect almost all the energy away from the satellite, just as a mirror reflects light in a single direction. Only a very small amount of energy is sent back to the sensor → Dark Pixels (indicating very low backscatter).
Such an ability to penetrate darkness, rain, dust, and smoke is what makes SAR irreplaceable for disaster response in cloudy, high-moisture environments.
The Core Flood Mapping Pipeline: From Echo to Map
A SAR satellite image is not directly available from the download. An average RISAT flood detection process is a well-organized, physics-based data science pipeline. Any error made at the beginning can spoil all the results that follow, hence the careful processing is very important.
1. Preparing the Radar Data
Essentially the first step is to change the satellite’s raw data in such a way that it expresses meaningful backscatter measurements. This step makes the numerical values in the picture a true representation of the Earth’s surface that can be compared with other pictures reliably.
2. Reducing Image Noise
Speckle is a granular, salt-and-pepper-like noise that SAR images have inherently. This noise should be lessened in a way that does not blur the outline of the land, in particular, the sharp boundaries between land and water.
The Challenge: Inappropriate strong use of a noise reduction method may delete small flood details or change water boundaries. An insufficiently strong method leaves too much noise that may cause errors in the identification of flooded areas.
The Solution: It is a clear result of the image, which is suitable for analysis, because specialized filters are brought in to smooth out the noisy parts while preserving the important edges.
3. Detecting Change: The Algorithmic Centerpiece
Essentially, flooding is a major change in the reflectivity of the surface to radar energy—from a bright-scattering land surface to a dark-scattering water surface. So, a comparison of a radar image taken before the flood with one taken after allows us to determine the exact locations of inundation.
One of the most effective methods is to determine the change in brightness between the images taken before and after the flood. Those locations that have changed from land to water will have a huge difference, thus disclosing the flooded area almost entirely
4. Isolating and Refining the Flood Zones
The last operations are all about finding the pixels that correspond to the flooded areas and ensuring the map is correct:
- Thresholding: An automatic method locates those pixels whose change is significant enough to be considered ‘flooded’. Thus, a first map of the flooded areas is obtained.
- Use of Additional Data: To refine the accuracy, we resort to different types of geographical data. For instance, we take out the zones that are always under water (like permanent lakes or rivers) and do not consider very steep slopes (which can be sometimes wrongly interpreted as dark areas in radar images due to shadows). This provides the means to get rid of the false detections and makes sure that the final flood map is accurate.
The Nuance of Radar Settings and Human Intervention
One of the small decisions which has more impact than the algorithm is the choice of the correct radar settings, especially the manner in which the radar waves are sent and received (known as polarization).
Various polarization configurations can reveal different aspects of the terrain. When it comes to flood monitoring, a certain polarization setting (frequently referred to as VV polarization) is usually selected since it results in the greatest contrast between the dark signal coming from the water and the light signal coming from the land around it.
Why Human Judgment Still Tops Pure AI
In current operational flood mapping, traditional methods have been found to produce more reliable results than complex artificial intelligence models. This is mainly because traditional methods are more consistent and adaptable.
- The AI Challenge: General-purpose AI models have a hard time dealing with the inherent noise in radar data. Additionally, these models fail when they are relocated to a new geographic area. For example, an AI model trained on floods in a flat, urban city might not be applicable in a hilly, agricultural river delta.
- The Human Edge: Even though the same satellite data is used, two expert analysts may come up with slightly different flood maps. This is not inaccuracy;rather, it is nuance. The analyst applies their knowledge to:
- Adjust the flood zones according to the local setting (recognizing that a flooded rice field would look different from a flooded road).
- Weigh the necessity of finding all flooded areas against the possibility of identifying non-flooded areas as flooded (false alarms).
Whereas AI is gradually gaining ground, it is mostly in a helping capacity. Advanced methods utilize the dependable physical principles of radar along with AI to not only narrow down flood boundaries but also to elevate the level of detail. By doing so, the comprehension of radar physics is still the primary consideration while AI is used to enhance the end product.
Conclusion
The RISAT program is one such initiative that essentially accomplishes this by providing consistent and reliable data which is instrumental in transforming the flood chaos into a manageable and strategic geospatial intelligence. At present, flood mapping is essentially the point of convergence of the latest developments in physical models, data processing, and the application of geo-spatial expertise by human agents.
Understanding and interpreting the backscatter patterns is the key step in moving from a mere visual of the catastrophe to a deep understanding of the extent and the flow of the disaster, thus allowing for a timely intervention. Besides, RISAT and similar initiatives should not be considered as mere technological devices stationed somewhere in the space, but rather as the indispensable instruments that sustain the harmonious functioning of the analyst and responder ecosystems. That is, the quicker and more precise our maps become, the relief teams are able to mobilize and execute their tasks in a much shorter time—being a perfect example of how data science can be a direct asset to humanity.
Thank you for visiting and reading.
References
- ISRO,“RISAT-1A Mission Overview,” (2022), ISRO Website.
- ESA, “Sentinel-1 SAR Processing Tutorials,” (2021), ESA Documentation.
- Jain, Kumar, Singh.“SAR-Based Flood Mapping Techniques: A Review,” (2020), Remote Sensing Applications.
- NRSC, “Flood Hazard Atlas of India, ” (2019), National Remote Sensing Centre Report.
- Schumann & Moller,“Microwave Remote Sensing of Floods,” (2015), Journal of Hydrology.