Flood Monitoring the Remote Sensing Way

Flood Monitoring the Remote Sensing Way

By: Upasana Dutta, Yogesh Singh, Manoj Khare and Sandeep K Srivastava
Remote sensing technologies serve as a management tool for authorities to forecast and issue early warning of floods. A Flood Response System developed by C-DAC focuses on near-real time flood monitoring in the Brahmaputra valley.
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Among various natural or humanly created disasters, floods are the most common and widespread. Growing exponentially during the last decade, they are undoubtedly the most destructive phenomena across the world. Floods are a consequence of the increasing frequency of heavy rain, changes in upstream land-use and a continuously increasing concentration of population and assets in flood prone areas. Floods can occur anywhere as a result of heavy rain—from small flash floods to sheets of water covering huge areas of land. All flood plains are vulnerable and heavy storms can cause flash flooding in any part of the world. They are broadly categorised as fluvial (riverine), flash, seasonal, coastal, estuarine, urban, snowmelt, and ice and debris-jam floods.

Like most parts of the world, India too is vulnerable to floods taking a toll on its economic, social and human resource potential, affecting the growth, development, productivity and macro-economic performance in the long run. According to the National Remote Sensing Centre (NRSC), about 40 million hectares of land in the country is prone to floods as per the National Flood Commission. The annual average of land area and crop area affected due to flood is about 18.6 million and 3.7 million hectares respectively. The average loss in financial terms is about INR 13,000 million. A recent report by the United Nations, ‘The human cost of weather-related disasters’, reveals that in the last twenty years, 157,000 people have died globally as a result of floods. The Report also says that between 1995 and 2015, floods affected 2.3 billion people (UNISDR, 2015), which accounts for as high as 56 per cent of all those affected by weather-related disasters (Flood List, 2016).

According to the Indian National Remote Sensing Centre of the Indian Space Research Organisation, Brahmaputra, Ganga and Meghana River basins in the Indo-Gangetic-Brahmaputra plains in North and Northeast India are the most flood-prone areas in India carrying 60 per cent of the nation’s total river flow. Floods are mostly precipitated in these regions during the southwest monsoon from June to September. Along with heavy precipitation, snowmelt adds to the woes of the river. Most of the Himalayan rivers bring heavy sediment load from the catchments, which because of inadequate carrying capacity of the rivers cause floods, drainage congestion and erosion of river-banks downstream. Apart from the monsoons, cyclones, cyclonic circulations and cloud bursts also cause flash floods, a quicker and a more destructive version of floods. Also, the fact that some of these tumultuous rivers originate in neighboring countries adds complexity to the flood problem.  

The control and harnessing of floods are often beyond effective human intervention and complete protection from flooding is rarely a viable goal (Moore, Bell and Jones, 2005). Flooding is unique in the sense that it has a very high degree of predictability, both in terms of short and long term durations. Although it is not possible to control flood disasters in totality, they can be managed by adopting suitable structural and non-structural measures and monitoring.

For planning any flood management measure, latest, reliable, accurate and timely information is required. In this context, satellite remote sensing coupled with weather forecasting and hydrological modeling plays an important and pivotal role.

Flood monitoring includes forecasting, movement, inundation and flood plain zoning. It also requires a variable degree of responses from local/municipal authorities, transport and communications operations and emergency services. Flood forecasting has to provide information to these users both for preparation and response. At the extreme level, it is a part of a disaster management plan. The nature of flooding events is also important, particularly whether floods are regular or irregular in occurrence.  

One such study was developed by the Centre for Development of Advanced Computing (C-DAC)—Flood Response System (FRS) and tested in the North Lakhimpur district of Assam. FRS is a network-enabled solution, developed using open source software. The system has query based flood damage assessment modules with outputs in the form of spatial and statistical databases including maps. FRS uses microwave data to utilise its cloud penetration, all-weather and day-and-night data acquisition capability. Mathematical transformation and thresholding-based microwave data analysis were used for automatic extraction of the inundated areas. This helped in gathering near real-time information on a holistic manner.

FRS effectively facilitates and maintains a steady flow of information at all levels regarding the management of post-disaster activities, such as area affected and infrastructural concerns. Other ancillary information is also used to derive flood response information for the end-user to operate in near-real time.

FRS is enriched with GIS maps and village level information including both census and infrastructure database. The resultant data and information generated therein are useful in planning and executing the emergency response measures in an effective manner. It helps map flood extent and duration, monitoring of lands inundated throughout the flood period and assess flood damage.

Space technology has also made substantial contributions to flood management and monitoring. The earth observation satellites including both geostationary and polar orbiting satellites provide comprehensive, synoptic and multi-temporal coverage of large areas in real time and at frequent intervals. Remote sensing techniques used to measure and monitor the areal extent of the flooded areas, efficient rescue efforts and quantifiable estimates of the amount of land and infrastructure include satellite-derived rainfall to infer flooding conditions. Satellite-derived land-cover observations are also used to detect flood water on the previously dry land surface. In addition, the identification and mapping of floodplains abandoned river channels and meanders for planning and transportation routing during floods can be effectively carried out using remote sensing-GIS techniques. Incorporating remotely sensed data into a GIS allows for quicker calculations and efficient assessment of water levels, damage, and areas facing potential flood danger.  

Apart from providing direct information about flooding, remote sensing data can also be integrated with flood models to augment the amount and type of information available for efficient flood management. Landsat, TRMM, GPM, Terra, Aqua, SMAP, GRACE are few of the satellites which are dedicatedly used for flood monitoring and prediction. RADARSAT and ENVISAT offer a high turnaround interval—from the acquisition of data to image delivery to the user on the ground. The land/water interface is quite easily discriminated with Synthetic Aperture Radar (SAR) data, allowing the flood extent to be delineated and mapped. MODIS data measure the extent of water with high temporal resolution, well designed for specific cases such as shallow and temporary lakes and floodplains mainly in the arid zones. Flooding conditions are relatively short term and generally occur during inclement weather. Optical sensors, although typically having high information content for this purpose, cannot penetrate through the cloud cover to view the flooded region below. However, SAR can achieve regular observation of the earth’s surface even in the presence of thick cloud cover.

Figure remote sensing
Fig. 1: Remote Sensing Capabilities in Hydrodynamic Models of Flood

Though flood monitoring can be carried out through remote sensing from global scale to storm scale, it is mostly used on the storm scale using hydrodynamic models by monitoring the intensity, movement and propagation of the precipitation system to determine how much, when and where the heavy precipitation is going to move (Fig. 1). Meteorological satellites detect various aspects of the hydrological cycle, precipitation rate, and accumulations, moisture transport, and surface/soil wetness. FENGYUN 4A, GOES-R, YUNHAI 1, HIMAWARI 9, SCATSAT 1, INSAT 3DR, ELEKTRO-L 2, MGS 4, etc., are few of the weather satellites launched between 2015 till the end of 2016 (N2YO, 2017).  NOAA Advanced Very High-Resolution Radiometer (AHVRR) allows for a family of satellites upon which flood monitoring and mapping can almost always be done in near real time. High-resolution infrared (10.7 microns) and visible are the principal data sets used. The wetness of the soil due to a heavy rainfall event or snow-melt is another useful parameter for flood (flash flood) guidance. For such analysis, SSM/I data from the DMSP prove useful.


With a proliferation of free earth observation data and an abundance of free and open source hydrodynamic models, flood monitoring has witnessed more research and the resultant solutions are seen to be more efficient. However, with India’s varied topography, a humongous network of rivers and streams, unique population problem and a ubiquitous data crunch, there is an obvious need to fully grasp the capacity and role of remote sensing in managing and monitoring disasters such as floods.


Centre for Development of Advanced Computing (C-DAC) (undated). Flood Response System. 

Flood List. 2016. UN – 1995 to 2015, Flood Disasters Affected 2.3 Billion and Killed 157,000. 

Jeyaseelan, A.T. 2003. Droughts & floods assessment and monitoring using remote
sensing and GIS, Satellite Remote Sensing and GIS Applications in Agricultural
Meteorology. pp 291-313.

Moore, R. J., A. V. Bell and D. A. Jones. 2005. Forecasting for flood warning. Comptes Rendus Geosciences, 337 (1–2): 203–217.

N2YO. 2017. Weather Satellites. 

National Remote Sensing Centre. Floods

The United Nations Office for Disaster Risk Reduction (UNISDR). 2015. The human cost of weather-related studies. 

Authors are Senior Technical Officers, Joint Director and Associate Director, respectively Emerging Solutions and e-Gov group, Center for Development of Advanced Computing (C-DAC), Pune. manojk@cdac.in

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