How can remote sensing improve flood mapping accuracy?

An optical sensor is an electronic detector that converts light or changes in light into electronic signals. Optical sensors are used in many industrial applications, for example: a light bulb that automatically turns on in response to darkness. Position sensors are activated when an object interrupts the light beam.The optical sensor can be found in the field of energy to monitor structures that generate energy, generate, distribute, and convert electrical energy.

All countries are very different. There may be places where there are frequent floods, but at the same time there is rarely precipitation during floods. Probably, in this case, optical sensors would be ideal. Also, I think an optical sensor is a good option if you just need to collect statistics at some frequency. Then this solution will be quite acceptable.

The most common use of optical sensors now is in digital cameras and smartphones. Everybody uses some kind of optical sensors today to create his/her photos. In remote sensing they capture light from UV through visible up to infrared. The selection of light type is made considering the goal. E.g. there are chemicals in atmosphere (like CO2, SO2 etc.) that are absorbing some special wavelengths, the same goes for healthy/drying vegetation or clean/polluted water and many others. Therefore correct selection of light sensors allows us to distinguish selected phenomena. In weather we can distinguish e.g. different types of clouds, air humidity or pollution. In land services the amount of vegetation and its changes in time.

Enhance flood mapping accuracy with remote sensing by integrating high-resolution satellite imagery, LiDAR data, and advanced sensors to provide real-time, detailed information on flood extent and dynamics.

GPR or ground penetrating radar, can penetrate deep into the soil All the information we need from there is to give us that by using electromagnetic waves, we can get a relatively accurate estimate of the structure and texture of underground layers and man-made structures. In the method of using penetrating radar, electromagnetic waves with different frequencies are sent into the ground Then the reflection of these waves is analyzed. Different frequencies are produced, transmitted and received by antennas with different center frequencies.In civil engineering, it determines the location of underground pipes and cables, etc.

In my personal experience, radar based sensor is a very good solution for monitoring water levels and floods. It is relatively inexpensive, easy to operate and absolutely unaffected by day and night, precipitation, storms, etc. The problem is that microwave radars do not have a very high resolution, and ultrasonic radars do not have a very good range. When using microwave and ultrasonic radars simultaneously, you can obtain fairly accurate data

Unfortunately, in the section of this topic, I would strongly discourage the use of lidars. Like optical sensors, they are very susceptible to the influence of precipitation (rain, snow) and it is not entirely clear how waves on the water surface will affect the measurement accuracy. Here it is worth mentioning the high cost of lidar.

Data fusion is one of the most cost-effective approaches for measuring water levels and preventing floods. All countries in the world have their own global weather centers, where you can request a history of observations of precipitation or water levels in rivers. Using and analyzing this data will tell you which month of the year you should start monitoring and which month you should end it. This information can also help in choosing the right sensor

Machine learning is a subfield of artificial intelligence that allows machines to learn without explicit programming. Machines use data and experience to improve performance and make decisions without the need for detailed programming. Common techniques of this method include self-organizing maps, nearest neighbor mapping, k-means clustering, and singular value decomposition. Also, these algorithms are used to segment text topics and identify sparse data.

Artificial intelligence and machine learning are technologies of the future that can be applied today. For example, the Google company has created a special Flood Hub, which not only shows the current water level in the river, but also helps predict what will happen to it next. Therefore, having a large amount of data and a good mathematical model, you can get away from all expensive sensors. However, I would not recommend completely abandoning data fusion just yet.