# A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment
## 3 Overview of Flood Sensor Development
### Flood Satellite Remote Sensing Sensors  
As the performance of sensors continues to improve, satellite remote sensing is gradually providing an alternative over traditional measurements with low costs, more frequent revisit intervals, and wider coverage [99]. It is seen that the emergence of satellite remote sensing has solved the problem of inaccessibility and measurement of flooded areas, and many studies on flood monitoring, assessment, early warning and prediction have been carried out using remote sensing technology. However, it is undeniable that the problem of temporal and spatial resolution has hindered the development of remote sensing technology for a long time [100]. At present, satellite remote sensing is mainly classified into optical remote sensing, infrared remote sensing, and microwave remote sensing. These three types of remote sensing sensors have been increasing in measurement accuracy and pursuing environmental immunity, and their monitoring characteristics apply to different flooding situations (Table 4). The passive multispectral scanner (MSS) on Landsat provided 80 m resolution images for early flood-prone area studies in the U.S. In the 1980s, the Landsat thematic mapper (TM), with a 30 m resolution and an 18-day revisit cycle, became the primary data source for flood monitoring and boundary delineation [101]. Later, the SPOT satellite's X spectral mode enabled multispectral imaging for small-area flood monitoring. The advanced very high-resolution radiometer (AVHRR) has a low spatial resolution but a high temporal resolution with a large spatial range. These characteristics have significantly improved real-time flood detection and monitoring, particularly in underdeveloped areas, due to its cost-effectiveness and minimal technical requirements. In addition, sensors such as the multispectral imager (MSI), enhanced thematic mapper (ETM+), and operational land imager (OLI) have played an important role in flood management activities, especially MODIS, providing globally consistent and independently verifiable data in transboundary river systems [102]. In the early 21 st century, hyperspectral sensors enabled optical satellites to capture a broader range of spectral bands, enhancing land cover classification and environmental monitoring. Between the 1980s and 1990s, various types of satellite sensors were developed, including optical, radar, and infrared sensors, for different remote sensing missions. Optical remote sensing is highly effective, but its ability to penetrate cloud cover is limited. During flooding events, cloud cover is often dense, particularly during monsoon seasons, which poses significant challenges. Synthetic aperture radar (SAR), however, can penetrate cloud cover, making it more suitable for these conditions [103]. The launch of civil SAR satellites, such as ERS-1, ERS-2, and RADARSAT, contributed to the increased popularity of SAR sensors in flood management applications [104]. The Key Earth observation programs include the German TerraSAR-X and TanDEM-X, the Italian COSMO-Skymed, and the Canadian RADARSAT-2. RADARSAT has proved to have the capability to accurately assess and identify inundated areas and to take images on specified dates and locations, thus enabling successful flood monitoring and mapping [105]. The low resolution of the special sensor microwave imager (SSM/I) limits its use in analysis. In the 21 st century, Sentinel-1 became widely used after the launch of Europe's Copernicus program. The U.S.-Japan Tropical Rainfall Measuring Mission (TRMM), which launched in 2015, marked a milestone in space-based precipitation estimation, later succeeded by the higher-resolution Global Precipitation Measurement Mission (GPM). The use of LiDAR data with sub-meter spatial resolution has proven valuable for urban modeling; however, it is more expensive compared to SAR imagery. LiDAR effectively identifies vertical differences in terrain and flood depths, making it suitable for flood mapping in flat terrains.