# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
## 3 Earth Observation
### Natural Disasters  
Nanosatellites play a critical role in disaster monitoring and management due to their ability to provide timely, high-resolution imagery, and frequent revisit times. Their low-cost design and flexibility allow for the deployment of constellations capable of real-time monitoring during emergencies such as earthquakes, floods, oil spills, and volcanic eruptions. This Section reviews studies showcasing the use of nanosatellites in disaster response and mitigation efforts. Deploying a constellation of CubeSats can considerably reduce the revisit time over remote areas, allowing for more frequent data collection and aiding in disaster mitigation efforts. CubeSats can also be used for disaster management, particularly in providing real-time, continuous information transmission to Disaster Management Centers [21]. Also, [PERSON] et al. [22] propose a constellation of four low earth orbit 6U nanosatellites with a spatial resolution of 62 m and the ability of Earth covering in 72 h for disaster monitoring in Africa and Asia. [PERSON] et al. [23] propose harnessing nanosatellite technology to address Indonesia's vulnerability to natural disasters. The authors emphasize the prospective applications of nanosatellites, particularly in addressing Indonesia's unique challenges, such as geophysical and weather-related disasters. Additionally, they highlight the potential for nanosatellites to enhance early warning systems for disasters, facilitate Earth and space observation, support the Internet of Things (IoT) and communications infrastructure, enable asset tracking, and contribute to scientific and environmental programs. [PERSON] et al. [24] propose a novel approach using satellite imagery to effectively detect oil spill areas in marine environments. With oil spill accidents posing significant threats to ocean ecosystems, rapid response is essential, and satellite images are crucial, for such efforts. Leveraging a miniaturized satellite equipped with an optical sensor offers distinct advantages, including shorter imaging periods due to the operation of numerous satellites. Focusing on a specific incident near Ras Al Zour, Kuwait, on 10 August 2017, the authors \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method Used** & **Evaluation Metrics** & **Reference** \\ \hline Developed Theia, a scientific-grade multispectral imager designed for nanosatellites. & Achieved 5\% radiometric accuracy, MTF above 0.13 at Nyquist frequency. & [14] \\ \hline High-resolution PS nanosatellite data, NDVI time series. & 98\% accuracy (RF model), Strong correlation between NDVI and RF. & [15] \\ \hline Time-series satellite data, NDVI values from surface reflectance imagery. & Significant correlations with ground data, Monitoring crop phenology. & [16] \\ \hline CubeSat technology alongside modified PT-JPL model for ET estimation. & Real-time agricultural monitoring, Rapid identification of issues. & [17] \\ \hline PS satellite data, maximum vegetation indices for AGB estimation. & Positive correlation between AGB and cash-crop yield. & [18] \\ \hline sUAS and PS imagery for forage production mapping. & Remarkable accuracy in forage production prediction. & [19] \\ \hline Normalized vegetation indices with high-spatial resolution PS imagery for rice disease mapping. & Successful classification of rice diseases. & [20] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: EO methods used for vegetation monitoring. applied Artificial Neural Network (ANN) techniques to analyze PS satellite images. Despite challenges such as sunlight effects and interference from dust areas, the study successfully detected oil slick areas through a multi-step image processing approach. The results show an overall accuracy of 82.01% in oil slick classification by employing pre-processing techniques to mitigate these limitations and utilizing ANN-generated probability maps, a valuable tool for timely and accurate oil spill detection and response efforts. [PERSON] et al.