# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
## 3 Earth Observation
### Specialized Applications
Leveraging upgraded hardware and experimental protocols, BioSentinel stands as a crucial tool in enhancing our understanding of the biological impacts of long-term deep-space missions, thus contributing to the safeguarding of astronaut health and performance. Moreover, the versatility of the BioSentinel and BioSensor instruments opens avenues for future exploration, with potential applications ranging from novel life-detection assays to astrobiology-focused missions, lunar rovers, and beyond, underscoring their significance in advancing scientific inquiry beyond Earth's confines. [PERSON] et al. [42] propose a design for a lunar remote sensing micro-nanosatellite equipped with autonomous Earth-Moon transfer capabilities. Integrating a compact yet high-resolution multispectral camera enables the satellite to achieve unparalleled imaging capabilities, capturing lunar surface details with a remarkable resolution of 0.2 m from an orbital altitude of 100 km. Furthermore, the radiation-resistant components and high-resolution camera showcased in this study hold promise for broader applications in lunar micro-nanosatellite missions, underscoring their significance in advancing space exploration technologies. [PERSON] et al. [43] propose a novel solution to address the evolving landscape of IoT connectivity, particularly in remote regions near the Equator, implementing a nanosatellite constellation as an innovative IoT communications platform, offering real-time data collection and remote command activation capabilities. The authors highlight critical considerations such as interoperability between satellite systems and IoT devices and the compatibility of IPv6 onboard satellites. Furthermore, a few nanosatellites deployed in Equatorial orbit can provide continuous coverage, offering a cost-effective alternative to traditional global coverage solutions. While current antenna technology may pose limitations, advancements in this area hold promise for enhancing the efficacy of the proposed constellation design in the future. [PERSON] et al. [44] propose a CubeSat mission, the SWEET (Sweet Water Earth Education Technologies), to monitor water quality in African countries. The constellation consists of four nanosatellites with hyperspectral sensors, with a spatial resolution proposed to be 250 m, enough to monitor 62 lakes in Africa with an average revisit time of 3.5 days. The mission aims to find harmful cyanobacterial blooms (cHABSs) that impact society, economy, and ecology. [PERSON] [45] propose generating a Digital Elevation Model (DEM) from PS imagery using multiple images from satellite overpasses. The results show a Normalized Median of Absolute Deviation (NMAD) value of 4.1 m for elevation differences between the generated DEM and LiDAR data. The proposed method can be used to monitor disasters. [PERSON] et al. [46] propose an approach to improve satellite image indexing, retrieval, and classification efficiency and accuracy using deep learning models. Using Deep Convolutional Neural Network (CNN) models, the authors achieve the multi-label classification of clouds, shadows, and land cover scenes in high-resolution satellite imagery at the scene level, thus allowing for faster performance, higher accuracy, and greater generalizability. The CNN models trained on PS imagery performed well when applied to both PS and Sentinel-2 imagery. Also, the models compare to state-of-the-art methods designed to classify clouds and shadows in Sentinel-2 imagery. Furthermore, the CNN models offer a valuable tool for identifying cloud, shadow, and dominant land cover classes in satellite imagery with resolutions similar to PS and Sentinel-2, enabling efficient and accurate analysis across diverse geographic regions. NASA's Jet Propulsion Laboratory (JPL) has been instrumental in advancing nanosatellite capabilities beyond Earth observation. For instance, the INSPIRE (Interplanetary NanoSpacecraft Pathfinder In Relevant Environment) project, led by [PERSON] et al.