# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
## 3 Earth Observation
### Vegetation  
Vegetation monitoring is one of the primary applications of nanosatellites, offering high-frequency and high-resolution data for agriculture, forestry, and ecological studies. Traditional Earth observation satellites have provided significant advancements; however, nanosatellites, such as the PlanetScope constellation, enable cost-effective, real-time monitoring with unprecedented detail. This Section explores various studies that demonstrate the use of nanosatellites in vegetation mapping, crop growth analysis, and disease detection. [PERSON] et al. [14] proposed a solution to address the limitations in acquiring high-quality remote sensing data for various applications, including agriculture, land-cover change, and vegetation phenology. The authors developed Theia, a scientific-grade multispectral imager designed to complement larger EO satellites with nanosatellites. Theia's innovative design ensures a remarkable 5% radiometric accuracy and maintains a modulation transfer function (MTF) above 0.13 at the Nyquist frequency, which is crucial for preserving image quality. Reflective optics with an aluminum optomechanical framework and advanced coatings to suppress stray light have been employed in the imager, ensuring superior performance in a compact form factor that fits within 0.6 CubeSat Units and weighs only about 600 g. Furthermore, the imager incorporates a post-launch calibration system for continuous monitoring, ensuring sustained instrument quality. Although challenges regarding MTF optimization exist, the authors provide insightful solutions, demonstrating the feasibility of creating cost-effective nanosatellite imagers capable of revolutionizing remote sensing missions. [PERSON] [15] used a high-resolution image dataset from the PS constellation focused on the Al-Qassim region, Kingdom of Saudi Arabia (KSA), utilizing the high-resolution PS nanosatellite data to assess agricultural spatiotemporal dynamics, employing Normalized Difference Vegetation Index (NDVI) time series spanning 29 months. The investigation incorporated GRACE and GRACE-FO data to estimate water usage for vegetation, revealing significant relationships between vegetation, environmental variables, and water extraction. Furthermore, the Random Forest (RF) model used to classify the vegetation showcased promising accuracy (98%). Also, Multinomial Logistic Regression (MLR) was used to compare the classification performance between NDVI and RF. The results show a strong correlation with similar results between NDVI and RF. [PERSON] and [PERSON] (2016) presented a method for tracking crop growth and development using time-series satellite data with high spatial resolution. Their main focus was on winter wheat phenology using images from the PS constellation. The authors have conducted a rigorous analysis and determined that NDVI values derived from surface reflectance imagery exhibit significant correlations with ground data obtained from a manual active GreenSeeker optical sensor. By comparing NDVI values at different levels of crop fertilization, this study clarifies the efficacy of the proposed approach in monitoring temporal changes in crop growth. The results underscore the potential of utilizing simplified image processing techniques, particularly those based on surface reflectance values and adjusted GreenSeeker data, for accurate and efficient monitoring of crop phenology and nutritional status. [PERSON] et al. (2017) propose using CubeSat technology alongside a modified Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model to advance the estimation of evapotranspiration (ET) in precision agriculture. The results show insights into irrigation efficiency and highlight significant water losses. Furthermore, the findings highlight CubeSat's potential for real-time agricultural monitoring, enabling rapid identification of field issues and treatment evaluation at unprecedented levels of detail. [PERSON] et al.