Abstract
Advances in remote sensing technologies have revolutionized the monitoring of soil conditions in forest ecosystems, providing valuable insights into soil moisture, nutrient content, and degradation without requiring physical access to remote areas. This article explores the application of key techniques, including satellite-based L-band radiometry, UAV-enabled LiDAR, and visible–NIR spectroscopy, in assessing forest soil properties. Challenges such as canopy interference, spatial resolution limitations, and data validation are discussed, alongside innovative solutions like machine learning and high-resolution digital elevation models. Case studies highlight the effectiveness of remote sensing in addressing environmental and forestry challenges, such as tracking the effects of climate change, logging, and erosion. By integrating advanced imaging technologies with ground-based observations, remote sensing supports sustainable forest management, conservation practices, and ecological research. Future developments in sensor technology, data integration, and machine learning hold promise for even greater precision and scalability in forest soil monitoring.