Satellite-Based Environmental Monitoring: AI-Assisted Analysis for Land, Water, and Air Systems

Abstract

The convergence of satellite technology and artificial intelligence (AI) has revolutionized environmental monitoring, enabling real-time, high-resolution analysis of Earth's land, water, and air systems. This paper explores the evolution from traditional remote sensing to AI-driven paradigms, highlighting key orbital platforms such as Sentinel-5P, Landsat-8/9, and GHGSat for data acquisition. Foundational AI methodologies, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Vision Transformers (ViTs), are examined for their roles in feature extraction, time-series analysis, and global context capture. Applications span terrestrial land cover change detection and precision agriculture; hydrospheric water quality assessment, groundwater tracking, and cryospheric dynamics; and atmospheric greenhouse gas detection and air quality modeling. Technological optimizations like edge computing and image restoration address data bottlenecks, while socio-economic implications, including Explainable AI (XAI), the AI digital divide, and environmental footprints, underscore ethical considerations. Through a synthesis of emerging trends, the paper posits that AI-assisted monitoring fosters proactive sustainability but risks rebound effects and inequities, urging balanced policy frameworks for equitable planetary stewardship.