Spatiotemporal Modeling and Environmental Prediction in Earth System Observation: A Review

Abstract

The continuous monitoring and precise prediction of Earth system dynamics are fundamental to understanding global environmental changes, managing natural resources, and mitigating the impacts of climate change and natural disasters. Over the past few decades, the proliferation of remote sensing technologies, terrestrial sensor networks, and advanced satellite constellations has led to an exponential increase in Earth observation data. This data is inherently characterized by complex spatial dependencies and temporal dynamics, necessitating the development of robust spatiotemporal modeling frameworks. This comprehensive review paper synthesizes the current state of spatiotemporal modeling and environmental prediction within the domain of Earth system observation. We systematically trace the evolution of modeling paradigms from traditional geostatistical approaches and classical time-series analysis to contemporary deep learning architectures and hybrid physics-informed neural networks. By examining critical preprocessing methodologies, data assimilation techniques, and predictive architectures, this paper highlights how modern computational frameworks handle the high dimensionality, nonlinearity, and multi-scale nature of environmental data. Furthermore, we evaluate the empirical performance of these models across diverse applications, including climate modeling, extreme weather forecasting, ecological monitoring, and urban air quality assessment. The review concludes by identifying persistent challenges, such as model interpretability, computational demands, and data sparsity in remote regions, while charting future directions toward the development of highly accurate, real-time digital twins of the Earth system.