Environmental modeling involves the processes of analyzing the interactions between geophysical, biological, economic and social systems. It helps us understand the natural system and predict how the natural system responds to climate change and human activities. Model-driven methods have remained dominant, especially for traditional and classical problems that deal with Big Earth Data that come from various data sources with different formats and scales, leading to tremendous computational challenges. With the accumulation of knowledge about the natural system, environmental models have become increasingly sophisticated. Therefore, data and knowledge-driven environmental modeling has attracted attention in many application areas, such as disaster management, environmental monitoring, climate change, and environmental health management.
With recent breakthroughs in data acquisition technology, Big Earth Data can provide globally established, multi-source, multi-scale, high-dimensional, heterogeneous, high-resolution, highly dynamic datasets. At the same time, artificial intelligence has been progressing thanks to the advanced algorithm architectures, powerful computing devices, and large available datasets. The convergence between Big Earth Data and artificial intelligence could open a new era for the advance of environmental modeling. Big Earth Data Intelligence provides new opportunities to understand the environmental modeling of earth systems which helps to resolve problems, such as spatiotemporal complement, data assimilation, uncertainty, and model calibration.
With these issues in mind, it is time to present the current state-of-the-art theoretical, methodological, and application research on Environmental Modeling using Big Earth Data Intelligence. This Research Topic encourages articles that are related to the topic of environmental modeling and geospatial big data analytics. We welcome high quality contributions proposing solution and approaches in the domain of the following topics:
• The potential of using Big Earth Data for improving the understanding of natural system and environmental modelling.
• Fundamental theories for Big Earth Data Intelligence, such as data representation, data cleaning, geospatial artificial intelligence, spatial correlation analysis, etc.;
• Multidisciplinary integrated environmental model development;
• Data and parameters processing for environmental modeling;
• Big Earth Data for data assimilation;
• Model calibration using Big Earth Data;
• Uncertainty analysis of environmental modeling;
• Big Earth Data Intelligence in large-scale environmental remote sensing;
• Cyberinfrastructure and CyberGIS;
• Spatial decision-making in environmental management.
Environmental modeling involves the processes of analyzing the interactions between geophysical, biological, economic and social systems. It helps us understand the natural system and predict how the natural system responds to climate change and human activities. Model-driven methods have remained dominant, especially for traditional and classical problems that deal with Big Earth Data that come from various data sources with different formats and scales, leading to tremendous computational challenges. With the accumulation of knowledge about the natural system, environmental models have become increasingly sophisticated. Therefore, data and knowledge-driven environmental modeling has attracted attention in many application areas, such as disaster management, environmental monitoring, climate change, and environmental health management.
With recent breakthroughs in data acquisition technology, Big Earth Data can provide globally established, multi-source, multi-scale, high-dimensional, heterogeneous, high-resolution, highly dynamic datasets. At the same time, artificial intelligence has been progressing thanks to the advanced algorithm architectures, powerful computing devices, and large available datasets. The convergence between Big Earth Data and artificial intelligence could open a new era for the advance of environmental modeling. Big Earth Data Intelligence provides new opportunities to understand the environmental modeling of earth systems which helps to resolve problems, such as spatiotemporal complement, data assimilation, uncertainty, and model calibration.
With these issues in mind, it is time to present the current state-of-the-art theoretical, methodological, and application research on Environmental Modeling using Big Earth Data Intelligence. This Research Topic encourages articles that are related to the topic of environmental modeling and geospatial big data analytics. We welcome high quality contributions proposing solution and approaches in the domain of the following topics:
• The potential of using Big Earth Data for improving the understanding of natural system and environmental modelling.
• Fundamental theories for Big Earth Data Intelligence, such as data representation, data cleaning, geospatial artificial intelligence, spatial correlation analysis, etc.;
• Multidisciplinary integrated environmental model development;
• Data and parameters processing for environmental modeling;
• Big Earth Data for data assimilation;
• Model calibration using Big Earth Data;
• Uncertainty analysis of environmental modeling;
• Big Earth Data Intelligence in large-scale environmental remote sensing;
• Cyberinfrastructure and CyberGIS;
• Spatial decision-making in environmental management.