New Methods and Applications of Vegetation Remote Sensing Monitoring

Accelerating climate change and ecosystem degradation demand advanced vegetation monitoring to support global sustainability goals, yet conventional remote sensing faces critical limitations: fragmented data sources, sparse 3D structural insights, and prohibitive costs for high-resolution surveys. New AI-integrated methodologies address these gaps—exemplified by Google’s AlphaEarth unifying multi-source data into 10m-grid planetary embeddings, China’s L-band interferometry enabling single-baseline forest 3D mapping, among others. These innovations directly serve urgent applications, with vegetation underpinning 30% of climate mitigation pathways (IPCC AR6).

Despite major advances, accurate and scalable vegetation assessment remains constrained by fragmented data streams, spectral–spatial trade-offs, persistent cloud contamination, and limited vertical-structure sampling. Therefore, establishing a scalable, AI-driven framework that fuses satellite, UAV, and ground observations is crucial to accelerating ecological governance, strengthening agricultural resilience, and advancing global carbon-neutrality initiatives.

This Research Topic welcomes original research articles, reviews, and meta-analyses published by Frontiers in Plant Science that advance the understanding of vegetation-climate interactions, with particular emphasis on:

• Merging multi-source remote sensing images, geographical big data, and artificial intelligence methods to achieve refined extraction or change detection of vegetation information

• Combining artificial intelligence and remote sensing technology, identify diseases and pests in forests, grasslands, and crops

• Integrating remote sensing and ecological models to quantitatively assess the carbon sequestration capacity of various ecosystems and to analyze their spatiotemporal dynamics

• Responses of vegetation structure and physiological function to climate change, focusing on changes in greenness and physiological traits and their underlying mechanisms

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Remote Sensing, Vegetation, Climate Change, Deep Leaning, Big Data

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.