SAR and Stereo/Tri-Stereo Satellite Data in Assessment of Vegetation Conditions

The importance of RADAR lies in its non-invasive nature and its capability to monitor expansive areas. Additionally, RADAR sensors, such as Synthetic Aperture Radar (SAR), have advanced to deliver high-resolution imaging and the ability to penetrate vegetation, soil, and certain man-made structures. Realizing the significant contribution this technology holds in Earth observations, we propose this Research Topic on “SAR and stereo/tri-stereo satellite data in assessment of vegetation conditions”. SAR and stereo/tri-stereo satellite data have emerged as critical tools with all-weather, day-and-night capabilities that are exceptionally well-suited for vegetation assessment. Their ability to penetrate vegetation canopies can provide detailed structural information about plant biomass, vegetation health and structure of vegetation etc. Stereo and tri-stereo satellite data offer 3D reconstructions of vegetation landscapes, adding depth to our understanding of vegetation dynamics, canopy structure, and ecosystem functions. Together, SAR and stereo data provide complementary insights, enabling more accurate and comprehensive monitoring of vegetation health, and carbon storage over time.

This Research Topic aims to bring together the latest and novel research on how SAR and stereo/tri-stereo satellite data can be applied to assess various vegetation-related parameters. The collection will highlight methodological advancements and case studies that demonstrate the potential of these technologies in monitoring vegetation conditions across diverse ecosystems. Inclusion of novel methodologies and case studies, will provide valuable insights for researchers, practitioners, and policymakers involved in vegetation management, climate change mitigation, and ecosystem services evaluation.

We welcome submissions focusing on, but not limited to:

• Integration of Radar with AI/ML in vegetation analysis;
• Vegetation biomass and health assessment;
• Vegetation degradation monitoring, biodiversity mapping, and precision agriculture;
• Methodological advancement in satellite-based vegetation monitoring;
• Radar data fusion techniques;
• New techniques in radar remote sensing;
• Radiative transfer model development for biophysical parameter retrieval;
• Radar remote sensing for soil moisture;
• Stereo image analysis and application; and
• Any other related areas.

We would like to acknowledge Shristi Gwal has acted as coordinator and has contributed to the preparation of the proposal for this Research Topic.

Article types and fees

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

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... 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:

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

Keywords: Vegetation, AI/ML, Radiative Transfer Model, Stereo satellite

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.