Kumhálová et al. (2026) Monitoring of Agricultural Crops by Remote Sensing in Central Europe: A Comprehensive Review

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Remote Sensing
• Year: 2026
• Date: 2026-04-03
• Authors: Jitka Kumhálová, Jiri Sedlak, Jiří Marčan, Věra Vandírková, Petr Novotný, Matěj Kohútek, František Kumhála
• DOI: 10.3390/rs18071075

Research Groups

Not specified in the provided text, as this is a review article.

Short Summary

This review article provides an overview of crop monitoring in Central Europe over the past fifteen years, highlighting technological and procedural developments, the integration of multi-sensor remote sensing data (Sentinel-1 and Sentinel-2), and future trends in digital agriculture.

Objective

• To provide an overview of crop monitoring in the Central European region over approximately the past fifteen years, emphasizing trends in subsequent technological and procedural developments.

Study Configuration

• Spatial Scale: Central European region, with a focus on parcel-level applications.
• Temporal Scale: Approximately the past fifteen years (review period); future applications involve near-real-time monitoring.

Methodology and Data

• Models used: The review discusses the future integration of agrometeorological and crop model data, but does not specify models used within the reviewed literature.
• Data sources: Sentinel-2 (optical data), Sentinel-1 (radar data), Land Parcel Identification System (LPIS) for reference data.

Main Results

• Optical data from Sentinel-2 are effective for crop type mapping and phenology analysis, but are limited by persistent cloud cover.
• Integration of radar data from Sentinel-1 addresses cloud cover limitations, enabling robust multi-sensor strategies for continuous monitoring.
• Reliable reference data, such as from the Land Parcel Identification System, is crucial for parcel-level validation and object-oriented analyses.
• Future developments in crop monitoring will increasingly rely on advanced time-series analysis, machine learning, and the integration of agrometeorological and crop model data.
• Remote sensing is identified as pivotal for near-real-time monitoring and decision support within digital agriculture ecosystems, especially in response to increasing drought frequency and yield variability due to climate change.

Contributions

• Provides a comprehensive synthesis of crop monitoring advancements and trends in Central Europe over the last 15 years.
• Emphasizes the importance and effectiveness of multi-sensor remote sensing strategies (Sentinel-1 and Sentinel-2) for agricultural applications in regions with challenging conditions (e.g., cloud cover, fragmented land).
• Highlights the critical role of reliable reference data (e.g., LPIS) for validation and object-oriented analysis.
• Identifies key future directions for remote sensing in agriculture, including advanced analytics, machine learning, and model integration, within the context of digital agriculture and climate change adaptation.

Funding

Not specified in the provided text.

Citation

@article{Kumhálová2026Monitoring,
  author = {Kumhálová, Jitka and Sedlak, Jiri and Marčan, Jiří and Vandírková, Věra and Novotný, Petr and Kohútek, Matěj and Kumhála, František},
  title = {Monitoring of Agricultural Crops by Remote Sensing in Central Europe: A Comprehensive Review},
  journal = {Remote Sensing},
  year = {2026},
  doi = {10.3390/rs18071075},
  url = {https://doi.org/10.3390/rs18071075}
}