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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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Sentinel-2 Data for Precision Agriculture?-A UAV-Based Assessment.

Josephine Bukowiecki¹, Till Rose¹, Henning Kage¹

¹Institute of Crop Science and Plant Breeding, Christian-Albrechts-University, 24118 Kiel, Germany.

Sensors (Basel, Switzerland)

|April 30, 2021

Summary

Sentinel-2 satellite data shows potential for precision agriculture, but its coarse Red Edge band limits site-specific crop monitoring. Further research is needed for optimal application in precision farming.

Keywords:

GAI Sentinel-2 UAV precision agriculture winter wheat

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Area of Science:

Agricultural Remote Sensing
Precision Agriculture
Crop Monitoring

Background:

Precision agriculture relies on accurate crop monitoring for optimized resource management.
Unmanned aerial vehicles (UAVs) offer high-resolution data but have limited spatial coverage.
Satellite data, like Sentinel-2, provides broader coverage but requires validation for precision applications.

Purpose of the Study:

To assess the potential of Sentinel-2 data for precision agriculture by calibrating it with UAV data.
To evaluate the use of satellite-derived Green Area Index (GAI) for crop and yield prediction.
To investigate the limitations of Sentinel-2 for site-specific crop variation mapping.

Main Methods:

Calibrated Sentinel-2 data using a UAV-based Green Area Index (GAI) model for winter wheat.
Evaluated satellite-derived GAI predictions using independent datasets.
Calculated total absorbed photosynthetic radiation and correlated it with grain yield.
Generated yield maps by combining plot-level yield data with UAV crop coverage maps.

Main Results:

A simple ratio combining near-, infrared-, and Red Edge-wavebands achieved R² = 0.82 for satellite-based GAI prediction.
Sentinel-2 data captured average seasonal GAI trends but struggled with site-specific variations.
The coarser Red Edge band of Sentinel-2 was identified as a limitation for detailed crop monitoring.
Cloud cover impacted temporal resolution, making it unsatisfactory for frequent monitoring.

Conclusions:

Sentinel-2 data shows promise for broad crop monitoring in precision agriculture but requires careful application.
Limitations in spatial and temporal resolution, particularly the Red Edge band, hinder site-specific analysis.
Further research is essential to enhance the utility of Sentinel-2 data for precision agriculture applications.