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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Ground reference data for sugarcane biomass estimation in São Paulo state, Brazil
Ramses A Molijn1, Lorenzo Iannini1, Jansle Vieira Rocha2
1Geoscience and Remote Sensing, Delft University of Technology, Delft 2628CN, The Netherlands.
Scientific Data
|August 8, 2018
Summary
This study presents a new sugarcane biophysical dataset for Brazil, crucial for understanding crop dynamics and sustainable development. It offers methods to estimate biomass from plant measurements, aiding agronomic modeling and satellite data validation.
Area of Science:
- Agricultural Science
- Agronomy
- Remote Sensing
Background:
- Effective sustainable development decisions in sugarcane production require accurate acreage and production dynamics data.
- Biophysical data throughout the growing season is vital for mapping dynamics, tuning agronomic models, and validating satellite measurements.
Purpose of the Study:
- To introduce a comprehensive sugarcane biophysical dataset.
- To propose a method for converting plant biometrics into biomass estimates.
- To address measurement precision for ground-based data and derived metrics.
Main Methods:
- Collected 3,500 sugarcane observations over one year in São Paulo, Brazil.
- Utilized both non-destructive plant biometric measurements and destructive biomass weighing.
- Developed an allometric model-based method to convert biometrics to biomass, compensating for data imbalance.
Main Results:
- A dataset of 3,500 sugarcane observations was created, balancing cost-effectiveness and field invasiveness.
- An empirical method was proposed to estimate biomass from plant biometrics.
- The precision of ground measurements and derived metrics was analyzed.
Conclusions:
- The presented sugarcane growth dynamics and precision data can inform future measurement campaigns.
- The dataset and conversion method support improved agronomic modeling and satellite data validation for sustainable sugarcane production.
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