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Context <p>Accurate and regular estimation of above-ground biomass (AGB) in grassland ecosystems is essential for sustainable grazing management, feed planning, and carbon accounting. However, AGB mapping in heterogeneous grasslands remains challenging due to the spatial and temporal variability of vegetation and management practices.</p> Aims <p>This study explores the potential of Gaussian Process Regression (GPR) models combined with multispectral imagery from Sentinel-2 and PlanetScope to predict AGB across different grassland systems in Northern Italy.</p> Methods <p>Extensive field measurements (n = 954) were collected over 18 months across meadows, lowland pastures, and alpine grasslands, covering a range of altitudes, management regimes, and canopy structures. Spectral predictors from Sentinel-2 and PlanetScope were used to train independent GPR models and evaluate their predictive performance at both pixel and field scales.</p> Key Results <p>At the pixel level, GPR models achieved R<sup>2</sup> = 0.520 (Sentinel-2) and R<sup>2</sup> = 0.514 (PlanetScope) with mean absolute errors (MAE) of ~400 kg DM ha<sup>−1</sup>, consistent with the high heterogeneity of grassland canopies. Aggregating predictions at the field scale markedly improved accuracy (R<sup>2</sup> = 0.972 and 0.968; MAE = 60–120 kg DM ha<sup>−1</sup>, ≤10% relative error). These results are comparable to those of commercial pasture monitoring platforms.</p> Conclusion <p>The integration of high-resolution multispectral imagery and non-parametric GPR modeling allows robust AGB estimation in heterogeneous grasslands, reducing uncertainty through field-scale aggregation.</p> Implications and Impacts <p>This research provides a scalable and transferable framework for operational biomass monitoring, offering a practical tool for digital decision support systems (DSS) and a scientific basis for integration into carbon Measurement, Reporting, and Verification (MRV) protocols. The novelty of the study lies in demonstrating the combined use of Sentinel-2 and PlanetScope data within a unified GPR framework for multi-site grassland systems, validated through extensive field observations.</p>

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Optimising grassland Above-Ground biomass Estimation for managed grasslands: A Gaussian process regression approach for Sentinel-2 and Planet Scope in Northern Italy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月16日

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  • Daniele Pinna,
  • Elena Basso,
  • Cristina Pornaro,
  • Reddy Pullanagari,
  • Stefano Macolino,
  • Andrea Pezzuolo,
  • Francesco Marinello

摘要

Context

Accurate and regular estimation of above-ground biomass (AGB) in grassland ecosystems is essential for sustainable grazing management, feed planning, and carbon accounting. However, AGB mapping in heterogeneous grasslands remains challenging due to the spatial and temporal variability of vegetation and management practices.

Aims

This study explores the potential of Gaussian Process Regression (GPR) models combined with multispectral imagery from Sentinel-2 and PlanetScope to predict AGB across different grassland systems in Northern Italy.

Methods

Extensive field measurements (n = 954) were collected over 18 months across meadows, lowland pastures, and alpine grasslands, covering a range of altitudes, management regimes, and canopy structures. Spectral predictors from Sentinel-2 and PlanetScope were used to train independent GPR models and evaluate their predictive performance at both pixel and field scales.

Key Results

At the pixel level, GPR models achieved R2 = 0.520 (Sentinel-2) and R2 = 0.514 (PlanetScope) with mean absolute errors (MAE) of ~400 kg DM ha−1, consistent with the high heterogeneity of grassland canopies. Aggregating predictions at the field scale markedly improved accuracy (R2 = 0.972 and 0.968; MAE = 60–120 kg DM ha−1, ≤10% relative error). These results are comparable to those of commercial pasture monitoring platforms.

Conclusion

The integration of high-resolution multispectral imagery and non-parametric GPR modeling allows robust AGB estimation in heterogeneous grasslands, reducing uncertainty through field-scale aggregation.

Implications and Impacts

This research provides a scalable and transferable framework for operational biomass monitoring, offering a practical tool for digital decision support systems (DSS) and a scientific basis for integration into carbon Measurement, Reporting, and Verification (MRV) protocols. The novelty of the study lies in demonstrating the combined use of Sentinel-2 and PlanetScope data within a unified GPR framework for multi-site grassland systems, validated through extensive field observations.