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Remote Sensing-Based Biomass Assessment of Hedysarum coronarium from Multispectral UAV Imagery in a Mediterranean Pasture.

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Title: Remote Sensing-Based Biomass Assessment of Hedysarum coronarium from Multispectral UAV Imagery in a Mediterranean Pasture.
Authors: Furnitto, Nicola1 (AUTHOR), Failla, Sabina I. G.1,2 (AUTHOR) sabina.failla@unict.it, Sottosanti, Giuseppe1,3 (AUTHOR), Avondo, Marcella1 (AUTHOR), Bognanno, Matteo2 (AUTHOR), Biondi, Luisa1,3 (AUTHOR), Ramírez-Cuesta, Juan Miguel3 (AUTHOR)
Source: Remote Sensing. May2026, Vol. 18 Issue 10, p1594. 20p.
Subjects: Biomass estimation, Remote sensing, Range management, Grasslands, Normalized difference vegetation index
Geographic Terms: Southern Europe, Italy, Sicily (Italy)
Abstract: Highlights: What are the main findings? UAV-based multispectral vegetation indices accurately estimated fresh and dry above-ground biomass in the Mediterranean "Sulla" pasture. Visible-band greenness indices were among the best-performing predictors of biomass. What are the implications of the main findings? Inter-sensor harmonization enabled the combined analysis of data collected in two different growing seasons. A simple single-index workflow may support grazing management through rapid and non-destructive biomass assessment. The accurate estimation of pasture above-ground biomass (AGB) is critical for optimizing stocking rates and ensuring the sustainable use of Mediterranean pastures. This study developed empirical models to estimate fresh (AGBfresh) and dry above-ground biomass (AGBdry) using multispectral imagery acquired by Unmanned Aerial Vehicles (UAVs) in a Hedysarum coronarium pasture in Sicily, Italy. Field biomass was destructively sampled simultaneously with UAV surveys in 28 georeferenced plots during pre- and post-grazing phases over the 2023–2024 and 2024–2025 seasons. Data were collected with a DJI Mavic 3 Multispectral (for the 2024 test) and a DJI Matrice 300 + Altum-PT (for the 2025 test) and radiometrically calibrated to surface reflectance. Because two different multispectral sensors were used across years, an inter-sensor harmonization step was applied before vegetation-index calculation. Thirty-three vegetation indices were extracted as mean values within circular buffers of 1 m radius, centered on each sample plot to accommodate GNSS/georeferencing uncertainty. For each vegetation index, linear and exponential models were calibrated using 66% of the dataset and validated on the remaining 33% to predict fresh and dry above-ground biomass, and model performance was assessed using R2 and RMSE. On the validation dataset, ARVI2 and EVI2 showed the highest explanatory power for AGBfresh (R2 = 0.89), with ARVI2 providing the lower RMSE (2047 g m−2). For AGBdry, visible-band indices such as NGRDI and GRVI were among the best performers, reaching R2 = 0.85 with RMSE = 1371 g m−2. Visible-band greenness indices were among the most competitive predictors, whereas several conventional NIR-based indices showed only moderate performance. Overall, this UAV-based multispectral approach represents a promising and interpretable tool for biomass estimation in heterogeneous Mediterranean pastures, although further validation across additional seasons and sites is required to strengthen its transferability. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: Remote Sensing-Based Biomass Assessment of Hedysarum coronarium from Multispectral UAV Imagery in a Mediterranean Pasture.
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  Data: <searchLink fieldCode="AR" term="%22Furnitto%2C+Nicola%22">Furnitto, Nicola</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Failla%2C+Sabina+I%2E+G%2E%22">Failla, Sabina I. G.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> sabina.failla@unict.it</i><br /><searchLink fieldCode="AR" term="%22Sottosanti%2C+Giuseppe%22">Sottosanti, Giuseppe</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Avondo%2C+Marcella%22">Avondo, Marcella</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bognanno%2C+Matteo%22">Bognanno, Matteo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Biondi%2C+Luisa%22">Biondi, Luisa</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ramírez-Cuesta%2C+Juan+Miguel%22">Ramírez-Cuesta, Juan Miguel</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 10, p1594. 20p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Biomass+estimation%22">Biomass estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Range+management%22">Range management</searchLink><br /><searchLink fieldCode="DE" term="%22Grasslands%22">Grasslands</searchLink><br /><searchLink fieldCode="DE" term="%22Normalized+difference+vegetation+index%22">Normalized difference vegetation index</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Southern+Europe%22">Southern Europe</searchLink><br /><searchLink fieldCode="DE" term="%22Italy%22">Italy</searchLink><br /><searchLink fieldCode="DE" term="%22Sicily+%28Italy%29%22">Sicily (Italy)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? UAV-based multispectral vegetation indices accurately estimated fresh and dry above-ground biomass in the Mediterranean "Sulla" pasture. Visible-band greenness indices were among the best-performing predictors of biomass. What are the implications of the main findings? Inter-sensor harmonization enabled the combined analysis of data collected in two different growing seasons. A simple single-index workflow may support grazing management through rapid and non-destructive biomass assessment. The accurate estimation of pasture above-ground biomass (AGB) is critical for optimizing stocking rates and ensuring the sustainable use of Mediterranean pastures. This study developed empirical models to estimate fresh (AGBfresh) and dry above-ground biomass (AGBdry) using multispectral imagery acquired by Unmanned Aerial Vehicles (UAVs) in a Hedysarum coronarium pasture in Sicily, Italy. Field biomass was destructively sampled simultaneously with UAV surveys in 28 georeferenced plots during pre- and post-grazing phases over the 2023–2024 and 2024–2025 seasons. Data were collected with a DJI Mavic 3 Multispectral (for the 2024 test) and a DJI Matrice 300 + Altum-PT (for the 2025 test) and radiometrically calibrated to surface reflectance. Because two different multispectral sensors were used across years, an inter-sensor harmonization step was applied before vegetation-index calculation. Thirty-three vegetation indices were extracted as mean values within circular buffers of 1 m radius, centered on each sample plot to accommodate GNSS/georeferencing uncertainty. For each vegetation index, linear and exponential models were calibrated using 66% of the dataset and validated on the remaining 33% to predict fresh and dry above-ground biomass, and model performance was assessed using R2 and RMSE. On the validation dataset, ARVI2 and EVI2 showed the highest explanatory power for AGBfresh (R2 = 0.89), with ARVI2 providing the lower RMSE (2047 g m−2). For AGBdry, visible-band indices such as NGRDI and GRVI were among the best performers, reaching R2 = 0.85 with RMSE = 1371 g m−2. Visible-band greenness indices were among the most competitive predictors, whereas several conventional NIR-based indices showed only moderate performance. Overall, this UAV-based multispectral approach represents a promising and interpretable tool for biomass estimation in heterogeneous Mediterranean pastures, although further validation across additional seasons and sites is required to strengthen its transferability. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18101594
    Languages:
      – Code: eng
        Text: English
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      – SubjectFull: Biomass estimation
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      – SubjectFull: Remote sensing
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      – SubjectFull: Range management
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      – SubjectFull: Grasslands
        Type: general
      – SubjectFull: Normalized difference vegetation index
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      – SubjectFull: Southern Europe
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              Text: May2026
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