Multi-source remote sensing for large-scale biomass estimation in Mediterranean olive orchards using GEDI LiDAR  and machine learning

Contreras, Francisco; Cayuela, María L.; Sánchez-Monedero, Miguel A.; Pérez-Cutillas, Pedro

doi:10.5194/bg-22-7625-2025

Articles | Volume 22, issue 23

https://doi.org/10.5194/bg-22-7625-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-22-7625-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 23

Research article

|

04 Dec 2025

Research article |

| 04 Dec 2025

Multi-source remote sensing for large-scale biomass estimation in Mediterranean olive orchards using GEDI LiDAR and machine learning

Francisco Contreras, María L. Cayuela, Miguel A. Sánchez-Monedero, and Pedro Pérez-Cutillas

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Jun 2025	98	86	6	190
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Aug 2025	246	38	4	288
Sep 2025	976	62	2	1,040
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Cumulative views and downloads (calculated since 02 Apr 2025)

Month	HTML	PDF	XML	Total
Apr 2025	252	68	12	332
May 2025	174	78	12	264
Jun 2025	98	86	6	190
Jul 2025	92	80	0	172
Aug 2025	246	38	4	288
Sep 2025	976	62	2	1,040
Oct 2025	98	78	2	178
Nov 2025	90	208	8	306
Dec 2025	311	526	31	868
Jan 2026	226	312	18	556
Feb 2026	225	147	7	379
Mar 2026	287	170	11	468
Apr 2026	202	128	15	345
May 2026	179	76	8	263
Jun 2026	18	21	5	44
Jul 2026	41	15	9	65

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Short summary

This study presents an exploratory approach to estimate above-ground biomass in Mediterranean olive orchards using satellite and laser data. A volumetric framework was developed to model biomass from tree structure and environmental variables, offering a scalable method to improve large-scale assessments of carbon storage in low-stature vegetation.