@misc{10481/105536,
year = {2024},
month = {11},
url = {https://hdl.handle.net/10481/105536},
abstract = {Human activities have significantly disrupted the global carbon cycle, leading to increased
atmospheric CO2 levels and altering ecosystems' carbon absorption capacities, with soils serving as
the largest carbon reservoirs in terrestrial ecosystems. The complexity and variability of soil
properties, shaped by long-term transformations, make it crucial to study these properties at various
spatial and temporal scales to develop effective climate change mitigation strategies. However,
integrating disparate soil databases presents challenges due to the lack of standardized protocols,
necessitating collaborative efforts to standardize data collection and processing to improve the
reliability of Soil Organic Carbon (SOC) estimates. This issue is particularly relevant in peninsular
Spain, where variations in sampling protocols and calculation methods have resulted in significant
discrepancies in SOC concentration and stock estimates. This study aimed to improve the
understanding of SOC storage and distribution in peninsular Spain by focusing on two specific
goals: integrating and standardizing existing soil profile databases, and modeling SOC
concentrations (SOCc) and stocks (SOCs) at different depths using an ensemble machine-learning
approach. The research produced four high-resolution SOC maps for peninsular Spain, detailing
SOCc and SOCs at depths of 0-30 cm, 30-100 cm and the effective soil depth, along with associated
uncertainties. These maps provide valuable data for national soil carbon management and contribute
to compiling Spain's National Greenhouse Gas Emissions Inventory Report. Additionally, the
findings support global initiatives like the Global Soil Organic Carbon Map, aligning with
international efforts to improve soil carbon assessments. The soil organic carbon concentration
(g/kg) maps for the 0-30 cm and 30-100 cm standard depths, along with the soil organic carbon
stock (tC/ha) maps for the 0-30 cm standard depth and the effective soil depth, including their
associated uncertainties, —all at a 90-meter pixel resolution— (SOCM90) are freely available at
https://doi.org/10.6073/pasta/48edac6904eb1aff4c1223d970c050b4 (Durante et al., 2024).},
organization = {PD acknowledges support from the pre-doctoral grant [DI-15-08093] awarded by the ‘National Programme for the Promotion of Talent and Its Employability’ of the Ministry of Economy, Industry, and Competitiveness, which are partially funded by the European Social Fund (ESF) from the European Commission. JMRM was funded by the University of Almería through the Spanish Ministry of Universities (María Zambrano Program) [grant number RR_C_2021_09]; the University of Almeria’s programme for research and knowledge transfer [grant number P_FORT_GRUPOS_2023/26]. RV was supported by the NASA Carbon Monitoring System grant
80NSSC21K0964. MG was funded by UNESCO-IGCP (grant no. 765) and Conahcyt (grant no. CF2023-I-1846). DA acknowledges support from the project “Plan Complementario de I+D+i en el área de Biodiversidad (PCBIO)” funded by the European Union within the framework of the Recovery, Transformation and Resilience Plan - NextGenerationEU and by the Regional Government of
Andalucia, by the EarthCul project (PID2020-118041GB-I00 Spanish National Research and Innovation Plan 2020). CO acknowledges support from the projects INTEGRATYON3 (PID2020-117825GB-C21 and C22), both funded by MCIN/AEI/10.13039/501100011033.},
publisher = {Earth System Science Data},
keywords = {SOCM90},
title = {Soil organic carbon maps and associated uncertainty at 90 m for peninsular Spain},
doi = {https://doi.org/10.5194/essd-2024-431, 2024},
author = {Pilar, Durante and Requena-Mullor, Juan Miguel and Vargas, Rodrigo and Guevara, Mario and Alcaraz-Segura, Domingo and Oyonarte, Cecilio},
}