Forest aboveground biomass (AGB) estimation over large extents and high temporal resolution is crucial in managing Mediterranean forest ecosystems, which have been predicted to be very sensitive to climate change effects. Although many modeling procedures have been tested to assess forest AGB, most of them cover small areas and attain high accuracy in evaluations that are difficult to update and extrapolate without large uncertainties. In this study, focusing on the Region of Murcia in Spain (11,313 km²), we integrated forest AGB estimations, obtained from high-precision airborne laser scanning (ALS) data calibrated with plot-level ground-based measures and bio-geophysical spectral variables (eight different indices derived from MODIS computed at different temporal resolutions), as well as topographic factors as predictors. We used a quantile regression forest (QRF) to spatially predict biomass and the associated uncertainty. The fitted model produced a satisfactory performance (R² 0.71 and RMSE 9.99 t·ha⁻¹) with the normalized difference vegetation index (NDVI) as the main vegetation index, in combination with topographic variables as environmental drivers. An independent validation carried out over the final predicted biomass map showed a satisfactory statistically-robust model (R² 0.70 and RMSE 10.25 t·ha⁻¹), confirming its applicability at coarser resolutions.

One of the largest challenges with soil information around the world is how to harmonize archived soil data from different sources and how to make it accessible to soil scientist. In Ecuador, there have been two major projects that have provided soil information, but the methodology of these projects, although comparable, did not coincide, especially with respect to how information was reported. Here, we present a new soil database for Ecuador, comprising 13 542 soil profiles with 51 713 measured soil horizons, including 92 different edaphic variables. The original data were in a non-editable format (i.e., PDF), which made it difficult to access and process the information. Our study provides an integrated framework that combines multiple analytic tools for automatically converting legacy soil information from an analog format into usable digital soil mapping inputs across Ecuador. This framework allowed us to incorporate quantitative information on a broad set of soil properties and retrieve qualitative information on soil morphological properties collected in the profile description phase, which is rarely included in soil databases. We present a new harmonized national soil database using a specific methodology to preserve relevant information. The national representativeness of soil information has been enhanced compared with other international databases, and this new database contributes to filling the gaps in publicly available soil information across the country. The database is freely available at https://doi.org/10.6073/pasta/1560e803953c839e7aedef78ff7d3f6c (Armas et al., 2022).

Armas, D., Guevara, M., Alcaraz-Segura, D., Vargas, R., Soriano-Luna, M.A., Durante, P., Oyonarte, C. 2017. Mapa digital del perfil del carbono orgánico en los suelos de Andalucía, España. Ecosistemas 26(3):80-88. Doi.: 10.7818/ECOS.2017.26-3.10 Conocer y comprender la distribución espacial del carbono orgánico del suelo (COS) es necesario para el manejo de este reservorio del ciclo global del carbono, y la planificación de acciones para mitigar el cambio climático. El objetivo fue generar mapas de porcentaje de COS en Andalucía mediante modelos consistentes, cuantificando la incertidumbre asociada, e identificando los factores que controlan su variabilidad. Se empleó una base de datos patrimonial con 1500 perfiles de suelo, y 20 covariables ambientales como factores predictivos descriptores del clima, topografía, y atributos funcionales de los ecosistemas. Se utilizó una combinación de modelos lineales y un ensamble de árboles de regresión combinado con geoestadística para estimar la distribución espacial (horizontal y vertical) del COS. Se generaron mapas de la distribución del COS a seis intervalos de profundidad (0-5, 5-15, 15-30, 30-60, 60-100 y 100-200 cm). La varianza explicada por los modelos osciló entre el 57 y 63.0 %, pero con alta incertidumbre en los sitios con mayor concentración de COS (hasta el 8 %). La variabilidad del COS respondió a una compleja combinación de factores, siendo la precipitación el predictor más importante en todas las profundidades, el EVI (Indicador de la productividad) en los horizontes superficiales, y la topografía en los profundos. Los mapas y modelos producidos resultan herramientas útiles para la gestión ambiental, al facilitar la actualización periódica de los contenidos del COS y aportar información para el manejo de este reservorio. Digital map of the organic carbon profile in the soils of Andalusia, Spain. Armas, D., Guevara, M., Alcaraz-Segura, D., Vargas, R., Soriano-Luna, M.A., Durante, P., Oyonarte, C. 2017. Digital map of the organic carbon profile in the soils of Andalusia, Spain. Ecosistemas 26(3):80-88. Doi.: 10.7818/ECOS.2017.26-3.10 To know and understand the spatial distribution of soil organic carbon (SOC) is the first step for management of this important pool in the global carbon cycle, and to develop actions to address climate change. The objective of this work was to generate maps of percent SOC across Andalucía through the use of consistent models, quantifying the associated uncertainty and identify controls of the spatial variability. We used a legacy soil profile collection with 1500 soil profiles and 20 environmental covariates as prediction factors for climate, topography, and ecosystem functional attributes related to the dynamic of primary production. A combination of linear models and an ensemble of regression trees coupled with geostatistics was used to estimate the spatial distribution (horizontal and vertical) of SOC, maps of SOC distribution across six soil depths (0-5, 5-15, 15-30, 30-60, 60-100 y 100-200 cm). Explained variance of our models varied between 63 to 57 %, with high uncertainty at sites with the highest values of SOC (up to 8%). The variability of SOC corresponded to a complex interaction of factors, whereas precipitation is an outstanding predictive factor across all depths, annual primary production (EVI) at the superficial horizons, and topography across deep horizons. Generated maps result in a useful tool for environmental policy, because they facilitate the periodical update of SOC and provide information for the management of this pool.

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).