This research received financial support from the European Regional Development Fund
(ERDF) through the Operational Programme for Competitiveness Factors (COMPETE) and fromnational
funds through FCT (Foundation for Science and Technology) (PTDC/AAG-MAA/4539/2012/FCOMP01-0124-FEDER-02786), and from national funds FCT/MCTES (PIDDAC) through CIMO (UIDB/00690/
2020 and UIDP/00690/2020) and SusTEC (LA/P/0007/2020).
This study investigated the impact of regional land abandonment in northeast Portugal.
It specifically focused on carbon sequestration opportunities in the Upper Sabor RiverWatershed,
situated in the northeast of Portugal, amidst agricultural land abandonment. The study involved
mapping the distribution of soil organic carbon (SOC) across four soil layers (0–5 cm, 5–10 cm,
10–20 cm, and 20–30 cm) at 120 sampling points. The quantification of SOC storage (measured
in Mg C ha−1) allowed for an analysis of its relationship with various landscape characteristics,
including elevation, land use and land cover (LULC), normalized difference vegetation index (NDVI),
modified soil-adjusted vegetation index (MSAVI), topographic wetness index (TWI), and erosion
risk (ER). Six statistical tests were employed, including multivariate approaches like Cubist and
Random Forest, within different scenarios to assess carbon distribution within the watershed’s soils.
These modeling results were then utilized to propose strategies aimed at enhancing soil carbon
storage. Notably, a significant discrepancy was observed in the carbon content between areas at
higher elevations (>1000 m) and those at lower elevations (<800 m). Additionally, the study found
that the amount of carbon stored in agricultural soils was often significantly lower than in other land
use categories, including forests, mountain herbaceous vegetation, pasture, and shrub communities.
Analyzing bi- and multivariate scenarios, it was determined that the scenario with the greatest
number of independent variables (set 6) yielded the lowest RMSE (root mean squared error), serving
as a key indicator for evaluating predicted values against observed values. However, it is important to
note that the independent variables used in set 4 (elevation, LULC, and NDVI) had reasonably similar
values. Ultimately, the spatialization of the model from scenario 6 provided actionable insights for
soil carbon conservation and enhancement across three distinct elevation levels.
