Space-time mapping of soil organic carbon through remote sensing and machine learning

Abstract

Pedosphere is the largest terrestrial carbon reservoir. Soil organic carbon (SOC) is a critical attribute for soil quality and crop productivity, being directly linked to climate change mitigation and food security. Brazil boasts a significant agricultural production area and substantial potential for carbon sequestration. Nevertheless, the spatial-temporal distribution of SOC across the country is poorly understood, hindering the implementation of low-carbon agriculture public policies. We aimed to map the spatio-temporal distribution of SOC at from 0.00 to 0.20 cm depth over two periods. We assessed the SOC variation over seven years, generating a time series with five periods, obtaining the average SOC values. The Cubist algorithm was used to calibrate two short period (two years) and a long period (seven years/all period) models for SOC spatial prediction. Remote sensing data and soil particle size distribution maps were used as environmental covariates. We found in validation R² values of 0.47 and 0.25 for short period models, and 0.34 for the long period model. The SOC content decreased by 54.97 % in the area according to the mapping by short period models and 53.72 % according to mapping by the long-period model. The predicted maps showed the same trend of the database (soil samples with observed SOC values) for the study areas using both short period and long period models.