Drivers of Organic Carbon Stocks in Different LULC History and along Soil Depth for a 30 Years Image Time Series

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2021-06, Vol.13 (11), p.2223
• Main Authors: Tayebi, Mahboobeh, Fim Rosas, Jorge Tadeu, Mendes, Wanderson de Sousa, Poppiel, Raul Roberto, Ostovari, Yaser, Ruiz, Luis Fernando Chimelo, dos Santos, Natasha Valadares, Cerri, Carlos Eduardo Pellegrino, Silva, Sérgio Henrique Godinho, Curi, Nilton, Silvero, Nélida Elizabet Quiñonez, Demattê, José A. M.
• Format: Article
• Language: English
• Subjects: Algorithms; Bioclimatology; Boreholes; Carbon; Clay; Clay minerals; Clay soils; Climate change; Environmental monitoring; Ethanol; Geology; Influence; Land cover; Land use; land use and cover history; Learning algorithms; Machine learning; Mineralization; Mineralogy; Model testing; Organic carbon; Organic soils; Precipitation; Prediction models; random forest; Remote monitoring; Remote sensing; Satellite imagery; Soil analysis; Soil classification; Soil depth; Soil dynamics; soil organic carbon stocks; Soil properties; Soil types; Soils; Subsoils; Sugarcane; Time series; Topography; Topsoil; Variables; Vegetation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13112223

Soil organic carbon (SOC) stocks are a remarkable property for soil and environmental monitoring. The understanding of their dynamics in crop soils must go forward. The objective of this study was to determine the impact of temporal environmental controlling factors obtained by satellite images over the SOC stocks along soil depth, using machine learning algorithms. The work was carried out in São Paulo state (Brazil) in an area of 2577 km2. We obtained a dataset of boreholes with soil analyses from topsoil to subsoil (0–100 cm). Additionally, remote sensing covariates (30 years of land use history, vegetation indexes), soil properties (i.e., clay, sand, mineralogy), soil types (classification), geology, climate and relief information were used. All covariates were confronted with SOC stocks contents, to identify their impact. Afterwards, the abilities of the predictive models were tested by splitting soil samples into two random groups (70 for training and 30% for model testing). We observed that the mean values of SOC stocks decreased by increasing the depth in all land use and land cover (LULC) historical classes. The results indicated that the random forest with recursive features elimination (RFE) was an accurate technique for predicting SOC stocks and finding controlling factors. We also found that the soil properties (especially clay and CEC), terrain attributes, geology, bioclimatic parameters and land use history were the most critical factors in controlling the SOC stocks in all LULC history and soil depths. We concluded that random forest coupled with RFE could be a functional approach to detect, map and monitor SOC stocks using environmental and remote sensing data.