MONITORAMENTO DA VARIABILIDADE ESPAÇO-TEMPORAL DA CONCENTRAÇÃO ATMOSFÉRICA DE DIÓXIDO DE CARBONO SOBRE ÁREAS AGRÍCOLAS NA ROTA DE EXPORTAÇÃO DE GRÃOS DO PORTO DE ITAQUI

Abstract

The monitoring of atmospheric carbon dioxide (CO₂) concentration is essential for understanding the impacts of land use and land cover on the carbon balance. In this context, remote sensing emerges as a strategic tool, enabling the acquisition of key data to promote the sustainability of agricultural production, including a better understanding of CO₂ emissions in agroecosystems. Therefore, the objective of this study was to analyze, characterize, and quantify the spatiotemporal dynamics of carbon stock in agricultural areas along the grain export corridor of the Port of Itaqui, using data from the Orbiting Carbon Observatory-2 (OCO2) over the past decade. Time series from 2016 to 2023 of XCO₂ information for pasture and soybean areas were obtained from the platform managed by NASA’s GES DISC and compared with meteorological elements (precipitation, temperature, and relative humidity) extracted from NASA POWER, as well as vegetation indices (NDVI, EVI, and LAI) derived from the MODISTerra sensor. Statistical analyses included annual and monthly boxplots, temporal trend graphs of XCO₂ using the non-parametric Mann-Kendall test, and the Shapiro-Wilk normality test with a 5% significance level, all performed using Python programming language. Thematic maps were created using QGIS® and ArcGIS® software to investigate the influence of environmental factors on carbon dynamics. In addition, the “hot spot analysis” technique was applied to identify spatial clusters of high and low CO₂ concentrations in soybean and pasture areas, along with the analysis of XCO₂ anomalies for both land-use types. The temporal variation for pasture and soybean areas did not show a significant trend, presenting stable behavior with few oscillations over the years. The Shapiro-Wilk test indicated that the data do not follow a normal distribution, although they approximate a normal curve. Spatial analysis revealed patterns of CO₂ concentration hot-spots in degraded pasture areas, whereas regions dominated by soybean cultivation showed a higher occurrence of cold-spots. The years with the greatest hot spot intensity in pasture and soybean areas were 2020 and 2022, while the years with the most prominent cold-spots in pasture areas were 2019, 2021, and 2023, and for soybean areas, 2019 and 2021. In soybean areas, a negative correlation was observed between XCO₂ and the vegetation indices NDVI and EVI. The influence of meteorological variables was significant: precipitation and relative humidity showed a positive association with XCO₂ reduction, especially during drought periods, while temperature exhibited an inverse correlation. These findings indicate that the region’s agroecosystems respond differently to climatic conditions and soil management, with direct implications for agricultural sustainability and greenhouse gas mitigation. Furthermore, socioeconomic connections are feasible to link XCO₂ patterns to agricultural expansion, global commodity demand, and public policies aimed at carbon neutrality. These perspectives reinforce the importance of using remote sensing combined with climate data to monitor emissions and carbon sequestration.