Remote sensing-based analysis of methane (CH₄) and ozone (O₃) concentration variations with agricultural growth: a...

Apart from the several natural sources of methane (CH₄) and ozone (O₃) emissions, several anthropogenic sources such as industrial exhausts and agriculture emit these gases directly into the atmosphere. There has been a lack of research that analyses vegetation as the primary source of CH₄ and O₃ in the atmosphere. This study seeks to integrate the Sentinel-5P datasets with the different parameters of vegetation growth and gaseous dispersal in the troposphere using remote sensing datasets from other remote sensing satellite sensors such as thermal and multi-spectral. The multi-sensor remote sensing data integration approach is important to establish sensor interoperability to understand the variations in the concentrations of CH₄ and O₃ in a better and more logical manner, besides making the study more reliable. A high correlation was observed between the Normalized Differential Vegetation Index (NDVI) derived from the multi-spectral satellite data with the tropospheric concentrations of CH₄ and O₃ with an R² value of 0.732 and 0.668 respectively. The Land Surface Temperature (LST), wind speed, tropospheric concentration values of CH₄ and O₃, and NDVI values were fed into a Support Vector Regressor model, and different kernel performances were analysed based on the estimated concentrations of CH₄ and O₃. It was observed that the SVR with Radial Basis Function (RBF) kernel performed better with R²-statistics of 0.646 and 0.557 in the training and testing phases respectively. The study is a first-of-its-kind approach to identifying the agricultural environment as a potential source of O₃ and CH₄ emissions in the atmosphere.