ABSTRACT
Spatio-temporal greenhouse gas (GHG) monitoring is essential to understand the seasonality in sources and sinks of GHGs distribution. This chapter analyses the CO2 concentration over the Indian mainland during 2010-2012 using GOSAT data. The ground observed data from the Cape Rama station in the Western Ghats was used to rectify the GOSAT based observations by integrating the wind (u-wind, v-wind, resultant) and water vapour data. The second-order nonlinear and first-order multiple linear regression analyses were performed employing yearly and seasonal data. The regression equation was generated using 70% and validated with 30% of the observation data following the data splitting method. The mean CO2 concentration of 2.09 gC/m2/day and 3.16 gC/m2/day was recorded over the Indian mainland in 2010 and 2012, respectively, which are highly correlated (2010, R2 =0.57; 2012, R2 =0.69) to the station observed and modeled data in different seasons. The assessment of mean absolute error (MAE) and root mean square error (RMSE) indicated higher accuracy with seasonal data with the highest level of accuracy for the winter season (MAE, 0.35; RMSE, 0.36). The seasonal changes indicated the highest carbon emission in summer and low in post-monsoon autumn. The forest-dominated Indian Himalayan Region, including the northeastern region, demonstrated higher carbon sequestration through the year, indicating prominent aseasonality. Long-term GHG monitoring may improve our understanding of the impact of climate change and global warming in response to increased GHG emissions and projecting changes under the various future climate change scenarios.
