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Forests play a crucial role by regulating the global and local weather through the exchange of atmospheric gases and water vapor. The present study aims to study the intra-annual variability of net ecosystem exchange (NEE) of carbon dioxide in a sal (Shorea robusta) dominated moist deciduous forest in India by integrating eddy covariance (EC) data and Biome-Biogeochemical Cycle (Biome-BGC) model. The study also attempts to address the spatial variability of NEE with respect to phenology. Monthly average NEE were estimated using a calibrated Biome-BGC model and the spatial NEE was mapped using random forest (RF) regression algorithm. Phenology metrics were generated using the moderate resolution imaging spectrometer (MODIS) enhanced vegetation index product (MOD13A2) and its relationship with the estimated NEE was studied. RF regression model for monthly average spatial NEE estimation was built with an R-2 of 0.84 and % RMSE of 3.68%. The study revealed that the NEE at the regional scale can be estimated using the basic meteorological variables like mean temperature, vapor pressure deficit, minimum temperature and total precipitation. Biome-BGC model output showed that the sal forest of the study area acted as a net sink of carbon in almost all months of 2015, except April to June. Peak NEE value (-2.80 to -2.96gCm(-2)day(-1)) was observed during October month. Annual NEE of sal forest in 2015 was found to be -526.87gCm(-2)year(-1). With the start of season (end of June), sal forest showed an increasing trend in NEE while decreasing trend was observed at the end of season (end of October). The study showed the applicability of Biome-BGC model in Indian forest when integrated with EC data. The study also highlighted the utility of RF in capturing the spatial variability of NEE over large area.