Assessing the contribution of mango orchards to carbon sequestration using field derived data and satellite remote sensing techniques in Shibganj, Bangladesh

• Biomass carbon stock estimation of mango orchards using Landsat 8 OLI satellite images. • Significant positive relationship spotted between DBH and NDVI. • Integrated remote sensing techniques effectively predicted carbon sequestration potential of mango orchards. • Quantified the role of mango orchards to global climate change mitigation. Carbon sequestration mitigates global warming by capturing and storing atmospheric CO₂. The robust growth, wide adaptability and long lifespan traits of mango plantations make them significant contributors to carbon sequestration. However, evaluating their carbon sequestration potential is challenging, as it requires accurate biomass estimation over a large area. This study aims to mapping mango orchards, investigate relationships between diameter at breast height (DBH) and the Normalized Difference Vegetation Index (NDVI) and evaluate the carbon sequestration potential using Landsat 8 OLI satellite imagery. Integrating field derived data with satellite remote sensing techniques are capable of estimating biomass carbon at a spatial scale with reasonable cost while maintaining acceptable accuracy. Using these integrated techniques, we have determined the role of mango orchards in carbon sequestration. Overall, 61 plots, each measuring 2500 m 2 , were established to represent samples from different land cover types. The relationship between DBH and NDVI was scrutinized using simple linear regression analysis yielding R 2 value of 0.85, 0.96 and 0.77, for DBH classes < 25 cm, >25 cm to <65 cm and >65 cm, respectively. Using allometric equations with field derived data, the mean biomass carbon stock density was found 3.85 Mg ha −1 . However, when combining field derived data with remote sensing approaches, we found biomass carbon stock densities ranging from a maximum 8 Mg ha −1 to a minimum 0.15 Mg ha −1 . The methodology adopted in this study will aid in large scale biomass carbon estimation for species-specific area, but its application to ecosystems with diverse species may require further investigation.