Estimation of gross primary productivity of Indian Sundarbans mangrove forests using field measurements and Landsat 8 Operational Land Imager data

Mangroves are considered to be one of the vital coastal ecosystems in the world and Sundarbans is one of the largest blocks of mangrove ecosystem. It covers an area of about 1 mha, of which 60% is located in Bangladesh and the remaining 40% lies in India. For sustainable management of mangrove forests, there is a need to study the health of the mangrove vegetation in terms of their productivity. In the present study, Landsat 8 Operational Land Imager (OLI) surface reflectance data of 2017-18 over Indian Sundarbans, encompassing three seasons (summer, winter and post-monsoon) were used for computing certain spectral/ vegetation indices. Subsequently, a satellite-based vegetation photosynthesis-light use efficiency model was adopted to estimate Gross Primary Productivity (GPP) using the above indices along with in-situ data measured using portable gas exchange system and soil salinity information. The mean GPP value of post monsoon period was found to be higher than the winter and the summer seasons. Furthermore, the mean GPP values were estimated for the different Reserve Forests, islands and localities of Indian Sundarbans. These GPP estimates provide an insight into the mangrove health in terms of their photosynthetic efficiency and carbon sequestration. The reported methodology can be used for estimating primary productivity of other mangrove areas or forests at landscape-level. This study reveals integration of satellite data and in-situ measurements towards assessment of GPP of mangrove forests and thereby serves as one of the major applications in studying mangrove physiology and ecology.