Multitemporal seagrass carbon assimilation and aboveground carbon stock mapping using Sentinel-2 in Labuan Bajo 2019–2020

Seagrass is considered one of the most effective and efficient natural carbon sinks. In order to fully understand how it can help to adapt and mitigate climate change, information on the variability of the extent of its area, carbon stock, assimilation, sequestration, and potential emission is critical. However, details on spatially and temporally explicit seagrass biomass carbon stock, assimilation and sequestration are lacking in many parts of the world, including Indonesia as the seagrass biodiversity hot spot. To address this issue, this research aims to develop a remote sensing model to map aboveground seagrass carbon stock (AGC) and seagrass carbon assimilation rates (CA) based on multitemporal Sentinel-2 image analysis that can be used to assess the variability of seagrass over times and assess the potential carbon loss and emission reduction by seagrass. Sentinel-2 images with ideal conditions (cloud, haze, sunglint-free) and less ideal ones (cloud and haze-free, but with sunglint) were both used as the basis to develop the model. The research used two regression models, the random forest (RF) regression and stepwise regression (SW), and four input types, i.e., the reflectance bands, deglint bands, Principal Component (PC) bands, and co-occurrence texture bands. Field AGC and CA data estimated from percent cover were used to train and assess the accuracy of each model. Cross-validation was also performed to determine the model consistency when applied to images with different conditions. The results showed that the SW regression model using reflectance and deglint bands produced the most accurate and consistent AGC and CA maps. Applied to a 24-month Sentinel-2 image, from 2019 until 2020, the model showed a consistent pattern where the AGC and CA were lower from September to February and higher in March to August for both 2019 and 2020. Based on the two-year seagrass AGC and CA maps, the monthly average AGCs for 2019 and 2020 were estimated at 28.4 ± 2.7 and 28.4 ± 1.3 g C/m2, respectively, while the monthly average CAs were 6.7 ± 1.4 and 6.6 ± 0.7 Mg C/ha/year. The total AGCs in 2019 and 2020 were 113.8 ± 10.9 and 113.9 ± 5.2 Mg C, and the total CAs were 2648.3 ± 623.4 and 2636.9 ± 281.4 Mg C/year. All these values were from 400.71 ha of seagrass meadows. Finally, the availability of such remote sensing models helps to solve the time and cost-effectiveness challenges in acquiring spatially and temporally extensive seagrass AGC and CA information. Moreover, the research was able to produce the first map of multitemporal seagrass AGC and CA in specific seagrass meadow environments in Indonesia, which can be further improved to map these data rapidly at the national level and will be very useful for future work that implements Tier 3 methodological approach on carbon inventory.