ABSTRACT: Seagrass ecosystems play a vital role in climate change mitigation as they are globally significant carbon sinks and are responsible for 18% of marine carbon sequestration. However, their increasingly high rates of loss and degradation over the last decade have necessitated the development of effective and non-destructive ways to estimate biomass and, consequentially, stored organic carbon. In this study, we explore cost-effective ways to estimate total organic carbon storage in monospecific (Enhalus acoroides) and mixed (E. acoroides and Thalassia hemprichii or Cymodocea serrulata) seagrass ecosystems of Southeast Asia using a modeling approach. The model can be divided into 3 units: (1) biomass prediction, (2) carbon in living vegetation prediction, and (3) carbon in sediment prediction. A series of linear regression relationships linking the units, in which the results of the previous unit represent the predictor for the subsequent unit, was used to obtain information about seagrass biomass (above- and belowground), organic carbon in the living vegetation, and organic carbon in the sediment. All of the modeling units of monospecific patches had higher and more significant correlations between the predictor and response variables compared to those of mixed patches. Following the linked units, the predicted organic carbon on a landscape scale had a small margin of error for both monospecific and mixed patches. Although the models are applicable only for certain species, they improve the cost effectiveness of the data collection and can be easily applied over a larger spatial scale. The models provide the essential knowledge required to better understand and manage seagrass ecosystems and to more effectively address climate change.