Peatland plays an important role in the global climate. Balancing economic, social and conservation needs on peatland utilization become an obligation in developing sustainable peatland regulation. To identify the appropriate land function in the peatland environment, the depth of peat is the main property to manage those balance needs. On the other hand, vast areas of peatland changing hinder rapid peat depth mapping method to have high accuracy. Multi-temporal remote sensing data were used to identify peatland-related land-use changes. The vegetation and wetness indices spectral transformations had been analyzed. The method used for the accuracy test in this study was correlation and regression analysis for modeling and the Standard Error of Estimate (SEE). The results of this study showed that the vegetation indices (NDVI, SAVI, and MSARVI) and NDSI were not able to obtain peat thickness models due to the unstable vegetation and land cover changes. However, the NDWI was fairly satisfied with the statistical assessment and was able to model the peat thickness with 41.96% accuracy. The determination of a sample design, the number and distribution of samples in preserved land covers, and the unexplained variables and external factors in this study need to be considered in further research. The vegetation indices and wetness indices potentially can be the alternative variables to construct the peat depth map.
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