Improvement of Vegetation Index Image Simulations by Applying Accumulated Temperature

To analyze temporal and spatial changes in vegetation, it is necessary to determine the associated continuous distribution and conduct growth observations using time series data. For this purpose, the normalized difference vegetation index, which is calculated from optical images, is employed. However, acquiring images under cloud cover and rainfall conditions is challenging; therefore, time series data may often be unavailable. To address this issue, La et al. (2015) developed a multilinear simulation method to generate missing images on the target date using the obtained images. This method was applied to a small simulation area, and it employed a simple analysis of variables with lower constraints on the simulation conditions (where the environmental characteristics at the moment of image capture are considered as the variables). In contrast, the present study employs variables that reflect the growth characteristics of vegetation in a greater simulation area, and the results are compared with those of the existing simulation method. By applying the accumulated temperature, the average coefficient of determination (R²) and RMSE (Root Mean-Squared Error) increased and decreased by 0.0850 and 0.0249, respectively. Moreover, when data were unavailable for the same season, R² and RMSE increased and decreased by 0.2421 and 0.1289, respectively.