Fusion of MODIS and OLI Images for Estimating Daily Surface Reflectance at 30m Spatial Resolution in Complex Heterogeneous Regions

Cocurrent access to high spatial and temporal resolution imageries is essential in many studies. However, this will not be provided by using images from one sensor. To achive this goal, the incorporation of different satellites with high spatial (e.g., Landsat) and temporal (e.g., MODIS) images can be used. In present study, one of newest data fusion model, Enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was evaluated with actual satellite data (OLI image). For emplementation and evaluation of this model, two different periods were selected (the first period selected between the days 204 to 220 and the second one were between the days 220 to 236). For evaluating the obtained results, OLI satellite images were used as a refrence data. Results show that ESTARFM not only improves the accuracy of predicted fine-resolution reflectance, especially for heterogeneous landscapes but it preserves spatial details also. The Coefficient of Determination (R2) of blue, green, red and near-infrared estimation bands with actual satellite data was 0.90, 0.91, 0.91 and 0.85 respectively, and the average Root-Mean-Square Error (RMSE) in four bands are 0.025, 0.030, 0.036 and 0.049 successively. In addition, a comparison between obtained NDVI from estimated reflectance values and observed NDVI, indicates outputs of ESTARFM have acceptable accuracy of (R2 =0.87 and RMSE =0.056). Thereby, this model can be successfully utilized to fusion images for enhancing the spatial and temporal resolution of reflectance.