Effective method for filling gaps in time series of environmental remote sensing data: An example on evapotranspiration and land surface temperature images

Phenomena such as cloudiness, atmospheric aerosol or sensor failure cause missing data (gaps) in remote sensing images and damage spatial and temporal continuity. Dealing with this deficiency is of importance for continuous spatio-temporal modeling and environmental studies. Simplicity, efficiency and accuracy are dominant factors in practicality of gap filling algorithms especially in dealing with large gaps and long time series. In this study, an effective and efficient spatio-temporal gap filling algorithm is proposed, implemented and tested. The method was applied to MODIS 8-day Land Surface Temperature (LST) and Evapotranspiration (ET) datasets with a 1 km spatial resolution. To assess the performance of the proposed methods, artificial gaps were introduced and filled. Then estimated and real values were compared. The results showed that our method can predict the missing values very accurately (based on RMSE) even in gaps with heterogeneous surface for both variables. Our proposed method has no limitation on the shape and size of the gaps. The proposed algorithm is flexible regarding parameterization. It can handle large volumes of datasets due to its parallelizable structure. More importantly, the method run-time is extremely low even in large gaps.