Comprehensive evaluation of a spatio-temporal gap filling algorithm: Using remotely sensed precipitation, LST and ET data. (1st May 2020) - Siabi, Negar; Sanaeinejad, Seyed Hossein; Ghahraman, Bijan
Abstract: Temporal and spatial continuity of remote sensing data is flawed due to cloudiness, sensor malfunction or atmospheric pollution. Different methods have been presented to estimate missing values in remote sensing data. In this study, we evaluate the performance of a spatio-temporal gap filling algorithm developed by Weiss et al. (2014). This algorithm is interesting and worthy for further evaluation because it achieves high accuracy while maintaining the computational complexity considerably low. To conduct a comprehensive evaluation, we applied the algorithm to MODIS (Land Surface Temperature (LST) and evapotranspiration (ET)) and TRMM (precipitation) time series and investigate the effects of several factors including seasonality, variable type, gap size and surface characteristics through simulation scenarios. The performances were discussed using qualitative and quantitative assessments based on different simulation scenarios. A crucial finding of this study is a subtle structural deficiency of the algorithm. In particular, the algorithm outputs highly erroneous estimations when dealing with pixels with values mostly between zero and one. Such unexpected errors were observed in the seasonal assessment of land surface temperature estimations. In addition, according to the results of this study, the algorithm was sensitive to the variable type; however there was no correlation between the studied gap sizes and the error values. Among the three studied variables, LST and ET missing values were restored very accurately while estimations of precipitation missing values were more erroneous. The results also exhibit that in heterogeneous areas with complex topography, the errors of estimations were higher than homogeneous regions and areas with less complex topography. Based on the results, the algorithm should be used with caution in the discrete parameters like precipitation and area with abrupt variations. Furthermore, the design of the method may be refined for such datasets which include values with range between zero and one. Highlights: Comprehensive evaluation of a spatio-temporal gap filling method. Contrary to precipitation, LST and ET gaps were filled accurately. There were unexpected errors in the seasonal assessment of LST estimations. Accuracy of the algorithm was not a function of the studied gap sizes. The algorithm was sensitive to land cover changes. .