Department of Water Engineering, Faculty of Agriculture, P.O. Box 9177949207, Ferdowsi University of Mashhad, Mashhad, Iran.
Department of Water Engineering, Faculty of Agriculture, P.O. Box 9177949207, Ferdowsi University of Mashhad, Mashhad, Iran.
J Environ Manage. 2020 May 1;261:110228. doi: 10.1016/j.jenvman.2020.110228. Epub 2020 Mar 2.
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.
由于云层、传感器故障或大气污染，遥感数据的时空连续性存在缺陷。已经提出了不同的方法来估计遥感数据中的缺失值。在本研究中，我们评估了 Weiss 等人开发的时空间隙填补算法的性能。该算法很有趣，值得进一步评估，因为它在保持计算复杂度相当低的情况下实现了高精度。为了进行全面评估，我们将该算法应用于 MODIS（陆地表面温度（LST）和蒸散量（ET））和 TRMM（降水）时间序列，并通过模拟情景研究了季节性、变量类型、间隙大小和表面特征等几个因素的影响。根据不同的模拟情景，通过定性和定量评估来讨论性能。这项研究的一个关键发现是算法存在微妙的结构缺陷。特别是，当处理值主要在零到一之间的像素时，算法输出高度错误的估计。在陆地表面温度估计的季节性评估中观察到了这种意外错误。此外，根据本研究的结果，该算法对变量类型敏感；然而，研究的间隙大小与误差值之间没有相关性。在研究的三个变量中，LST 和 ET 的缺失值得到了非常准确的恢复，而降水缺失值的估计则更加错误。结果还表明，在具有复杂地形的非均匀区域，估计误差高于均匀区域和地形较不复杂的区域。基于这些结果，在离散参数（如降水和具有急剧变化的区域）中应谨慎使用该算法。此外，对于包含 0 到 1 之间的值的数据集，该方法的设计可能需要进一步改进。
