Example data: Smoothing and gap-filling of high resolution multi-spectral time series: Example of Landsat data

The data-set represents an example of the results obtained with the smoothing and gap-filling algorithm to be published in the International Journal of Applied Earth Observation and Geoinformation. This paper introduces a novel methodology for generating 15-day, smoothed and gap-filled time series of high spatial resolution data. The approach is based on templates from high quality observations to fill data gaps that are subsequently filtered. We tested our method for one large contiguous area (Bavaria, Germany) and for nine smaller test sites in different ecoregions of Europe using Landsat data. Overall, our results match the validation dataset to a high degree of accuracy with a mean absolute error (MAE) of 0.01 for visible bands, 0.03 for near-infrared and 0.02 for short-wave-infrared. Occasionally, the reconstructed time series are affected by artefacts due to undetected clouds. Less frequently, larger uncertainties occur as a result of extended periods of missing data. Reliable cloud masks are highly warranted for making full use of time series.本数据集为即将发表于《国际应用地球观测与地理信息学报（International Journal of Applied Earth Observation and Geoinformation）》的平滑与间隙填充算法的测试结果示例。本研究提出一种全新方法，可生成15天周期、经平滑处理且填补了数据间隙的高空间分辨率时间序列数据。该方法依托高质量观测数据模板，对数据间隙进行填充后再执行滤波操作。我们采用Landsat数据，针对德国巴伐利亚州这一大型连续区域，以及欧洲不同生态区的9个小型试验场地开展了方法验证实验。总体而言，本研究结果与验证数据集的匹配精度极高：可见光波段的平均绝对误差（mean absolute error, MAE）为0.01，近红外波段为0.03，短波红外波段为0.02。偶尔情况下，重构得到的时间序列会因未被检测到的云而产生伪影；更为少见的情形是，因长时间数据缺失而出现较大的不确定性。若要充分挖掘时间序列数据的应用价值，可靠的云掩膜产品是不可或缺的前提条件。