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.

本数据集展示了在《国际应用地球观测与地理信息》期刊中即将发表的平滑与插补算法的结果示例。该论文提出了一种创新的方法，旨在生成15天周期的高空间分辨率数据的平滑与插补时间序列。该方法基于高质量观测的模板来填补数据空缺，随后对填补后的数据进行过滤。我们利用Landsat数据，对德国巴伐利亚州一个大型连续区域以及欧洲不同生态区域的九个小测试站点进行了方法测试。总体而言，我们的结果与验证数据集高度吻合，可见光波段平均绝对误差（MAE）为0.01，近红外波段为0.03，短波红外波段为0.02。偶尔，重建的时间序列会受到未检测到云层的伪影影响。较少情况下，由于长时间缺失数据，不确定性较大。为了充分利用时间序列，可靠的云掩膜是必不可少的。