Abstract Statistical time series models are increasingly being used to fit medium resolution time series provided by satellite sensors, such as Landsat, for terrestrial monitoring. Cloud and shadows, combined with low satellite repeat cycles, reduce surface observation availability. In addition, only a single year of data can be used where there is high inter-annual variation, for example, over many croplands. These factors reduce the ability to fit time series models and reduce model fitting accuracy. In solution, we propose a novel fill-and-fit (FF) approach for application to medium resolution satellite time series. In the ‘fill’ step, gaps are filled using a recently published algorithm developed to fill large-area gaps in satellite time series using no other satellite data. In the ‘fit’ step, a linear harmonic model is fitted to the gap-filled time series. The FF approach, and the conventional harmonic model fitting without gap filling, termed the F approach, are demonstrated using seven months of Landsat-7 and -8 surface reflectance Analysis Ready Data (ARD) over agricultural regions in North Dakota, Minnesota, Michigan, and Kansas. Synthetic model-predicted reflectance for days through the growing season are examined, and assessed quantitatively by comparison with an independent Landsat surface reflectance data set. The six Landsat reflective band root-mean-square difference (RMSD) between the predicted and the independent reflectance, considering millions of pixel observations for each ARD tile, show that the FF approach is more accurate than the F approach. The mean FF RMSD values varied from 0.025 to 0.026 for the four tiles, whereas the mean F RMSD values varied from 0.026 to 0.047. These mean FF RMSD values are

中文翻译：

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空间和时间完整的 Landsat 反射率时间序列建模：填充和拟合方法

摘要 统计时间序列模型越来越多地用于拟合卫星传感器（如 Landsat）提供的中等分辨率时间序列，用于地面监测。云和阴影，加上低卫星重复周期，降低了表面观测的可用性。此外，在年际变化较大的情况下，例如在许多农田上，只能使用一年的数据。这些因素会降低拟合时间序列模型的能力并降低模型拟合的准确性。在解决方案中，我们提出了一种适用于中等分辨率卫星时间序列的新型填充和拟合 (FF) 方法。在“填充”步骤中，使用最近发布的算法填充间隙，该算法开发用于在不使用其他卫星数据的情况下填充卫星时间序列中的大面积间隙。在“适合”步骤中，线性谐波模型拟合到间隙填充的时间序列。FF 方法和没有间隙填充的传统谐波模型拟合（称为 F 方法）使用七个月的 Landsat-7 和 -8 表面反射分析就绪数据（ARD）在密歇根州明尼苏达州北达科他州的农业地区进行了演示，和堪萨斯。检查合成模型预测的整个生长季节的反射率，并通过与独立的 Landsat 表面反射率数据集进行比较进行定量评估。考虑到每个 ARD 瓦片的数百万像素观测值，预测和独立反射率之间的六个 Landsat 反射带均方根差 (RMSD) 表明 FF 方法比 F 方法更准确。四个图块的平均 FF RMSD 值从 0.025 到 0.026 不等，而平均 F RMSD 值从 0.026 到 0.047 不等。这些平均 FF RMSD 值为