Abstract. Land surface temperature (LST) is one of the most important and widely used parameter for studying land surface processes. Moderate Resolution Imaging Spectroradiometer (MODIS) LST products (e.g., MOD11A1 and MYD11A1) can provide this information with high spatiotemporal resolution with global coverage. However, the broad applications of these data are hampered because of missing values caused by factors such as cloud contamination. In this study, we used a spatiotemporal gap-filling framework to generate a seamless global 1 km daily (mid-daytime and mid-nighttime) MODIS-like LST dataset from 2003 to 2020 based on standard MODIS LST products. The method includes two steps, 1) data pre-processing and 2) spatiotemporal fitting. In the data pre-processing, we filtered pixels with low data quality and filled gaps using the observed LST at another three time points of the same day. In the spatiotemporal fitting, first, we fitted the long-term trend (overall mean) of observations in each pixel (ordered by day of year). Then we spatiotemporally interpolated residuals between observations and overall mean values for each day. Finally, we estimated missing values of LST by adding the overall mean and interpolated residuals. The results show that the missing values in the original MODIS LST were effectively and efficiently filled, and there is no obvious block effect caused by large areas of missing values, especially near the boundary of tiles, which might exist in other seamless LST datasets. The cross-validation with different missing rates at the global scale indicates that the gap-filled LST data have high accuracies with the average root mean squared error (RMSE) of 1.88 °C and 1.33 °C, respectively for mid-daytime (1:30 pm) and mid-nighttime (1:30 am). The seamless global daily (mid-daytime and mid-nighttime) LST dataset at a 1 km spatial resolution is of great use in global studies of urban systems, climate research and modeling, and terrestrial ecosystems studies. The data are available at Iowa State University's DataShare at https://doi.org/10.25380/iastate.c.5078492 (Zhang et al., 2021a).

中文翻译：

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全球无缝 1 公里分辨率每日地表温度数据集（2003-2020）

摘要。地表温度（LST）是研究地表过程的最重要和最广泛使用的参数之一。中分辨率成像光谱仪 (MODIS) LST 产品（例如，MOD11A1 和 MYD11A1）可以提供具有全球覆盖范围的高时空分辨率的信息。然而，由于云污染等因素导致缺失值，这些数据的广泛应用受到阻碍。在这项研究中，我们使用时空间隙填充框架，基于标准 MODIS LST 产品，从 2003 年到 2020 年生成无缝的全球 1 公里（白天中午和午夜）类似 MODIS 的 LST 数据集。该方法包括两个步骤，1）数据预处理和2）时空拟合。在数据预处理中，我们在同一天的另外三个时间点使用观察到的 LST 过滤了数据质量低的像素并填补了空白。在时空拟合中，首先，我们拟合了每个像素中观测值的长期趋势（总体平均值）（按年份排序）。然后我们在每一天的观测值和总体平均值之间进行时空内插残差。最后，我们通过添加整体均值和插值残差来估计 LST 的缺失值。结果表明，原始MODIS LST中的缺失值得到了有效的填补，没有出现其他无缝LST数据集可能存在的大面积缺失值引起的明显块效应，尤其是在瓦片边界附近。在全球范围内具有不同缺失率的交叉验证表明，间隙填充的 LST 数据具有很高的准确度，平均均方根误差 (RMSE) 分别为 1.88 °C 和 1.33 °C，对于中午 (1: 30 pm）和午夜（凌晨 1:30）。1 公里空间分辨率的无缝全球每日（白天和午夜）LST 数据集在全球城市系统研究、气候研究和建模以及陆地生态系统研究中非常有用。数据可在爱荷华州立大学的 DataShare 上获得，网址为 https://doi.org/10.25380/iastate.c.5078492 (Zhang et al., 2021a)。1 公里空间分辨率的无缝全球每日（白天和午夜）LST 数据集在全球城市系统研究、气候研究和建模以及陆地生态系统研究中非常有用。数据可在爱荷华州立大学的 DataShare 上获得，网址为 https://doi.org/10.25380/iastate.c.5078492 (Zhang et al., 2021a)。1 公里空间分辨率的无缝全球每日（白天和午夜）LST 数据集在全球城市系统研究、气候研究和建模以及陆地生态系统研究中非常有用。数据可在爱荷华州立大学的 DataShare 上获得，网址为 https://doi.org/10.25380/iastate.c.5078492 (Zhang et al., 2021a)。