We develop a new statistically-robust adaptive regression method (SARM) to extract clear sky true color imagery, approximating the near-surface imagery, derived from multiple satellite daily imagery time series, while avoiding artifacts due to clouds and cloud shadows. We compare the SARM-derived near-surface imagery against simpler approaches for various surface types, and perform a quantitative evaluation. Existing mapped daily imagery from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20, and the Ocean and Land Colour Instrument (OLCI) on the Sentinel-3A and Sentinel-3B satellites is used to produce global clear sky near-surface imagery over various time intervals. We provide several examples of satellite-derived clear sky near-surface imagery over various regions to show potential applications. In addition, we apply this new method to derive clear sky near-surface imagery using higher spatial resolution Landsat-8 data, and discuss characteristics and limitations of our approach. The clear sky near-surface imagery is a useful satellite-derived product, representing the human perception of Earth’s near-surface features, which can be more directly interpreted and easily understood by the general public, and aids visualization and interpretation of various types of satellite derived data.

我们开发了一种新的统计稳健的自适应回归方法 (SARM) 来提取晴天真彩色图像，近似近地表图像，从多个卫星日常图像时间序列得出，同时避免由于云和云阴影造成的伪影。我们将 SARM 衍生的近地表图像与各种表面类型的更简单方法进行比较，并进行定量评估。来自 Suomi 国家极地轨道伙伴关系 (SNPP) 和 NOAA-20 上的可见红外成像辐射计套件 (VIIRS) 以及 Sentinel-3A 和 Sentinel-3B 卫星上的海洋和陆地颜色仪器 (OLCI) 的现有映射日常图像用于生成不同时间间隔内的全球晴空近地表图像。我们提供了几个不同区域的卫星衍生晴空近地表图像的示例，以展示潜在的应用。此外，我们应用这种新方法使用更高空间分辨率的 Landsat-8 数据获取清晰的天空近地表图像，并讨论我们方法的特点和局限性。晴空近地表图像是一种有用的卫星衍生产品，代表了人类对地球近地表特征的感知，可以更直接地解释和更容易被公众理解，有助于各类卫星的可视化和解释派生数据。