Abstract Dense time series of Landsat 8 and Sentinel-2 imagery are creating exciting new opportunities to monitor, map, and characterize temporal dynamics in land surface properties with unprecedented spatial detail and quality. By combining imagery from the Landsat 8 Operational Land Imager and the MultiSpectral Instrument on-board Sentinel-2A and -2B, the remote sensing community now has access to moderate (10–30 m) spatial resolution imagery with repeat periods of ~3 days in the mid-latitudes. At the same time, the large combined data volume from Landsat 8 and Sentinel-2 introduce substantial new challenges for users. Land surface phenology (LSP) algorithms, which estimate the timing of phenophase transitions and quantify the nature and magnitude of seasonality in remotely sensed land surface conditions, provide an intuitive way to reduce data volumes and redundancy, while also furnishing data sets that are useful for a wide range of applications including monitoring ecosystem response to climate variability and extreme events, ecosystem modelling, crop-type discrimination, and land cover, land use, and land cover change mapping, among others. To support the need for operational LSP data sets, here we describe a continental-scale land surface phenology algorithm and data product based on harmonized Landsat 8 and Sentinel-2 (HLS) imagery. The algorithm creates high quality times series of vegetation indices from HLS imagery, which are then used to estimate the timing of vegetation phenophase transitions at 30 m spatial resolution. We present results from assessment efforts evaluating LSP retrievals, and provide examples illustrating the character and quality of information related to land cover and terrestrial ecosystem properties provided by the continental LSP dataset that we have developed. The algorithm is highly successful in ecosystems with strong seasonal variation in leaf area (e.g., deciduous forests). Conversely, results in evergreen systems are less interpretable and conclusive.

中文翻译：

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来自 Landsat 8 和 Sentinel-2 图像的大陆尺度地表物候

摘要 Landsat 8 和 Sentinel-2 影像的密集时间序列正在创造令人兴奋的新机会，以前所未有的空间细节和质量监测、绘制和表征地表特性的时间动态。通过将来自 Landsat 8 Operational Land Imager 的图像与机载 Sentinel-2A 和 -2B 的多光谱仪器相结合，遥感界现在可以获得中等（10-30 m）空间分辨率图像，重复周期约为 3 天中纬度地区。与此同时，来自 Landsat 8 和 Sentinel-2 的大量组合数据给用户带来了大量的新挑战。地表物候 (LSP) 算法，用于估计物候转变的时间并量化遥感地表条件下季节性的性质和幅度，提供一种减少数据量和冗余的直观方法，同时还提供对广泛应用有用的数据集，包括监测生态系统对气候变化和极端事件的响应、生态系统建模、作物类型区分以及土地覆盖、土地利用，以及土地覆盖变化图等。为了支持对可操作 LSP 数据集的需求，我们在这里描述了基于协调的 Landsat 8 和 Sentinel-2 (HLS) 图像的大陆尺度地表物候算法和数据产品。该算法从 HLS 图像中创建高质量的植被指数时间序列，然后用于估计 30 m 空间分辨率下植被物候期转变的时间。我们展示了评估 LSP 检索的评估工作的结果，并提供示例说明我们开发的大陆 LSP 数据集提供的与土地覆盖和陆地生态系统特性相关的信息的特征和质量。该算法在叶面积季节性变化强烈的生态系统中非常成​​功（例如，落叶林）。相反，常绿系统中的结果可解释性和结论性较差。