Abstract The Harmonized Landsat and Sentinel-2 (HLS) project is a NASA initiative aiming to produce a Virtual Constellation (VC) of surface reflectance (SR) data acquired by the Operational Land Imager (OLI) and Multi-Spectral Instrument (MSI) aboard Landsat 8 and Sentinel-2 remote sensing satellites, respectively. The HLS products are based on a set of algorithms to obtain seamless products from both sensors (OLI and MSI): atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, bidirectional reflectance distribution function normalization and spectral bandpass adjustment. Three products are derived from the HLS processing chain: (i) S10: full resolution MSI SR at 10 m, 20 m and 60 m spatial resolutions; (ii) S30: a 30 m MSI Nadir BRDF (Bidirectional Reflectance Distribution Function)-Adjusted Reflectance (NBAR); (iii) L30: a 30 m OLI NBAR. All three products are processed for every Level-1 input products from Landsat 8/OLI (L1T) and Sentinel-2/MSI (L1C). As of version 1.3, the HLS data set covers 10.35 million km2 and spans from first Landsat 8 data (2013); Sentinel-2 data spans from October 2015. The L30 and S30 show a good consistency with coarse spatial resolution products, in particular MODIS Collection 6 MCD09CMG products (overall deviations do not exceed 11%) that are used as a reference for quality assurance. The spatial co-registration of the HLS is improved compared to original Landsat 8 L1T and Sentinel-2A L1C products, for which misregistration issues between multi-temporal data are known. In particular, the resulting computed circular errors at 90% for the HLS product are 6.2 m and 18.8 m, for S10 and L30 products, respectively. The main known issue of the current data set remains the Sentinel-2 cloud mask with many cloud detection omissions. The cross-comparison with MODIS was used to flag products with most evident non-detected clouds. A time series outlier filtering approach is suggested to detect remaining clouds. Finally, several time series are presented to highlight the high potential of the HLS data set for crop monitoring.

中文翻译：

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Harmonized Landsat 和 Sentinel-2 表面反射数据集

摘要 陆地卫星和 Sentinel-2 协调 (HLS) 项目是 NASA 的一项举措，旨在生成由操作陆地成像仪 (OLI) 和机载多光谱仪器 (MSI) 获取的表面反射率 (SR) 数据的虚拟星座 (VC)。 Landsat 8 和 Sentinel-2 遥感卫星，分别。HLS 产品基于一组算法从两个传感器（OLI 和 MSI）获得无缝产品：大气校正、云和云阴影遮蔽、空间协同配准和公共网格、双向反射分布函数归一化和光谱带通调整. HLS 处理链衍生出三种产品： (i) S10：10 m、20 m 和 60 m 空间分辨率下的全分辨率 MSI SR；(ii) S30：a 30 m MSI Nadir BRDF（双向反射分布函数）-调整反射率 (NBAR)；(iii) L30：30 m OLI NBAR。对于来自 Landsat 8/OLI (L1T) 和 Sentinel-2/MSI (L1C) 的每个 Level-1 输入产品，所有三个产品都进行了处理。从 1.3 版开始，HLS 数据集覆盖 1035 万平方公里，跨度来自第一个 Landsat 8 数据（2013 年）；Sentinel-2 数据从 2015 年 10 月开始。 L30 和 S30 与粗空间分辨率产品表现出良好的一致性，特别是 MODIS Collection 6 MCD09CMG 产品（总体偏差不超过 11%），用作质量保证的参考。与原来的 Landsat 8 L1T 和 Sentinel-2A L1C 产品相比，HLS 的空间配准得到了改进，因为多时间数据之间的配准问题是已知的。特别是，对于 S10 和 L30 产品，HLS 产品在 90% 处的计算所得圆误差分别为 6.2 m 和 18.8 m。当前数据集的主要已知问题仍然是 Sentinel-2 云掩码，其中有许多云检测遗漏。与 MODIS 的交叉比较用于标记具有最明显未检测到云的产品。建议使用时间序列异常值过滤方法来检测剩余的云。最后，提出了几个时间序列，以突出 HLS 数据集在作物监测方面的巨大潜力。建议使用时间序列异常值过滤方法来检测剩余的云。最后，提出了几个时间序列，以突出 HLS 数据集在作物监测方面的巨大潜力。建议使用时间序列异常值过滤方法来检测剩余的云。最后，提出了几个时间序列，以突出 HLS 数据集在作物监测方面的巨大潜力。