As the spatial and temporal resolution of remotely sensed imagery has improved over the last four decades, algorithms for monitoring and mapping seasonal changes in surface properties have evolved rapidly. Most recently, the availability of daily PlanetScope imagery has created new opportunities for monitoring the land surface phenology (LSP) of terrestrial ecosystems at high spatial resolution. However, the quality and value of LSP information from PlanetScope imagery have not been systematically examined. In this paper, we evaluate the character and quality of LSP information derived from PlanetScope by comparing time series of vegetation indices and LSP metrics from PlanetScope to corresponding time series and LSP metrics derived from Harmonized Landsat 8 and Sentinel-2 (HLS) imagery and PhenoCams at six sites that span a diverse range of land cover types and climate. Results show that vegetation index time series from all three data sources show high temporal correlation, and LSP metrics derived from HLS, PlanetScope, and PhenoCam show high agreement with negligible bias. Semi-variograms for phenometrics estimated from PlanetScope imagery indicate that the majority of spatial variance captured in PlanetScope phenometrics occurs well below the spatial resolution HLS imagery. At the same time, LSP metrics from HLS are most strongly correlated with the 50–75% quantiles of 3 m LSP metrics from PlanetScope. This indicates that HLS captures the average phenology at sub-pixel scale captured in PlanetScope imagery. Our results represent the first comprehensive comparison of LSP metrics estimated from PlanetScope and publicly available moderate spatial resolution imagery, and provide insights regarding: (1) the quality and character of LSP metrics derived from HLS and PlanetScope; and (2) the relative merits and trade-offs associated with the use of each data source for LSP studies.

随着遥感影像的空间和时间分辨率在过去四年中不断提高，用于监测和绘制地表特性季节性变化的算法也在迅速发展。最近，每日 PlanetScope 图像的可用性为以高空间分辨率监测陆地生态系统的地表物候 (LSP) 创造了新的机会。然而，尚未系统地检查来自 PlanetScope 图像的 LSP 信息的质量和价值。在本文中，我们通过将来自 PlanetScope 的植被指数和 LSP 指标的时间序列与来自 Harmonized Landsat 8 和 Sentinel-2 (HLS) 图像以及六个站点的 PhenoCams 的相应时间序列和 LSP 指标进行比较来评估来自 PlanetScope 的 LSP 信息的特征和质量跨越各种不同的土地覆盖类型和气候。结果表明，来自所有三个数据源的植被指数时间序列显示出高度的时间相关性，来自 HLS、PlanetScope 和 PhenoCam 的 LSP 指标显示出与可忽略偏差的高度一致性。从 PlanetScope 图像估计的表象半变异函数表明，PlanetScope 表象测量中捕获的大部分空间方差远低于空间分辨率 HLS 图像。同时，来自 HLS 的 LSP 指标与来自 PlanetScope 的 3 m LSP 指标的 50-75% 分位数相关性最强。这表明 HLS 捕获了 PlanetScope 图像中捕获的亚像素尺度的平均物候。我们的结果首次对从 PlanetScope 估计的 LSP 指标与公开可用的中等空间分辨率图像进行了全面比较，并提供了以下方面的见解：(1) 从 HLS 和 PlanetScope 得出的 LSP 指标的质量和特征；(2) 与使用每个数据源进行 LSP 研究相关的相对优点和权衡。我们的结果首次对从 PlanetScope 估计的 LSP 指标与公开可用的中等空间分辨率图像进行了全面比较，并提供了以下方面的见解：(1) 从 HLS 和 PlanetScope 得出的 LSP 指标的质量和特征；(2) 与使用每个数据源进行 LSP 研究相关的相对优点和权衡。我们的结果首次对从 PlanetScope 估计的 LSP 指标与公开可用的中等空间分辨率图像进行了全面比较，并提供了以下方面的见解：(1) 从 HLS 和 PlanetScope 得出的 LSP 指标的质量和特征；(2) 与使用每个数据源进行 LSP 研究相关的相对优点和权衡。